research paper on students absenteeism

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• Published: 23 April 2018

Reducing student absences at scale by targeting parents’ misbeliefs

• Todd Rogers   ORCID: orcid.org/0000-0001-5904-5439 1 &
• Avi Feller 2  

Nature Human Behaviour volume  2 ,  pages 335–342 ( 2018 ) Cite this article

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• Decision making
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Student attendance is critical to educational success, and is increasingly the focus of educators, researchers and policymakers. We report the results of a randomized experiment examining interventions targeting student absenteeism. Parents of 28,080 high-risk students in grades kindergarten to 12th grade received one of three personalized information treatments repeatedly throughout the school year or received no additional information (control). The most effective versions reduced chronic absenteeism by 10% or more, partly by correcting parents’ biased beliefs about their children’s total accumulated absences. The intervention reduced student absences comparably across grade levels, and reduced absences among untreated cohabiting students in treated households. This intervention is easy to scale and is more than one order of magnitude more cost effective than current absence-reduction best practices. Educational interventions that inform and empower parents, such as the one reported here, can complement more intensive student-focused absenteeism interventions.

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Acknowledgements

We thank the Laura and John Arnold Foundation, Pershing Square Venture Fund for Research on the Foundations of Human Behavior and IES/ICF/REL MidAtlantic number 14JTSK0003 for funding support. We thank J. Lasky-Fink, J. Ternovski and S. Subramanyam for research support. We thank T. Wolford, A. Reitano and W. Hite for district partnership and collaboration. We thank B. Balfanz, G. Basse, M. Bazerman, P. Bergman, H. Chang, L. Coffman, D. Deming, C. Fox, H. Gehlbach, A. Gelber, F. Gino, E. Glaeser, M. Gottfried, D. Green, H. Hoynes, L. John, G. King, D. Laibson, M. Laitin, S. Mullainathan, M. Norton, L. Page, L. Pierce, S. Reardon and J. Schwartzstein for feedback on earlier drafts. No funders had any role in study design, data collection and analysis, decision to publish or preparation of the manuscript.

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T.R. and A.F. designed the experiment, oversaw data analysis and wrote the manuscript.

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The authors declare that they had no competing financial interests while this project was being conducted. In the light of the results of this and other projects, T.R. and A.F. started an organization to help US schools implement this intervention to reduce student absenteeism. It is called In Class Today and worked with two school districts at the time of initial submission, including the school district in which the experiment reported in this manuscript was conducted—SDP.

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Rogers, T., Feller, A. Reducing student absences at scale by targeting parents’ misbeliefs. Nat Hum Behav 2 , 335–342 (2018). https://doi.org/10.1038/s41562-018-0328-1

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Received : 13 February 2017

Accepted : 26 February 2018

Published : 23 April 2018

Issue Date : May 2018

DOI : https://doi.org/10.1038/s41562-018-0328-1

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Investigating the reasons for students’ attendance in and absenteeism from lecture classes and educational planning to improve the situation

Sepideh mokhtari.

Education Development Office, School of Dentistry, Tehran University of Medical Sciences, Tehran, Iran

Sakineh Nikzad

1 Department of Prosthodontics, School of Dentistry, Tehran University of Medical Sciences, Tehran, Iran

Saeedeh Mokhtari

2 Department of Pediatric Dentistry, School of Dentistry, Tehran University of Medical Sciences, Tehran, Iran

Siamak Sabour

3 Department of Clinical Epidemiology, School of Public Health and Safety, Shahid Beheshti University of Medical Sciences, Tehran, Iran

Sepideh Hosseini

Background:.

This study investigated the reasons for the students’ attendance in and absenteeism from lecture classes from the perspective of professors, students, and educational planning to change the unsatisfactory status quo.

MATERIALS AND METHODS:

The present study was a narrow needs assessment survey which was performed on students ( n = 70) of the Faculty of Dentistry, Tehran University of Medical Sciences, in four stages. In the first stage, the opinions of professors and students about the reasons for absenteeism from the lecture classes were collected. In the second stage, the results of the first stage were discussed by an expert panel to find solutions for the problem. The results of the survey were tabulated, summarized, and discussed. In the third stage, online classes were held as one of the solutions and evaluated in the fourth stage.

The results showed that various factors, such as professor empowerment, evaluation system, audiovisual equipment of the classes, educational curriculum, and class schedules, are associated with the students’ attendance in the classes. Along with these factors, one of the most important reasons for students’ absenteeism from classes in recent years might be the generational differences of students. The evaluation of online classes showed that the ratio of the number of students who actively participated in the online classes to the number of students participating in the online classes varied from 30% to 64% ( P < 0.05).

CONCLUSION:

In addition to improving the factors associating students’ attendance in classes, online education is a proper solution for reducing absenteeism in lecture classes and increasing students’ active participation from the perspective of professors and students.

Introduction

Academic performance is one of the most critical issues of students in higher education. Since learning requires attendance and active participation in classes, attendance in classes is thought to be an essential factor in students’ academic performance.[ 1 , 2 , 3 ] Previously, it was believed that students with a high attendance rate were more successful at the end of their course.[ 4 ] Students’ absenteeism from the classes significantly reduces academic achievement, which in turn disrupts the expected learning goals.[ 2 ] Class attendance and learning have received much attention, and there is a well-established positive relationship between class attendance and academic grades.[ 5 ] According to researchers, class attendance is a predictor of student success and reflects a student's positive learning habits, skills, and attitudes, all of which are directly related to their ultimate success.[ 6 ] Absenteeism is an essential issue in the medical and health sciences despite the strictness of attendance policies, affecting students’ performance around the world. Students who attend classes regularly receive useful information and use medical skills more professionally than others throughout their lives.[ 7 ] For example, nursing students’ absenteeism from classes adversely affects their performance and prolongs their duration of the study.[ 8 ] Absenteeism also prevents them from accessing relevant information and contact with relevant materials (clinical skills, lectures, and practical sessions) necessary for active learning.[ 9 ] Medical physiology education also states that classroom lectures should be considered an essential component.[ 10 ]

Although there is a high rate of absenteeism from classes, the students’ presence in the classes is significant to educational institutes because providing resources for this type of education is costly and challenging.[ 3 ] On the other hand, with the emergence of new educational technologies and new online learning methods, the level of interest and the presence of students in classes have decreased even more. Today, the world is affected by the widespread availability of the Internet, which paves the way for a revolution in education. Conventional classes have been replaced by smart classes with the latest technology.[ 11 ] The children of this generation are not confined to traditional textbooks and have more opportunities to access online education.

In recent years, in the Faculty of Dentistry, Tehran University of Medical Sciences, the low attendance rate of students in some lectures has attracted the attention of education planners in this faculty. This nonattendance has invoked protests by some faculty members in recent years. Therefore, to solve this problem, this study examined the root causes of the problem to provide plans to solve the problem.

Materials and Methods

The present study was a narrow needs assessment survey which was performed on students ( n = 70) of the Faculty of Dentistry, Tehran University of Medical Sciences, in four stages. We had a preset list of questions to be answered by a predetermined sample of the professors ( n = 24) and students ( n = 70) to answer these questions chosen in advance. In the first stage, the opinions of professors and students about the reasons for absenteeism from the lecture classes were collected. In the second stage, the results of the first stage were discussed by an expert panel to find solutions for the problem. The results of the survey were tabulated, summarized, and discussed. In the third stage, online classes were held as one of the solutions and evaluated in the fourth stage

The research steps were designed as follows:

• Step 1: Investigation of the factors associating with the attendance and absenteeism of students from classroom lectures
• Step 2: Provision of solutions to increase students’ attendance in classes
• Step 3: Implementation of the proposed solutions based on the set implementation priorities
• Step 4: Evaluation.

After the study protocol was approved by the Faculty Ethics Committee, the study was instituted.

Step 1: Evaluation of the factors affecting the attendance and absenteeism of students from classes from the perspective of professors and students

At this stage, the students’ opinions were collected both qualitatively and quantitatively by the “focus group” method, and the data were collected through a regional standard questionnaire. In this way, since face-to-face sessions with students and discussing open-end questions might help better identify the factors that associate with students’ attendance in classes, a focus group was formed, consisting of student representatives (approximately 10 from each academic year). Then, two faculty members on behalf of the Vice-Chancellor for Education interviewed these students and collected their opinions and views. In the next step, to quantify the students’ opinions, a valid and reliable questionnaire (Cronbach's α = 0.86) was submitted to all the clinical students. The questionnaire was designed in two parts: the study of factors associating with the presence and absence, each of which was based on 12 questions. The questions were scored on a five-point Likert scale and explained to the students before completing the questionnaire.

The professors’ views were also qualitatively examined by the “focus group” method. The young professors were only a few years older than the students, belonging almost to the same generation. Therefore, it was expected that the opinions of young professors would be different from those of experienced professors. As a result, two professors from each department of the faculty (including 12 departments), as young professors and experienced professors, were selected. Then, the opinions of these two groups of professors on the subject were examined in two separate sessions in the presence of the Vice-Chancellor for Education. In each of these sessions, 12 professors, project managers, and statistical consultants were present. This stage was carried out to analyze the reasons for students’ absence from the classes so that the results would be a basis for educational programming.

Step 2: A meeting of experts and provision of solutions to increase student attendance in classes for theoretical lessons

A meeting was held in the Educational Deputy Office with the project managers’ presence to determine proper strategies and plans. After reviewing the results of the students’ and professors’ opinions and summarizing the issues raised in the Educational Council Meeting, the project managers presented their strategies to increase the students’ attendance.

Step 3: Implementation of solutions based on executive priorities

Finally, one of the solutions was adopted by the Vice-Chancellor for Education of the Faculty of Dentistry and implemented.

Step 4: Evaluation

In this stage, to evaluate the proposed solution after its implementation, a meeting was held with the project managers and professors to collect the professors’ opinions. Besides, a reliable and valid regional standard questionnaire was designed to collect students’ opinions ( n = 70). With the cooperation of the University Development Office and the use of the e-poll system, a survey of students was conducted through the web. To get the results, data were analyzed using McNemar's test.

In the first stage, which included the evaluation of factors affecting attendance, 85 questionnaires were completed in the group with 70 students (71% response rate). Tables ​ Tables1 1 and ​ and2 2 present the results of the survey of clinical dental students of Tehran University of Medical Sciences on the reasons for attending and not attending the classes for theoretical courses, respectively.

Prioritizing the factors affecting the absence of lecture classes from the students’ perspectives

Prioritizing the factors affecting the attendance of lecture classes from the students’ perspectives

Factors which were responsible for attendance or absenteeism of students from the classes basis of the young and experienced professors 's evaluation showed in Table 3 .

Factors responsible for attendance or absenteeism of students from the classes basis of the young and experienced professors ‘s evaluation

In addition, the following were some of the highlights of the differences between the views of young and experienced professors:

• Both groups of young and experienced professors emphasized the development of virtual education
• In both groups, some professors believed in mandatory attendance, while others considered mandatory attendance useless, disturbing the classroom's peace
• Young professors laid greater emphasis on the practical and clinical nature of the material presented as an essential factor in attracting students, compared to experienced professors
• Young professors emphasized the rotational nature of the teaching curriculum of professors as an essential factor in attracting students and increasing the ability of professors
• Young professors believed that the exciting topics and chapters of the course that attract students are always in the experienced professors’ teaching agenda, and teaching entirely theoretical and unattractive topics is usually the responsibility of young professors
• Young professors emphasized presenting new educational methods, such as PBL, to increase students’ active learning
• Young professors pointed to the critical role of university policies in this regard and mentioned the gap in incentive policies for active professors in the education development compared to the incentive policies for research activities.

In stage 2, the project managers summarized the strategies for increasing student attendance in the following six areas after evaluating the students’ and professors’ points of view:

• Empowerment of professor
• Paying attention to the characteristics of the new generation (the need to benefit from new technologies and developments and promotion of virtual education)
• Improving the evaluation system
• Improving audiovisual equipment in classrooms
• Improving educational curricula
• Improving class schedules.

The project managers reviewed the six areas mentioned above, and the following points were raised about these areas:

• Professor empowerment requires policy-making and fundamental and long-term planning. It should be noted that although the faculty members might have higher capabilities for educating the learners compared to that in the past due to scientific developments, the mean abilities of current professors have not increased significantly over time
• The use of new technologies in the educational field has not improved significantly by considering the significant changes in the characteristics of the current generation compared to the students of previous decades
• The evaluation system performs better than that previously; however, fundamental changes and reforms are necessary
• The audio and visual equipment of the classes is undoubtedly more better and more numerous compared to previous years
• Concerning educational planning, the curriculum has improved in many cases. However, the timing and presentation of some topics are undesirable, necessitating a review of the new dental curriculum by the Ministry of Health and Medical Education, which is beyond the jurisdiction of the faculty
• In some cases, the class schedule poses problems for students, with highly crowded classes on some days owing to a lack of time.

During the meeting, the project managers analyzed the points mentioned above and realized that although essential factors, such as the empowerment of professors, evaluation system, audiovisual equipment of the classes, educational curriculum, and class schedules, still need to be revised, they have improved to a great deal during the past decade. Therefore, they cannot be considered as the main reasons for students’ decreased desire to attend theoretical classes in recent years. Therefore, one of the most critical factors in reducing the presence of students in recent years could be a change in students’ generational preferences and ideals. In other words, today's students are more familiar with digital technology than ever before and benefit from them. The development of education is not possible without considering the developments in the present age, mainly in the field of information technology. E-learning is expanding globally, and many of the world's leading universities are taking advantage of it. The use of new technologies is not limited to virtual education, and virtual education, despite having many benefits, exhibits a lower rate of interaction between the professors and students than in conventional classes. However, this interaction forms the basis of learning in some educational topics. Therefore, virtual education could be used in cases where the simultaneous interaction of students and professors is needed at a low rate.

On the other hand, online classes, by taking advantage of virtual education, make it possible for professors and students to interact simultaneously on the web. Therefore, although it is not an in-person educational system (physical presence), it requires a kind of presence in the new world field, a presence that will become more acknowledged over time. It seems that online classes, like computers, would soon expand significantly. Therefore, it was suggested that online experimental educational classes should be held.

Finally, the project managers prioritized their planning and implementation to solve the problem of three issues, consisting of improving the capabilities of professors in using virtual education and digital technologies, paying more attention to virtual education and improving its quality, and holding online classes which is one of the new educational technologies with many benefits of in-person and non-in-person education.

The Vice-Chancellor for Education of the Faculty placed the online experimental educational classes on its agenda to develop new educational technologies in the faculty. The design and planning of the educational classes were carried out online; after coordination and education, the professors and students held online classes for at least 1 h for each of the four theoretical lessons. Online classes were selected so that students from four different academic years participated in the study.

Project managers’ evaluation of the online class attendance

Due to mandatory attendance, many students attend university classes reluctantly. Therefore, they only have a physical presence in the classroom, and in many cases, they interfere with the learning process of other students by disturbing the peace of the class. Therefore, the effective presence of students and their active participation in classes is necessary and vital. Since holding online classes for the first time was experienced by students and participating in it required some software measures for students, there were fears that many students would not be interested if they are not forced to attend the classes. Therefore, attending these classes, like conventional classes, was considered mandatory, and in the evaluation, their active presence in the classes was measured. The classes were held beyond the working hours of the faculty by coordination between the instructors and students.

Due to the mandatory attendance in conventional and online classes, the official attendance of students in both classes was almost the same. As mentioned, students’ active participation and level of activity are vital for the learning process. Therefore, the project managers considered the participation of students in online classes as an indicator of their active presence in such classes. The students’ answers to the questions posed by the professors in class and the students’ scientific questions were considered the students’ active participation. The classes were recorded to estimate the ratio of students with active participation to the total number of students present, which was estimated at 30%–64%, depending on the teaching method used, the number of questions and answers, and students’ engagement in scientific discussions. It should be noted that many students had more than one scientific activity and active participation in class, which was not calculated in the students’ participation percentage.

eProfessors’ opinions on the impact of online classes on student attendance

After holding the online classes, a meeting was held with the project managers and instructors involved to collect the comments and suggestions of the professors. After expressing their desire to hold these classes again, the professors evaluated the active participation of students in the classes as desirable and mentioned the role of online classes in increasing students’ active participation. The professors mentioned positive aspects of this project, including the possibility of roll call (which means physical presence and not necessarily active participation) in online classes like conventional classes, the possibility of re-using the classes by students since they were allowed to record the class, the impossibility of disturbing the class peace by students who are reluctant to benefit from the class, resulting in more active participation of interested students, and creating a useful environment for students with lower self-esteem who were not active in conventional classes.

Students’ feedback assessment about attending online classes

To collect the opinions of the students, a reliable and valid questionnaire was designed, and with the cooperation of the University Development Office and using the e-poll system, the students completed it through the web. Table 4 presents the results of this questionnaire. The results showed that the majority of the students were satisfied with attending online classes and the learning process in these classes. The students were eager to continue taking part in such classes, and the vast majority (80%) were reluctant to attend conventional classes with roll calls. The majority of the students (about 90%) considered recording the classroom content an essential advantage for online classes.

Student survey results about online classes based on the questionnaire

In another survey conducted as a focus group of 30 students participating in online classes, the students were asked if their active participation in online classes was more effective compared to conventional classes. This survey results showed that 73.4% of students believed that active participation and attention to educational content in online classes were better than those of conventional classes. Some students believed that the comfort of online classes, the lack of noise from other students, and the focus on the computer screen and the professor's lecture were the most important factors. However, 16.6% believed that their attention was better in conventional classes, and 10% considered conventional and online classes the same from this perspective. Most of the students’ criticisms of online classes were related to unconventional hours, stating that they were interested in attending classes during the regular hours, if possible. Students also found attending online classes easier than attending conventional classes due to the lack of commuting.

After reviewing the evaluations (reviewing by the project executives of the professors’ and students’ opinions), the active participation of students in online classes was deemed as effective, and according to the surveys, the active participation of students and their desire to attend these classes were higher compared to conventional classes.

Students’ absenteeism is a significant concern for higher and academic education around the world. One of the most important reasons for a decrease in students’ attendance classes in recent years might be their generational differences choices. As a limitation of our study, we did not try out the survey on a test group. A test group could let us know if our instructions are clear and if our questions make sense. Therefore, we did not revise the survey on the basis of our test group feedback.

Various studies have suggested many reasons for this. Magobolo and Dube[ 9 ] considered the reasons for the absence of nursing students as illness and not receiving payment for working in their study. Desalegn et al .[ 12 ] reported that the main reasons in the questionnaire completed by students for missing classes were preparing for an examination, an unfavorable class schedule, a lack of interest in the subject, a lack of interest in the teaching style, and ease of understanding the subject without guidance. They believed that not only the behavior of the students but also the characteristics of the teachers and the teaching methods to be effective in the absenteeism of the students from the lectures. In the present study too, the teaching method was considered as the main reason for missing classes due to generational preferences and was further reviewed. Abdelrahman and Abdelkader[ 8 ] showed that nursing students too attributed the main reasons for their absenteeism to educational factors, including a lack of staff in the clinical field and a lack of understanding of the lecture's content. The present study considered another factor to be more critical by considering the empowerment of the professors. A study by Bati et al .[ 13 ] on dental, medical, pharmaceutical, and nursing students showed that the factors that prevent students from attending class lectures are mainly individual (insomnia, lack of health, and the like) and the inefficiency of lecturing in a crowded hall. It is essential to improve the coaching and mentoring system by considering individual and external factors that have a critical impact on students’ attendance. In the present study, the educational aspect was the main reason for absenteeism, and factors such as fatigue and poor classroom conditions were the other less important reasons. Rawlani et al .[ 14 ] stated that the main reason for not attending lectures is the lack of motivation of students to learn. They said that new teaching styles need to be looked into. As in our study, a new way of teaching online was proposed as a solution.

So far, various solutions have been suggested to solve the problem of students’ absenteeism from class lectures. Sharmin et al .[ 1 ] showed that the use of strict roll call policies might affect students’ attendance, and medical schools should reinforce this policy to improve their students’ academic performance. However, according to the present study, it is important to note that this policy does not lead to active student attendance and probably does not improve their academic performance. Al-Shammari[ 15 ] showed that using management techniques and class attendance rules (such as assigning a portion of the total score, extra points to attend classes, and more assignments, or deducing grades for not attending or attending classes with delay) significantly increased the attendance of higher education students and on-time arrival at the class. These improvements were significantly correlated with students’ academic achievement. Thekedam and Kottaram[ 16 ] too reported that, to eradicate the problem of absenteeism, efforts must be made to address all factors in broader social, economic, and political environments, rather than focusing merely on students or faculties. They cited early interventions and preventative measures, positive reinforcement, and rewards for students who improved their attendance as practical factors in reducing chronic absenteeism and advised establishing programs for staff development, workshops, conferences, and symposiums to improve the professors’ performance by the faculty management. Professors who have tried interactive and innovative lecturing methods, by giving better and more engaging lectures, could change students’ attitudes and provide an environment that can reduce student absenteeism.

In the present study, the most important reason for the absence of dental students was the generational characteristics and subsequent changes in students’ learning passion and preferences. In the definition of generations, individuals born in 1995 or later are referred to as Generation Z.[ 17 ] These individuals are indeed our current students. Over the years, this generation has been given various names, such as Generation Z, Internet Generation, and iGeneration, because they are mainly characterized by computer addiction as well as addiction to any other type of technology. What sets this generation apart from previous generations is that they are the “most electronic generation” in history and have grown up with technology. They are growing with the Internet, cell phones, laptops, iPods, tablets, and other electronic devices that have become part of their daily lives.[ 18 ] Generation Z prefers nontraditional teaching methods and likes to use logic-based and practical learning approaches.[ 17 ] Instead of taking notes, Generation Z students rely on computer records, are more inclined to ask questions online, and do not like to wait for answers. Instead, they prefer immediate information and communication. Generation Z students do now fill our classrooms and expect an educational environment in which they can interact in the same way they do in their virtual world. This means the demand for immediate information, visual forms of learning, and the replacement of “communication” with “interaction.”[ 19 ] Active learning classes, such as flipped classrooms or problem-based learning methods, are more popular with this younger generation.[ 20 ]

In this study, holding online classes was proposed and implemented to solve the problem of student absenteeism. The results showed that the ratio of the students with active participation in the online class to the total number of participating students varied from 30% to 64%. Besides, the results of a survey of students’ opinions showed that the majority of the students were satisfied with attending online classes and the quality of learning in these classes. Similar results have been achieved in other studies on dental students.[ 21 , 22 , 23 , 24 , 25 ] Changiz et al .[ 26 ] observed that students showed good readiness in all components of e-learning. Hence, the instructional designer can trust the e-learning strategies and build the course based on them. Dalmolin et al .[ 17 ] showed that in addition to the positive attitude of dental students toward e-learning, the use of websites as a supportive tool for learning was significantly different between different age groups. Younger students believed that websites were a better tool to help them learn compared to older students.

Rensburg carried out a systematic review of the data from 36 articles on online classes and reported results consistent with the present study. It can be concluded from the similar results of these two studies that online teaching and learning has positive results, such as increasing student satisfaction and motivation, improving problem-solving skills, increasing flexibility for learning, and increasing student participation for undergraduate health sciences educators and students. Rensburg[ 27 ] reported that unstable Internet connectivity, inadequate Internet access, technological problems, and concerns about useful and fast feedback to students as challenges to online teaching and learning. Ochs[ 28 ] also showed that the online classes were more efficient in some teaching areas compared to classroom instruction; therefore, determining the teaching topics in online class planning is one of the most critical topics in organizing and designing these classes. Kwok et al .[ 29 ] and Tse and Ellman[ 30 ] reported that a combination of online education with conventional class-based teaching might play an essential role in improving students’ scientific knowledge and increasing their skills in clinical areas. A review by Tang et al .[ 31 ] showed that the integration of online lectures in undergraduate medical education is more acceptable by students and leads to improved knowledge and clinical skills. The results of the present study are consistent with many studies focusing on medical education.[ 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 ]

Fadol et al .[ 40 ] showed that both online and flipped classes were held better than the conventional classes, and flipped classes were held better than the online classes. Furthermore, students who had access to online content missed fewer classes and performed better. All these studies are consistent with the current study. However, there are studies with different results, such as that by Fish and Snodgrass,[ 41 ] which advocated conventional education (face to face) of students. The reasons for this preference were reported to be motivation and discipline in conventional teaching and concerns about learning in online courses. The study suggested that taking a course in online classes and preparing for them could help students gain a realistic understanding of online classes and produce a positive impact. A meta-analysis in 2015 also found that students performed better in conventional classes. It considered that online classes were not affordable for institutions, and reported that the possibility for students to leave online courses and changes in existing technologies were the weak points of online classes.[ 42 ] Some of the problems in the present study were a lack of sufficient funding due to the impossibility of holding classes during the regular hours, a lack of sufficient experience of some professors in holding classes (which was solved with the help of the support system), and a lack of access to laptops for all the students (some students shared their laptops with classmates).

Finally, e-learning makes it possible for students to tailor the educational content to their individual learning styles with visual media, charts, digital content, interactive videos, or web-based interactions. This is facilitated by the use of mobile devices that provide easy access. Learning online could be an excellent option to help university professors teach future dentists. Teachers need to acknowledge that by introducing e-learning courses, they can encourage students to use online tools to educate and communicate with their professors and peers. Typically, teaching in dentistry relies more on visual techniques; therefore, students are more interested in visual transmission than text transmission.[ 17 ]

Undoubtedly, with the rapid advances in educational technologies and virtual teaching methods around the world, and with generational preferences of students, soon, the physical space of most universities will become centers merely for program coordination for educational courses. Theoretical classes will be held only with new and online methods. With the advances in online classroom software, a complete simulation of conventional classes will be possible virtually so that each individual will sit in a specific chair in the virtual classroom and will be trained. Clearly, at that time, having the skills to use these technologies and using new methods of virtual learning for professors will be an essential measure of excellence and success.

The results showed that various factors, such as the empowerment of professors, evaluation system, audiovisual equipment of the classes, educational curriculum, and class schedules, affect the attendance of students in the classroom. However, significant progress has been made in many of these factors over the past decade. Therefore, along with these factors, one of the most important reasons for the decrease in the attendance of students in recent years could be related to the change of generation and preferences of students. Since the new generation is more inclined to use educational technologies, in the present study, online classes were used as a solution to increase the active participation of students in the classrooms. The results showed that online classes are suitable from the perspective of professors and students and could significantly increase the participation of students in class lectures.

Financial support and sponsorship

Conflicts of interest.

There are no conflicts of interest.

Acknowledgment

The authors would like to acknowledge Dr. MJ Kharazi Fard and all professors and students in Dental School of Tehran University of Medical Sciences for their assistance in this study.

Report | Children

Student absenteeism : Who misses school and how missing school matters for performance

Report • By Emma García and Elaine Weiss • September 25, 2018

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A broader understanding of the importance of student behaviors and school climate as drivers of academic performance and the wider acceptance that schools have a role in nurturing the “whole child” have increased attention to indicators that go beyond traditional metrics focused on proficiency in math and reading. The 2015 passage of the Every Student Succeeds Act (ESSA), which requires states to report a nontraditional measure of student progress, has codified this understanding.

The vast majority of U.S. states have chosen to comply with ESSA by using measures associated with student absenteeism—and particularly, chronic absenteeism. This report uses data on student absenteeism to answer several questions: How much school are students missing? Which groups of students are most likely to miss school? Have these patterns changed over time? And how much does missing school affect performance?

Data from the National Assessment of Educational Progress (NAEP) in 2015 show that about one in five students missed three days of school or more in the month before they took the NAEP mathematics assessment. Students who were diagnosed with a disability, students who were eligible for free lunch, Hispanic English language learners, and Native American students were the most likely to have missed school, while Asian students were rarely absent. On average, data show children in 2015 missing fewer days than children in 2003.

Our analysis also confirms prior research that missing school hurts academic performance: Among eighth-graders, those who missed school three or more days in the month before being tested scored between 0.3 and 0.6 standard deviations lower (depending on the number of days missed) on the 2015 NAEP mathematics test than those who did not miss any school days.

Introduction and key findings

Education research has long suggested that broader indicators of student behavior, student engagement, school climate, and student well-being are associated with academic performance, educational attainment, and with the risk of dropping out. 1

One such indicator—which has recently been getting a lot of attention in the wake of the passage of the Every Student Succeeds Act (ESSA) in 2015—is student absenteeism. Absenteeism—including chronic absenteeism—is emerging as states’ most popular metric to meet ESSA’s requirement to report a “nontraditional” 2 measure of student progress (a metric of “school quality or student success”). 3

Surprisingly, even though it is widely understood that absenteeism has a substantial impact on performance—and even though absenteeism has become a highly popular metric under ESSA—there is little guidance for how schools, districts, and states should use data about absenteeism. Few empirical sources allow researchers to describe the incidence, trends over time, and other characteristics of absenteeism that would be helpful to policymakers and educators. In particular, there is a lack of available evidence that allows researchers to examine absenteeism at an aggregate national level, or that offers a comparison across states and over time. And although most states were already gathering aggregate information on attendance (i.e., average attendance rate at the school or district level) prior to ESSA, few were looking closely into student-level attendance metrics, such as the number of days each student misses or if a student is chronically absent, and how they mattered. These limitations reduce policymakers’ ability to design interventions that might improve students’ performance on nontraditional indicators, and in turn, boost the positive influence of those indicators (or reduce their negative influence) on educational progress.

In this report, we aim to fill some of the gaps in the analysis of data surrounding absenteeism. We first summarize existing evidence on who misses school and how absenteeism matters for performance. We then analyze the National Assessment of Educational Progress (NAEP) data from 2003 (the first assessment with information available for every state) and 2015 (the most recent available microdata). As part of the NAEP assessment, fourth- and eighth-graders were asked about their attendance during the month prior to taking the NAEP mathematics test. (The NAEP assessment may be administered anytime between the last week of January and the end of the first week of March, so “last month” could mean any one-month period between the first week of January and the first week of March.) Students could report that they missed no days, 1–2 days, 3–4 days, 5–10 days, or more than 10 days.

We use this information to describe how much school children are missing, on average; which groups of children miss school most often; and whether there have been any changes in these patterns between 2003 and 2015. We provide national-level estimates of the influence of missing school on performance for all students, as well as for specific groups of students (broken out by gender, race/ethnicity and language status, poverty/income status, and disability status), to detect whether absenteeism is more problematic for any of these groups. We also present evidence that higher levels of absenteeism are associated with lower levels of student performance. We focus on the characteristics and outcomes of students who missed three days of school or more in the previous month (the aggregate of those missing 3–4, 5–10, and more than 10 school days), which is our proxy for chronic absenteeism. 4 We also discuss data associated with children who had perfect attendance the previous month and those who missed more than 10 days of school (our proxy for extreme chronic absenteeism).

Given that the majority of states (36 states and the District of Columbia) are using “chronic absenteeism” as a metric in their ESSA accountability plans, understanding the drivers and characteristics of absenteeism and, thus, the policy and practice implications, is more important than ever (Education Week 2017). Indeed, if absenteeism is to become a useful additional indicator of learning and help guide effective policy interventions, it is necessary to determine who experiences higher rates of absenteeism; why students miss school days; and how absenteeism affects student performance (after controlling for factors associated with absenteeism that also influence performance).

Major findings include:

One in five eighth-graders was chronically absent. Typically, in 2015, about one in five eighth-graders (19.2 percent) missed school three days or more in the month before the NAEP assessment and would be at risk of being chronically absent if that pattern were sustained over the school year.

• About 13 percent missed 3–4 days of school in 2015; about 5 percent missed 5–10 days of school (between a quarter and a half of the month); and a small minority, less than 2 percent, missed more than 10 days of school, or half or more of the school days that month.
• We find no significant differences in rates of absenteeism and chronic absenteeism by grade (similar shares of fourth-graders and eighth-graders were absent), and the patterns were relatively stable between 2003 and 2015.
• While, on average, there was no significant change in absenteeism levels between 2003 and 2015, there was a significant decrease over this period in the share of students missing more than 10 days of school.

Absenteeism varied substantially among the groups we analyzed. In our analysis, we look at absenteeism by gender, race/ethnicity and language status, FRPL (free or reduced-price lunch) eligibility (our proxy for poverty status), 5 and IEP (individualized education program) status (our proxy for disability status). 6 Some groups had much higher shares of students missing school than others.

• Twenty-six percent of IEP students missed three school days or more, compared with 18.3 percent of non-IEP students.
• Looking at poverty-status groups, 23.2 percent of students eligible for free lunch, and 17.9 percent of students eligible for reduced-price lunch, missed three school days or more, compared with 15.4 percent of students who were not FRPL-eligible (that is, eligible for neither free lunch nor reduced-price lunch).
• Among students missing more than 10 days of school, the share of free-lunch-eligible students was more than twice as large as the share of non-FRPL-eligible students (2.3 percent vs. 1.1 percent). Similarly, the share of IEP students in this category was more than double the share of non-IEP students (3.2 percent vs. 1.5 percent).
• Perfect attendance rates were slightly higher among black and Hispanic non-ELL students than among white students, although all groups lagged substantially behind Asian students in this indicator.
• Hispanic ELL students and Asian ELL students were the most likely to have missed more than 10 school days, at 3.9 percent and 3.2 percent, respectively. These shares are significantly higher than the overall average rate of 1.7 percent and than the shares for their non-ELL counterparts (Hispanic non-ELL students, 1.6 percent; Asian non-ELL students, 0.6 percent).

Absenteeism varied by state. Some states had much higher absenteeism rates than others. Patterns within states remained fairly consistent over time.

• In 2015, California and Massachusetts were the states with the highest full-attendance rates: 51.1 and 51.0 percent, respectively, of their students did not miss any school days; they are closely followed by Virginia (48.4 percent) and Illinois and Indiana (48.3 percent).
• At the other end of the spectrum, Utah and Wyoming had the largest shares of students missing more than 10 days of school in the month prior to the 2015 assessment (4.6 and 3.5 percent, respectively).
• Five states and Washington, D.C., stood out for their high shares of students missing three or more days of school in 2015: in Utah, nearly two-thirds of students (63.5 percent) missed three or more days; in Alaska, nearly half (49.6 percent) did; and in the District of Columbia, Wyoming, New Mexico, and Montana, nearly three in 10 students were in this absenteeism category.
• In most states, overall absenteeism rates changed little between 2003 and 2015.

Prior research linking chronic absenteeism with lowered academic performance is confirmed by our results. As expected, and as states have long understood, missing school is negatively associated with academic performance (after controlling for factors including race, poverty status, gender, IEP status, and ELL status). As students miss school more frequently, their performance worsens.

• Overall performance gaps. The gaps in math scores between students who did not miss any school and those who missed three or more days of school varied from 0.3 standard deviations (for students who missed 3–4 days of school the month prior to when the assessment was taken) to close to two-thirds of a standard deviation (for those who missed more than 10 days of school). The gap between students who did not miss any school and those who missed just 1–2 days of school was 0.10 standard deviations, a statistically significant but relatively small difference in practice.
• For Hispanic non-ELL students, missing more than 10 days of school harmed their performance on the math assessment more strongly than for the average (0.74 standard deviations vs. 0.64 on average).
• For Asian non-ELL students, the penalty for missing school was smaller than the average (except for those missing 5–10 days).
• Missing school hindered performance similarly across the three poverty-status groups (nonpoor, somewhat poor, and poor). However, given that there are substantial differences in the frequency with which children miss school by poverty status (that is, poor students are more likely to be chronically absent than nonpoor students), absenteeism may in fact further widen income-based achievement gaps.

What do we already know about why children miss school and which children miss school? What do we add to this evidence?

Poor health, parents’ nonstandard work schedules, low socioeconomic status (SES), changes in adult household composition (e.g., adults moving into or out of the household), residential mobility, and extensive family responsibilities (e.g., children looking after siblings)—along with inadequate supports for students within the educational system (e.g., lack of adequate transportation, unsafe conditions, lack of medical services, harsh disciplinary measures, etc.)—are all associated with a greater likelihood of being absent, and particularly with being chronically absent (Ready 2010; U.S. Department of Education 2016). 8 Low-income students and families disproportionately face these challenges, and some of these challenges may be particularly acute in disadvantaged areas 9 ; residence in a disadvantaged area may therefore amplify or reinforce the distinct negative effects of absenteeism on educational outcomes for low-income students.

A detailed 2016 report by the U.S. Department of Education showed that students with disabilities were more likely to be chronically absent than students without disabilities; Native American and Pacific Islander students were more likely to be chronically absent than students of other races and ethnicities; and non-ELL students were more likely to be chronically absent than ELL students. 10 It also showed that students in high school were more likely to miss school than students in other grades, and that about 500 school districts reported that 30 percent or more of their students missed at least three weeks of school in 2013–2014 (U.S. Department of Education 2016).

Our analysis complements this evidence by adding several dimensions to the breakdown of who misses school—including absenteeism rates by poverty status and state—and by analyzing how missing school harms performance. We distinguish by the number of school days students report having missed in the month prior to the assessment (using five categories, from no days missed to more than 10 days missed over the month), 11 and we compare absenteeism rates across grades and across cohorts (between 2003 and 2015), as available in the NAEP data. 12

How much school are children missing? Are they missing more days than the previous generation?

In 2015, almost one in five, or 19.2 percent of, eighth-grade students missed three or more days of school in the month before they participated in NAEP testing. 13 About 13 percent missed 3–4 days, roughly 5 percent missed 5–10 days, and a small share—less than 2 percent—missed more than 10 days, or half or more of the instructional days that month ( Figure A , bottom panel). 14

How much school are children missing? : Share of eighth-grade students by attendance/absenteeism category, in the eighth-grade mathematics NAEP sample, 2003 and 2015

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Source: EPI analysis of National Assessment of Educational Progress microdata, 2003 and 2015

On average, however, students in 2015 did not miss any more days than students in the earlier period; by some measures, they missed less school than children in 2003 (Figure A, top panel). While the share of students with occasional absences (1–2 days) increased moderately between 2003 and 2015, the share of students who missed more than three days of school declined by roughly 3 percentage points between 2003 and 2015. This reduction was distributed about evenly (in absolute terms) across the shares of students missing 3–4, 5–10, and more than 10 days of school. But in relative terms, the reduction was much more significant in the share of students missing more than 10 days of school (the share decreased by nearly one-third). We find no significant differences by grade ( Appendix Figure A ) or by subject. Thus, we have chosen to focus our analyses below on the sample of eighth-graders taking the math assessment only.

Which groups miss school most often? Which groups suffer the most from chronic absenteeism?

Absenteeism by race/ethnicity and language status.

Hispanic ELLs and the group made up of Native Americans plus “all other races” (not white, black, Hispanic, or Asian) are the racial/ethnic and language status groups that missed school most frequently in 2015. Only 39.6 percent (Native American or other) and 41.2 percent (Hispanic ELL) did not miss any school in the month prior to the assessment (vs. 44.4 percent overall, 43.2 percent for white students, 43.5 percent for black students, and 44.1 percent for Hispanic non-ELL students; see Figure B1 ). 15

Which groups of students had the highest shares missing no school? : Share of eighth-graders with perfect attendance in the month prior to the 2015 NAEP mathematics assessment, by group

Notes: Students are grouped by gender, race/ethnicity and ELL status, FRPL eligibility, and IEP status. ELL stands for English language learner; IEP stands for individualized education program (learning plan designed for each student who is identified as having a disability); and FRPL stands for free or reduced-price lunch (federally funded meal programs for students of families meeting certain income guidelines).

Source: EPI analysis of National Assessment of Educational Progress microdata, 2015

Asian students (both non-ELL and ELL) are the least likely among all racial/ethnic student groups to be absent from school at all. Two-thirds of Asian non-ELL students and almost as many (61.6 percent of) Asian ELL students did not miss any school. Among Asian non-ELL students, only 8.8 percent missed three or more days of school: 6.1 percent missed 3–4 days (12.7 percent on average), 2.1 percent missed 5–10 days (relative to 4.8 percent for the overall average), and only 0.6 percent missed more than 10 days of school (relative to 1.7 percent for the overall average). Among Asian ELL students, the share who missed three or more days of school was 13.3 percent.

As seen in Figure B2 , the differences in absenteeism rates between white students and Hispanic non-ELL students were relatively small, when looking at the shares of students missing three or more days of school (18.3 percent and 19.1 percent, respectively). The gaps are somewhat larger for black, Native American, and Hispanic ELL students relative to white students (with shares missing three or more days at 23.0, 24.0, and 24.1 percent, respectively, relative to 18.3 percent for white students).

Which groups of students had the highest shares missing three or more days? : Share of eighth-graders missing three or more days of school in the month prior to the 2015 NAEP mathematics assessment, by group

Notes: This chart represents the aggregate of data for students who missed 3–4 days, 5–10 days, and more than 10 days of school. Students are grouped by gender, race/ethnicity and ELL status, FRPL eligibility, and IEP status. ELL stands for English language learner; IEP stands for individualized education program (learning plan designed for each student who is identified as having a disability); and FRPL stands for free or reduced-price lunch (federally funded meal programs for students of families meeting certain income guidelines).

Among students who missed a lot of school (more than 10 days), there were some more substantial differences by race and language status. About 3.9 percent of Hispanic ELL students and 3.2 percent of Asian ELL students missed more than 10 days of school, compared with 2.2 percent for Native American and other races, 2.0 percent for black students, 1.4 percent for white students, and only 0.6 percent for Asian non-ELL students (all relative to the overall average of 1.7 percent) (see Figure B3 ).

Which groups of students had the highest shares missing more than 10 days? : Share of eighth-graders missing more than 10 days of school in the month prior to the 2015 NAEP mathematics assessment, by group

Notes:  Students are grouped by gender, race/ethnicity and ELL status, FRPL status, and IEP status. ELL stands for English language learner; IEP stands for individualized education program (learning plan designed for each student who is identified as having a disability); and FRPL stands for free or reduced-price lunch (federally funded meal programs for students of families meeting certain income guidelines).

Absenteeism by income status

The attendance gaps are even larger by income status than they are by race/ethnicity and language status (Figures B1–B3). Poor (free-lunch-eligible) students were 5.9 percentage points more likely to miss some school than nonpoor (non-FRPL-eligible) students, and they were 7.8 percentage points more likely to miss school three or more days (23.2 vs. 15.4 percent). 16 Among somewhat poor (reduced-price-lunch-eligible) students, 17.9 percent missed three or more days of school. The lowest-income (free-lunch-eligible) students were 4.1 percentage points more likely to miss school 3–4 days than non-FRPL-eligible students, and more than 2.4 percentage points more likely to miss school 5–10 days ( Appendix Figure B ). Finally, and most striking, free-lunch-eligible students—the most economically disadvantaged students—were more than twice as likely to be absent from school for more than 10 days as nonpoor students. In other words, they were much more likely to experience extreme chronic absenteeism. Figures B1–B3 show that the social-class gradient for the prevalence of absenteeism, proxied by eligibility for free or reduced-price lunch, is noticeable in all absenteeism categories, and especially when it comes to those students who missed the most school.

Absenteeism by disability status

Students with IEPs were by far the most likely to miss school relative to all other groups. 17 The share of IEP students missing school exceeded the share of non-IEP students missing school by 7.7 percentage points (Figure B1). More than one in four IEP students had missed school three days or more in the previous month (Figure B2). About 15.5 percent of students with IEPs missed school 3–4 days (vs. 12.4 percent among non-IEP students); 7.3 percent missed 5–10 days; and 3.2 percent missed more than 10 days of school in the month before being tested (Appendix Figure B; Figure B3).

Absenteeism by gender

The differences by gender are slightly surprising (Figures B1–B3). Boys showed a higher full-attendance rate than girls (46.6 vs. 42.1 percent did not miss any school), and boys were no more likely than girls to display extreme chronic absenteeism (1.7 percent of boys and 1.6 percent of girls missed more than 10 days of school). Boys (18.2 percent) were also slightly less likely than girls (20.2 percent) to be chronically absent (to miss three or more days of school, as per our definition).

Has there been any change over time in which groups of children are most often absent from school?

For students in several groups, absenteeism fell between 2003 and 2015 ( Figure C1 ), in keeping with the overall decline noted above. Hispanic students (both ELL and non-ELL), Asian non-ELL students, Native American and other race students, free-lunch-eligible (poor) students, reduced-priced-lunch-eligible (somewhat poor) students, non-FRPL-eligible (nonpoor) students, and IEP students were all less likely to miss school in 2015 than they were over a decade earlier. For non-IEP and white students, however, the share of students who did not miss any school days in the month prior to NAEP testing remained essentially unchanged, while it increased slightly for black students and Asian ELL students (by about 2 percentage points each).

How much have perfect attendance rates changed since 2003? : Percentage-point change in the share of eighth-graders who had perfect attendance in the month prior to the NAEP mathematics assessment, between 2003 and 2015, by group

Notes: Students are grouped by gender, race/ethnicity and ELL status, FRPL status, and IEP status. ELL stands for English language learner; IEP stands for individualized education program (learning plan designed for each student who is identified as having a disability); and FRPL stands for free or reduced-price lunch (federally funded meal programs for students of families meeting certain income guidelines).

As seen in Figure C2 , we also note across-the-board reductions in the shares of students who missed three or more days of school (with the exception of the share of Asian ELL students, which increased by 1.7 percentage points over the time studied). The largest reductions occurred for students with disabilities (IEP students), Hispanic non-ELL students, Native American students or students of other races, free-lunch-eligible students, and non-FRPL-eligible students (each of these groups experienced a reduction of at least 4.4 percentage points). 18 For all groups except Asian ELL students, the share of students missing more than 10 days of school ( Figure C3 ) also decreased (for Asian ELL students, it increased by 1.3 percentage points).

How much have rates of students missing three or more days changed since 2003? : Percentage-point change in the share of eighth-graders who were absent from school three or more days in the month prior to the NAEP mathematics assessment, between 2003 and 2015, by group

Notes: This chart represents the aggregate of data for students who missed 3–4 days, 5–10 days, and more than 10 days of school. Students are grouped by gender, race/ethnicity and ELL status, FRPL status, and IEP status. ELL stands for English language learner; IEP stands for individualized education program (learning plan designed for each student who is identified as having a disability); and FRPL stands for free or reduced-price lunch (federally funded meal programs for students of families meeting certain income guidelines).

How much have rates of students missing more than 10 days changed since 2003? : Percentage-point change in the share of eighth-graders who were absent from school more than 10 days in the month prior to the NAEP mathematics assessment, between 2003 and 2015, by group

Notes: Students are grouped by gender, race/ethnicity and ELL status, FRPL status, and IEP status. ELL stands for English language learner; IEP stands for individualized education program (learning plan designed for each student who is identified as having a disability); and FRPL stands for free or reduced-price lunch (federally funded meal programs for students of families meeting certain income guidelines).

In order to get a full understanding of these comparisons, we need to look at both the absolute and relative differences. Overall, the data presented show modest absolute differences in the shares of students who are absent (at any level) in various groups when compared with the averages for all students (Figures B1–B3 and Appendix Figure B). The differences (both absolute and relative) among student groups missing a small amount of school (1–2 days) are minimal for most groups. However, while the differences among groups are very small in absolute terms for students missing a lot of school (more than 10 days), some of the differences are very large in relative terms. (And, taking into account the censoring problem mentioned earlier, they could potentially be even larger.)

The fact that the absolute differences are small is in marked contrast to differences seen in many other education indicators of outcomes and inputs, which tend to be much larger by race and income divisions (Carnoy and García 2017; García and Weiss 2017). Nevertheless, both the absolute and relative differences we find are revealing and important, and they add to the set of opportunity gaps that harm students’ performance.

Is absenteeism particularly high in certain states?

Share of students absent from school, by state and by number of days missed, 2015.

Notes: Based on the number of days eighth-graders in each state reported having missed in the month prior to the NAEP mathematics assessment. “Three or more days” represents the aggregate of data for students who missed 3–4 days, 5–10 days, and more than 10 days of school.

Over the 2003–2015 period, 22 states saw their share of students with perfect attendance grow. The number drops to 15 if we count only states in which the share of students not missing any school increased by more than 1 percentage point. In almost every state (44 states), the share of students who missed more than 10 school days decreased, and in 41 states, the share of students who missed three or more days of school also dropped, though it increased in the other 10. 19 Louisiana, Massachusetts, Nevada, Indiana, New Hampshire, and California were the states in which these shares decreased the most, by more than 6 percentage points, while Utah, Alaska, and North Dakota were the states where this indicator (three or more days missed) showed the worst trajectory over time (that is, the largest increases in chronic absenteeism).

Is absenteeism a problem for student performance?

Previous research has focused mainly on two groups of students when estimating how much absenteeism influences performance: students who are chronically absent and all other students. This prior research has concluded that students who are chronically absent are at serious risk of falling behind in school, having lower grades and test scores, having behavioral issues, and, ultimately, dropping out (U.S. Department of Education 2016; see summary in Gottfried and Ehrlich 2018). Our analysis allows for a closer examination of the relationship between absenteeism and performance, as we look at the impact of absenteeism on student performance at five levels of absenteeism. This design allows us to test not only whether different levels of absenteeism have different impacts on performance (as measured by NAEP test scores), but also to identify the point at which the impact of absenteeism on performance becomes a concern. Specifically, we look at the relationship between student absenteeism and mathematics performance among eighth-graders at various numbers of school days missed. 20

The results shown in Figure D and Appendix Table 1 are obtained from regressions that assess the influence of absenteeism and other individual- and school-level determinants of performance. The latter include students’ race/ethnicity, gender, poverty status, ELL status, and IEP status, as well as the racial/ethnic composition of the school they attend and the share of students in their school who are eligible for FRPL (a proxy for the SES composition of the school). Our results thus identify the distinct association between absenteeism and performance, net of other factors that are known to influence performance. 21

In general, the more frequently children missed school, the worse their performance. Relative to students who didn’t miss any school, those who missed some school (1–2 school days) accrued, on average, an educationally small, though statistically significant, disadvantage of about 0.10 standard deviations (SD) in math scores (Figure D and Appendix Table 1, first row). Students who missed more school experienced much larger declines in performance. Those who missed 3–4 days or 5–10 days scored, respectively, 0.29 and 0.39 standard deviations below students who missed no school. As expected, the harm to performance was much greater for students who were absent half or more of the month. Students who missed more than 10 days of school scored nearly two-thirds (0.64) of a standard deviation below students who did not miss any school. All of the gaps are statistically significant, and together they identify a structural source of academic disadvantage.

The more frequently students miss school, the worse their performance : Performance disadvantage experienced by eighth-graders on the 2015 NAEP mathematics assessment, by number of school days missed in the month prior to the assessment, relative to students with perfect attendance in the prior month (standard deviations)

Notes: Estimates are obtained after controlling for race/ethnicity, poverty status, gender, IEP status, and ELL status; for the racial/ethnic composition of the student’s school; and for the share of students in the school who are eligible for FRPL (a proxy for school socioeconomic composition). All estimates are statistically significant at p < 0.01.

The results show that missing school has a negative effect on performance regardless of how many days are missed, with a moderate dent in performance for those missing 1–2 days and a troubling decline in performance for students who missed three or more days that becomes steeper as the number of missed days rises to 10 and beyond. The point at which the impact of absenteeism on performance becomes a concern, therefore, is when students miss any amount of school (vs. having perfect attendance); the level of concern grows as the number of missed days increases.

Gaps in performance associated with absenteeism are similar across all races/ethnicities, between boys and girls, between FRPL-eligible and noneligible students, and between students with and without IEPs. For example, relative to nonpoor (non-FRPL-eligible) students who did not miss any school, nonpoor children who missed school accrued a disadvantage of -0.09 SD (1–2 school days missed), -0.27 SD (3–4 school days missed), -0.36 SD (5–10 school days missed), and -0.63 SD (more than 10 days missed). For students eligible for reduced-price lunch (somewhat poor students) who missed school, compared with students eligible for reduced-price lunch who did not miss any school, the gaps are -0.16 SD (1–2 school days missed), -0.33 SD (3–4 school days missed), -0.45 SD (5–10 school days missed), and -0.76 SD (more than 10 days missed). For free-lunch-eligible (poor) students who missed school, relative to poor students who do not miss any school, the gaps are -0.11 SD (1–2 school days missed), -0.29 SD (3–4 school days missed), -0.39 SD (5–10 school days missed), and -0.63 SD (more than 10 days missed). By IEP status, relative to non-IEP students who did not miss any school, non-IEP students who missed school accrued a disadvantage of -0.11 SD (1–2 school days missed), -0.30 SD (3–4 school days missed), -0.40 SD (5–10 school days missed), and -0.66 SD (more than 10 days missed). And relative to IEP students who did not miss any school, IEP students who missed school accrued a disadvantage of -0.05 SD (1–2 school days missed), -0.21 SD (3–4 school days missed), -0.31 SD (5–10 school days missed), and -0.52 SD (more than 10 days missed). (For gaps by gender and by race/ethnicity, see Appendix Table 1).

Importantly, though the gradients of the influence of absenteeism on performance by race, poverty status, gender, and IEP status (Appendix Table 1) are generally similar to the gradients in the overall relationship between absenteeism and performance for all students, this does not mean that all groups of students are similarly disadvantaged when it comes to the full influence of absenteeism on performance. The overall performance disadvantage faced by any given group is influenced by multiple factors, including the size of the group’s gaps at each level of absenteeism (Appendix Table 1), the group’s rates of absenteeism (Figure B), and the relative performance of the group with respect to the other groups (Carnoy and García 2017). The total gap that results from adding these factors can thus become substantial.

To illustrate this, we look at Hispanic ELL, Asian non-ELL, Asian ELL, and FRPL-eligible students. The additional penalty associated with higher levels of absenteeism is smaller than average for Hispanic ELL students experiencing extreme chronic absenteeism; however, their performance is the lowest among all groups (Carnoy and García 2017) and they have among the highest absenteeism rates.

The absenteeism penalty is also smaller than average for Asian non-ELL students (except at 5-10 days); however, in contrast with the previous example, their performance is the highest among all groups (Carnoy and García 2017) and their absenteeism rate is the lowest.

The absenteeism penalty for Asian ELL students is larger than average, and the gradient is steeper. 22 Asian ELL students also have lower performance than most other groups (Carnoy and García 2017).

Finally, although there is essentially no difference in the absenteeism–performance relationship by FRPL eligibility, the higher rates of absenteeism (at every level) for students eligible for free or reduced-price lunch, relative to nonpoor (FRPL-ineligible) students, put low-income students at a greater risk of diminished performance due to absenteeism than their higher-income peers, widening the performance gap between these two groups.

Conclusions

Student absenteeism is a puzzle composed of multiple pieces that has a significant influence on education outcomes, including graduation and the probability of dropping out. The factors that contribute to it are complex and multifaceted, and likely vary from one school setting, district, and state to another. This analysis aims to shed additional light on some key features of absenteeism, including which students tend to miss school, how those profiles have changed over time, and how much missing school matters for performance.

Our results indicate that absenteeism rates were high and persistent over the period examined (2003–2015), although they did decrease modestly for most groups and in most states. Unlike findings for other factors that drive achievement gaps—from preschool attendance to economic and racial school segregation to unequal funding (Carnoy and García 2017; García 2015; García and Weiss 2017)—our findings here seem to show some positive news for black and Hispanic students: these students had slightly higher perfect attendance rates than their white peers; in addition, their perfect attendance rates have increased over time at least as much as rates for white students. But with respect to the absenteeism rates that matter the most (three or more days of school missed, and more than 10 days of school missed), black and Hispanic students still did worse (just as is the case with other opportunity gaps faced by these students). Particularly worrisome is the high share of Hispanic ELL students who missed more than 10 school days—nearly 4 percent. Combined with the share of Hispanic ELL students who missed 5–10 school days (nearly 6 percent), this suggests that one in 10 children in this group would miss school for at least a quarter of the instructional time.

The advantages that Asian students enjoy relative to white students and other racial/ethnic groups in academic settings is also confirmed here (especially among Asian non-ELL students): the Asian students in the sample missed the least school. And there is a substantial difference in rates of absenteeism by poverty (FRPL) and disability (IEP) status, with the difference growing as the number of school days missed increases. Students who were eligible for free lunch were twice as likely as nonpoor (FRPL-ineligible) students to be absent more than 10 days, and students with IEPs were more likely than any other group to be absent (one or more days, that is, to not have perfect attendance).

Missing school has a distinct negative influence on performance, even after the potential mediating influence of other factors is taken into account, and this is true at all rates of absenteeism. The bottom line is that the more days of school a student misses, the poorer his or her performance will be, irrespective of gender, race, ethnicity, disability, or poverty status.

These findings help establish the basis for an expanded analysis of absenteeism along two main, and related, lines of inquiry. One, given the marked and persistent patterns of school absenteeism, it is important to continue to explore and document why children miss school—to identify the full set of factors inside and outside of schools that influence absenteeism. Knowing whether (or to what degree) those absences are attributable to family circumstances, health, school-related factors, weather, or other factors, is critical to effectively designing and implementing policies and practices to reduce absenteeism, especially among students who chronically miss school. The second line of research could look at variations in the prevalence and influence of absenteeism among the states, and any changes over time in absenteeism rates within each state, to assess whether state differences in policy are reducing absenteeism and mitigating its negative impacts. For example, in recent years, Connecticut has made reducing absenteeism, especially chronic absenteeism, a top education policy priority, and has developed a set of strategies and resources that could be relevant to other states as well, especially as they begin to assess and respond to absenteeism as part of their ESSA plans. 23

The analyses in this report confirm the importance of looking closely into “other” education data, above and beyond performance (test scores) and individual and school demographic characteristics. The move in education policy toward widening accountability indicators to indicators of school quality, such as absenteeism, is important and useful, and could be expanded to include other similar data. Indicators of bullying, school safety, student tardiness, truancy, level of parental involvement, and other factors that are relevant to school climate, well-being, and student performance would also merit attention.

Acknowledgements

The authors gratefully acknowledge John Schmitt and Richard Rothstein for their insightful comments and advice on earlier drafts of the paper. We are also grateful to Krista Faries for editing this report, to Lora Engdahl for her help structuring it, and to Julia Wolfe for her work preparing the tables and figures included in the appendix. Finally, we appreciate the assistance of communications staff at the Economic Policy Institute who helped to disseminate the study, especially Dan Crawford and Kayla Blado.

About the authors

Emma García  is an education economist at the Economic Policy Institute, where she specializes in the economics of education and education policy. Her areas of research include analysis of the production of education, returns to education, program evaluation, international comparative education, human development, and cost-effectiveness and cost-benefit analysis in education. Prior to joining EPI, García was a researcher at the Center for Benefit-Cost Studies of Education, the National Center for the Study of Privatization in Education, and the Community College Research Center at Teachers College, Columbia University, and did consulting work for the National Institute for Early Education Research, MDRC, and the Inter-American Development Bank. García has a Ph.D. in economics and education from Teachers College, Columbia University.

Elaine Weiss  served as the national coordinator for the Broader, Bolder Approach to Education (BBA) from 2011 to 2017, in which capacity she worked with four co-chairs, a high-level task force, and multiple coalition partners to promote a comprehensive, evidence-based set of policies to allow all children to thrive. She is currently working on a book drawing on her BBA case studies, co-authored with Paul Reville, to be published by the Harvard Education Press. Weiss came to BBA from the Pew Charitable Trusts, where she served as project manager for Pew’s Partnership for America’s Economic Success campaign. Weiss was previously a member of the Centers for Disease Control and Prevention’s task force on child abuse and served as volunteer counsel for clients at the Washington Legal Clinic for the Homeless. She holds a Ph.D. in public policy from the George Washington University and a J.D. from Harvard Law School.

Appendix figures and tables

Are there significant differences in student absenteeism rates across grades and over time : shares of fourth-graders and eighth-graders who missed school no days, 1–2 days, 3–4 days, 5–10 days, and more than 10 days in the month before the naep mathematics assessment, 2003 and 2015, detailed absenteeism rates by group : shares of eighth-graders in each group who missed school no days, 1–2 days, 3–4 days, 5–10 days, and more than 10 days in the month before the naep mathematics assessment, 2015, the influence of absenteeism on eighth-graders' math achievement : performance disadvantage experienced by eighth-graders on the 2015 naep mathematics assessment, by group and by number of days missed in the month prior to the assessment, relative to students in the same group with perfect attendance in the prior month (standard deviations).

*** p < 0.01; ** p < 0.05; * p < 0.1

Notes: Students are grouped by gender, race/ethnicity and ELL status, FRPL eligibility, and IEP status. ELL stands for English language learner; IEP stands for individualized education program (learning plan designed for each student who is identified as having a disability); and FRPL stands for free or reduced-price lunch (federally funded meal programs for students of families meeting certain income guidelines). Estimates for the “All students” sample are obtained after controlling for race/ethnicity, poverty status, gender, IEP status, and ELL status; for the racial/ethnic composition of the student’s school; and for the share of students in the school who are eligible for FRPL (a proxy for school socioeconomic composition). For each group, controls that are not used to identify the group are included (for example, for black students, estimates control for poverty status, gender, IEP status, and ELL status; for the racial/ethnic composition of the student’s school; and for the share of students in the school who are eligible for FRPL; etc.)

1. See García 2014 and García and Weiss 2016.

2. See ESSA 2015. According to ESSA, this nontraditional indicator should measure “school quality or student success.” (The other indicators at elementary/middle school include measures of academic achievement, e.g., performance or proficiency in reading/language arts and math; academic progress, or student growth; and progress in achieving English language proficiency.)

3. Thirty-six states and the District of Columbia have included student absenteeism as an accountability metric in their states’ ESSA plans. This metric meets all the requirements (as outlined in ESSA) to be considered a measure of school quality or student success (valid, reliable, calculated the same for all schools and school districts across the state, can be disaggregated by student subpopulation, is a proven indicator of school quality, and is a proven indicator of student success; see Education Week 2017). See FutureEd 2017 for differences among the states’ ESSA plans. See the web page “ ESSA Consolidated State Plans ” (on the Department of Education website) for the most up-to-date information on the status and content of the state plans.

4. There is no precise official definition that identifies how many missed days constitutes chronic absenteeism on a monthly basis. Definitions of chronic absenteeism are typically based on the number of days missed over an entire school year, and even these definitions vary. For the Department of Education, chronically absent students are those who “miss at least 15 days of school in a year” (U.S. Department of Education 2016). Elsewhere, chronic absenteeism is frequently defined as missing 10 percent or more of the total number of days the student is enrolled in school, or a month or more of school, in the previous year (Ehrlich et al. 2013; Balfanz and Byrnes 2012). Given that the school year can range in length from 180 to 220 days, and given that there are about 20–22 instructional days in a month of school, these latter two definitions imply that a student is chronically absent if he or she misses between 18 and 22 days per year (depending on the length of the school year) or more, or between 2.0 and about 2.5 days (or more) per month on average (assuming a nine-month school year). In our analysis, we define students as being chronically absent if they have missed three or more days of school in the last month (the aggregate of students missing “3–4,” “5–10,” or “more than 10 days”), and as experiencing extreme chronic absenteeism if they have missed “more than 10 days” of school in the last month. These categories are not directly comparable to categories used in studies of absenteeism on a per-year basis or that use alternative definitions or thresholds. We purposely analyze data for each of these “days absent” groups separately to identify their distinct characteristics and the influence of those differences on performance. (Appendix Figure B and Appendix Table 1 provide separate results for each of the absenteeism categories.)

5.  In our analysis, we define “poor” students as those who are eligible for free lunch; we define “somewhat poor” students as those who are eligible for reduced-price lunch; and we define “nonpoor” students as those who are not eligible for free or reduced-price lunch. We use “poverty status,” “income status,” “socioeconomic status” (“SES”), and “social class” interchangeably throughout our analysis. We use the free or reduced-price lunch status classification as a metric for individual poverty, and we use the proportion of students who are eligible for FRPL as a metric for school poverty (in our regression controls; see Figure D). The limitations of these variables to measure economic status are discussed in depth in Michelmore and Dynarski’s (2016) study. FRPL statuses are nevertheless valid and widely used proxies of low(er) SES, and students’ test scores are likely to reflect such disadvantage (Carnoy and García 2017).

6. Under the Individuals with Disabilities Education Act (IDEA), an IEP must be designed for each student with a disability. The IEP “guides the delivery of special education supports and services for the student” (U.S. Department of Education 2000). For more information about IDEA, see U.S. Department of Education n.d.

7. Students are grouped by gender, race/ethnicity and ELL status, FRPL eligibility, and IEP status.

8. The U.S. Department of Education (2016) defines “chronically absent” as “missing at least 15 days of school in a year.” Ready (2010) explains the difference between legitimate or illegitimate absences, which may respond to different circumstances and behaviors. Ready’s findings, pertaining to children at the beginning of school, indicate that, relative to high-SES students, low-SES children with good attendance rates experienced greater gains in literacy skills during kindergarten and first grade, narrowing the starting gaps with their high-SES peers. No differences in math skills gains were detected in kindergarten.

9. U.S. Department of Education 2016. This report uses data from the Department of Education’s Civil Rights Data Collection 2013–2014.

10. The analysis finds no differences in absenteeism by gender. It is notable that the Department of Education report finds that ELL students have lower absenteeism rates than their non-ELL peers, given that we find (as described later in the report) that Asian ELL students have higher absenteeism rates than Asian non-ELL students and that Hispanic ELL students have higher absenteeism rates than Hispanic non-ELL students. It is important to note, however, that the data the Department of Education analyze compared all ELL students to all non-ELL students (not only Asian and Hispanic students separated out by ELL status), and thus our estimates are not directly comparable.

11. Children in the fourth and eighth grades were asked, “How many days were you absent from school in the last month?” The possible answers are: none, 1–2 days, 3–4 days, 5–10 days, and more than 10 days. An important caveat concerning this indicator and results based on its utilization is that there is a potential inherent censoring problem: Children who are more likely to miss school are also likely to miss the assessment. In addition, some students may be inclined to underreport the number of days that they missed school, in an effort to be viewed more favorably (in social science research, this may introduce a source of response-bias referred to as “social desirability bias”). Although we do not have any way to ascertain the extent to which these might be problems in the NAEP data and for this question in particular, it is important to read our results and findings as a potential underestimate of what the rates of missingness are, as well as what their influence on performance is.

12. One reason to look at different grades is to explore the potential connection between early absenteeism and later absenteeism. Ideally, we would be able to include data on absenteeism from earlier grades in students’ academic careers since, as Nai-Lin Chang, Sundius, and Wiener (2017) explain, attendance habits are developed early and often set the stage for attendance patterns later on. These authors argue that detecting absenteeism early on can improve pre-K to K transitions, especially for low-income children, children with special needs, or children who experience other challenges at home; these are the students who most need the social, emotional, and academic supports that schools provide and whose skills are most likely to be negatively influenced by missing school. Gottfried (2014) finds reduced reading and math achievement outcomes, and lower educational and social engagement, among kindergartners who are chronically absent. Even though we do not have information on students’ attendance patterns at the earliest grades, looking at patterns in the fourth and eighth grades can be illuminating.

13. Students are excluded from our analyses if their absenteeism information and/or basic descriptive information (gender, race/ethnicity, poverty status, and IEP) are missing.

14. All categories combined, we note that in 2015, 49.5 percent of fourth-graders and 55.6 percent of eighth-graders missed at least one day of school in the month prior. Just over 30 percent of fourth-graders and 36.4 percent of eighth-graders missed 1–2 days of school during the month.

15. In the sample, 52.1 percent of students are white, 14.9 percent black, 4.5 percent Hispanic ELL, 19.4 percent Hispanic non-ELL, less than 1 percent Asian ELL, 4.7 percent Asian non-ELL, and 3.8 percent Native American or other.

16. Of the students in the sample, 47.8 percent are not eligible for FRPL, 5.2 percent are eligible for reduced-price lunch, and 47.0 percent are eligible for free lunch.

17. In the 2015 eighth-grade mathematics sample, 10.8 percent of students had an IEP.

18. For students who were eligible for reduced-price lunch (somewhat poor students), shares of students absent three or more days also decreased, but more modestly, by 3.3 percentage points.

19. Number of states is out of 51; the District of Columbia is included in the state data.

20. The results discussed below cannot be interpreted as causal, strictly speaking. They are obtained using regression models with controls for the relationship between performance and absenteeism (estimates are net of individual, home, and school factors known to influence performance and are potential sources of selection). However, the literature acknowledges a causal relationship between (high-quality) instructional time and performance, in discussions about the length of the school day (Kidronl and Lindsay 2014; Jin Jez and Wassmer 2013; among others) and the dip in performance children experience after being out of school for the summer (Peterson 2013, among others). These findings could be extrapolable to our absenteeism framework and support a more causal interpretation of the findings of this paper.

21. Observations with full information are used in the regressions. The absenteeism–performance relationship is only somewhat sensitive to including traditional covariates in the regression (not shown in the tables; results available upon request). The influence of absenteeism on performance is distinct and is not due to any mediating effect of the covariates that determine education performance.

22. Asian ELL students who miss more than 10 days of school are very far behind Asian ELL students with perfect attendance, with a gap of more than a standard deviation. This result needs to be interpreted with caution, however, as it is based on a very small fraction of students for whom selection may be a concern, too.

23. The data used in our analysis are for years prior to the implementation of measures intended to tackle absenteeism. See Education Week 2017. Data for future (or more recent) years will be required to analyze whether Connecticut’s policies have had an effect on absenteeism rates in the state.

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Michelmore, K., and S. Dynarski. 2016.  The Gap within the Gap: Using Longitudinal Data to Understand Income Differences in Student Achievement . National Bureau of Economic Research Working Paper no. 22474.

Nai-Lin Chang, Hedy, Jane Sundius, and Louise Wiener. 2017. “ Using ESSA to Tackle Chronic Absence from Pre-K to K–12 ” (blog post). National Institute for Early Education Research website, May 23, 2017.

National Center for Education Statistics (NCES), National Assessment of Educational Progress (NAEP). Various years. NAEP microdata (unpublished data).

Peterson, T.K., ed. 2013. Expanding Minds and Opportunities: Leveraging the Power of Afterschool and Summer Learning for Student Success . Washington, D.C.: Collaborative Communications Group.

Ready, Douglas D. 2010. “Socioeconomic Disadvantage, School Attendance, and Early Cognitive Development: The Differential Effects of School Exposure.” Sociology of Education 83, no. 4: 271–286. https://doi.org/10.1177/0038040710383520 .

U.S. Department of Education. 2000. A Guide to the Individualized Education Program . Office of Special Education and Rehabilitative Services, July 2000.

U.S. Department of Education. 2016. Chronic Absenteeism in the Nation’s Schools: An Unprecedented Look at a Hidden Educational Crisis (online fact sheet).

U.S. Department of Education. n.d. “ About IDEA ” (webpage). IDEA (Individuals with Disabilities Education Act) website . Accessed September 19, 2018.

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Home > Graduate Research and Creative Practice > Culminating Experience Projects > 406

Culminating Experience Projects

Chronic absenteeism: exploring causes, impacts, and interventions in education.

Arlee M. Hoag , Grand Valley State University Follow

Date Approved

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Education-School Counseling (M.Ed.)

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Education Leadership & Counseling

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Judy Williams

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Chronic absenteeism among students presents a significant challenge within the educational landscape, impacting academic performance and contributing to educational disparities. This research delves into the multifaceted issue of chronic absenteeism, aiming to identify its root causes, implications, and potential interventions. The study examines diverse factors influencing student attendance, including socioeconomic barriers, health conditions, transportation challenges, and their subsequent impact on academic achievement. Moreover, it explores the inequities prevalent in absenteeism, aiming to shed light on disparities among student populations.

ScholarWorks Citation

Hoag, Arlee M., "Chronic Absenteeism: Exploring Causes, Impacts, and Interventions in Education" (2023). Culminating Experience Projects . 406. https://scholarworks.gvsu.edu/gradprojects/406

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Combatting Chronic Absenteeism with Family Engagement 

• Posted March 28, 2024
• By Jill Anderson
• Counseling and Mental Health
• Disruption and Crises
• Families and Community

Family engagement plays a pivotal role in combating chronic absenteeism, says Eyal Bergman , Ed.L.D.’21, senior vice president at Learning Heroes.

The number of students who are chronically absent — missing 10% or more of the school year — has skyrocketed since the pandemic. Bergman studied this issue and was surprised to discover how schools with robust family engagement had significantly lower rates of chronic absenteeism. “It shows that the strength of a school's family engagement is actually more predictive of a school's chronic absenteeism than their rates of poverty,” he says.

But fostering strong home-school partnerships has been a challenge for many school districts. “What we find is that schools often, despite really good intentions, have not really been designed to promote really strong partnerships with families,” he says. “This is why families are often treated as spectators to the work of schools. This is why their cultural wisdom and their expertise about their children aren't necessarily woven into the fabric of schooling. It's why we see that schools often apply assimilationist practices.”

Bergman emphasizes the need for trust-building between educators and families, personalized approaches to student learning, and systemic infrastructural support to enhance family engagement. In future work, Bergman will dig deeper into the data and try to gather more information about what certain school districts with strong family engagement did to keep chronic absenteeism down and a possible tool down the line to help schools with family engagement.

In this episode, he explains the soaring numbers of chronic absenteeism while underscoring the transformative potential of prioritizing family engagement in ensuring student well-being and academic success.

JILL ANDERSON: I'm Jill Anderson. This is the Harvard EdCast.

Eyal Bergman believes strong family engagement may counteract schools' growing problem with chronic absenteeism. Nearly 16 million students missed more than 10% of the 2022 school year — that's double pre-pandemic levels.

Bergman leads research on the impact of family engagement in schools. When he set out to study family engagement during the pandemic, he was surprised to see just how much better schools with pre-existing strong family engagement fared on attendance. Many schools struggle to develop strong family engagement, despite its well-known benefits and even funding available to help. I wanted to hear more about the challenges associated with chronic absenteeism and the role of family engagement. First, I asked how the pandemic impacted chronic absenteeism.

EYAL BERGMAN: If you compare chronic absenteeism numbers from pre-pandemic, from 2019, '18-'19, to '21-'22, which is the first full year once kids were fully back in school, it doubled. And we have about 15 million children that were chronically absent in the '21-'22 school year. And they're all over the country. So you have a little over 5 million kids in cities, about 5 million kids in suburbs. You've got about 4 million kids that live in rural or small towns. It's really everywhere. But it's especially important to note that it's especially pronounced in high-poverty communities.

So when you look at high poverty communities pre-pandemic, about 25% of schools were in what's called extreme chronic absenteeism. That's where 30% or more of students are chronically absent.

JILL ANDERSON: Right.

EYAL BERGMAN: So you've got 25% of schools in high-poverty communities that were chronically absent pre-pandemic, and that number shot up to 69%.

JILL ANDERSON: Wow.

EYAL BERGMAN: So nearly 7 in 10 schools in high-poverty communities are beset by extreme levels of chronic absence. It is a tremendous problem. Now, some of those numbers have started to come down, but only slightly. So '21-'22, we're currently two years removed from some of that data. And some states have released their data from this past school year, from the '22-'23 school year, but most of the states that have released data have had their numbers come down less than five percentage points. Some states have actually gone up. So we're really in, still, a very elevated level of chronic absence across the country.

JILL ANDERSON: Do we know a lot about why this is happening post-pandemic, or is it still kind of we need to figure it out?

EYAL BERGMAN: Well, there's a lot of different factors explaining why kids come or don't come to school. I think there's been changing norms.

EYAL BERGMAN: It's important to remember that chronic absence takes into account excused and unexcused absences. So norms around-- it used to be that a kid had sniffles, and they would still come to school. A lot of families have decided just to not send their kids to school at that point.

But I think also, norms have changed in schools around kids wanting to stay home, families choosing to send their kids home. And I think the way it relates to our study, and the work that we look at, and the relationship between home and school, is that it shows that in communities that have strong relationships with families, they can have real talk about how important it is to come to school regularly, what interventions can be placed that are mutually supported at home and at school, such that kids feel more welcomed in school, such that kids feel like school is an everyday experience and families feel that way too. I think it's helpful to see attendance as like a vital sign of a school's health. And the more that students and teachers and parents and all the community members are wrapping themselves around kids to really stress the importance and help them feel welcomed and safe and supported in schools, the more likely kids are to show up to school every day.

JILL ANDERSON: You set out to study this issue. Tell me a little bit about what you discovered-- why some schools were faring better than others?

EYAL BERGMAN: Let me give you a little bit of an explanation about where the data comes from, to give some context, and a little bit about where the study came from. Because Dr. Karen Mapp is a mentor of mine and was my advisor when the pandemic hit. And we were talking a lot about, surely, the pandemic would be a very useful time to study the impact of family engagement.

Because the hypothesis for this study was born in those first few months. We were hearing all sorts of anecdotal evidence in the spring of 2020, when schools were shuttered, that the schools that had strong relationships with families were going to be a little bit better equipped to withstand the disruptions in schooling. So that was the hypothesis that was set out. And Dr. Mapp was able to join on a Learning Heroes webinar. And in that webinar, where we are talking about this, it piqued the interest of some funders that were on the webinar. And so we were able to start to get some initial funding to be able to set out and conduct this study.

And as a part of the initial landscape assessment of preparing to conduct that study, we interviewed about 20 family engagement scholars, experts in the field. And one of the things that we learned from that landscape assessment is that there is really one really predominant data source for a large quantitative sample. And that is the Five Essentials, which is administered statewide in Illinois. It was designed by the University of Chicago.

If folks are familiar with Tony Bryk and Barbara Schneider's landmark study, "Trust In Schools," this found that the presence of trust in schools was highly predictive of whether schools would improve over time. And so this data has been-- is a very high teacher response rate, so it's very reliable and valid. And it measures the five essentials of school improvement, like classroom culture, principal efficacy, teacher efficacy-- a number of key essentials. And one of those is family-community ties.

And so there are over 3,000 schools in Illinois that respond to this survey every year. That doesn't really exist in many other places. And so it's a really important and rich data set. And so it dates back. 

So that is the data that we used. And there is a lot of publicly-available data on achievement levels, and poverty rates, and a bunch of community and school characteristics that allow us to do statistical modeling. And so we're able to control for a lot of variables.

The way I like to think about statistics is that there's a lot of factors that contribute to chronic absenteeism. And what the stats allow us to do is start to tease apart, like, how big are the different pieces of the pie, right? For all intents and purposes, if you take two schools, and you say they had the same achievement levels, the same chronic absenteeism levels in 2018-2019, they have the same poverty rates, they're the same type of schools-- so you're not comparing high school to elementary school-- and so for all intents and purposes, these schools are essentially the same except for that one school scored in the 10th percentile of the Five Essentials family engagement metric, and the other school scored in the 90th percentile. So you're comparing two schools where one school performed very low, and one school performed very high. What this allows us to say is that the family engagement score accounts for a 39% difference in their chronic absenteeism rates. So that's tremendous. That 39% accounts for 6.2 percentage points on their chronic absenteeism score.

So those are real numbers. In a school of 500 kids, 6% of kids is 31 kids that are not chronically absent. In a school of 1,000 kids, that's 62 kids. That's multiple classrooms. That could be a whole grade level. So these are real numbers, and that's the most important finding from the study.

JILL ANDERSON: Were you surprised, though, to see this huge—

EYAL BERGMAN: Yes.

JILL ANDERSON: --number, this huge difference?

EYAL BERGMAN: We had to run the numbers multiple times to make sure that we were seeing the right thing. And we sent it out to external reviewers to make sure that we weren't missing something critical. We're like, this is pretty significant. There's also other findings where it shows that the strength of a school's family engagement is actually more predictive of a school's chronic absenteeism than their rates of poverty. JILL ANDERSON: Wow.

EYAL BERGMAN: We found that the strength of a school's family engagement was twice as predictive post-pandemic as it was pre-pandemic. So that 6.2 number is cut in half in pre-pandemic. In other words, we knew family engagement mattered before, but it really mattered in the pandemic. And considering that our levels are still pretty close, they're much closer to what they were in '21-'22 now than what they were pre-pandemic. So the importance of family engagement, to me, seems like it's here to stay if you're talking about addressing chronic absenteeism.

JILL ANDERSON: So it says that there was 6% less chronic absenteeism post-pandemic in schools with strong family engagement versus weak family engagement. I'm just going to ask you again if you can put that number into some kind of context, because I worry that people might hear that and think, 6% is really such a small number.

EYAL BERGMAN: Yeah.

JILL ANDERSON: It doesn't seem that dramatic, but it is.

EYAL BERGMAN: If you work in a school-- I imagine a lot of your listeners work in schools-- show me a school that doesn't want to lower its chronic absenteeism by 6 percentage points right now. And if you want to talk about real kids, like I said, in a school of 500 kids-- that's a normal-sized elementary school. That's an average-sized school in Illinois-- that's 31 children that are not chronically absent. In a school of 1,000, that's 62.

In terms of attendance, it's 800 fewer student absences in a year in a school of 500. And I'll put it in financial terms, too. In my state-- I live in California-- a lot of states like ours get their school funding based on something called average daily attendance. In other words, the days that students come to school is how schools actually get their funding.

So when I used to work in a school district here in California, our Attendance Office was located in Fiscal Services, just to give you a sense of where we situate the relative importance of attendance. So in a school of 500, that 6 percentage point equates to $45,000. A school of 1,000-- there's lots of schools with 1,000 kids-- that's $90,000 of your operating budget.

JILL ANDERSON: So it's not a case of just the value, of course, of getting kids into the school so that they can learn. It has significant funding repercussions.

EYAL BERGMAN: It has significant funding repercussions. And I also just think that anybody who's been a teacher understands that you can't do all the work that you want to do if your kids aren't in school. There's this saying that I don't necessarily love. It's like, they can't learn if they're not here.

I mean, I think kids learn wherever they are. Like, my kids learn at home. Our kids learn in the community. But the skills we want to impart on kids, the social development and the academic development we want to impart on kids, school offers an important sense of consistency for kids. And if they're not coming to school, then they can't get the benefits of everything we're trying to do in school.

JILL ANDERSON: Would you say that the majority of schools in the US have weak family engagement? EYAL BERGMAN: I'll put it this way. So Dr. Karen Mapp and I wrote a piece that was published in June of 2021. We really started thinking about it and writing it in the summer of 2020 amidst the renewed reckoning of racial injustice in America in the wake of George Floyd's murder and schools grappling with how to respond, how to think about themselves as service providers in communities-- particularly in communities of color-- but also the ravages of the pandemic upon low income and communities of color.

These dual pandemics just reveal a lot about the historical relationships between home and school in America. And what we find is that schools often, despite really good intentions, have not really been designed to promote really strong partnerships with families. This is why families are often treated as spectators to the work of schools. This is why their cultural wisdom and their expertise about their children aren't necessarily woven into the fabric of schooling. It's why we see that schools often apply assimilationist practices.

What do I mean by that? When you look at how most schools and systems allocate their funding for family engagement, it's usually things like parent liaisons. They hire parent liaisons who are from the community-- oftentimes in immigrant communities that speak the language and that are from that culture and that community-- and things like parent universities or training programs for parents. None of that is wrong. In many cases, that comes from requests from parents and from the communities themselves.

So the point there is that that's not wrong, but that it's incomplete. Because as a strategy, what it says is, in order to resolve this gulf between home and school, we need parents to come closer to the school.

EYAL BERGMAN: So we need to have staff explain things to parents so that they understand things and so that they are better equipped to support their kids. But there isn't very much in the way of strategy design or funding to help staff get to understand and know the communities better to build the staffing capacity for stronger and richer community engagement.

I mean, when I say that schools haven't really been designed for comprehensive engagement, I think you can look at most schools, like, teachers will tell you they don't really have the time to build relationships with families and to collaborate really richly with them. That is a product of design. What do we have time to do?

EYAL BERGMAN: It's A question of where our priorities are. When you look at most pre-service training programs in America, teachers do not receive training in their pre-service programs for building relationships with families and collaborating with them so that they can actually work in strong partnership, even though we know from research-- and it just makes intuitive sense, too-- if you're a parent, if you're a teacher, you know that if parents and teachers are actually talking to one another, if they're looking at data, if they're on the same page, then it feels like one of you is playing offense—

Like on a football team, the offensive players don't play defense, and the defensive players don't play offense. But they're both doing their part to help the team win. So you can imagine that the teachers doing their part in the classroom, the parents doing their part at home, if they're in strong partnership with each other, that actually really helps kids.

And this is what the research bears out. But the systems are not designed to promote that type of activity and behavior. That's why teachers don't have the time. That's why teachers aren't trained. That's why there's not much PD in the way of helping teachers improve in this work.

There are tools and tactics like apps that can be helpful, or text message opt-out, opt-in methods where parents get notified. There are important technical things that can be done, and that does help. That helps a little bit. But the fundamental questions of why parents and teachers don't have the time or the dispositions to be able to really build strong relationships, that's at the core of the nature of the challenge: What makes one school strong in family engagement versus another?

I can give examples from two pieces. One is the piece that Dr. Mapp and I wrote. It's called, "Embracing a New Normal, Toward a More Liberatory Approach to Family Engagement." We have a nice case study in there. And then, there's another piece that I wrote a few years ago called, "Unlocking the How, Designing Family Engagement Strategies that Lead to School Success." And that has about a dozen examples from across the country.

And in that piece, I sort of break down three basic pillars of what constitutes strong engagement. And all of this is rooted, of course, in the dual capacity-building framework for family school partnerships. That's Dr. Mapp's framework. And in that framework, which we borrow from the Three Pillars, the first pillar is the indisputable importance of trust-building.

EYAL BERGMAN: A lot of times, what I see is events, activities in schools where we invite parents to come in and do things, or we send out communications to parents. Those are the most common things. But if you're not building trust between teachers and parents, you're not going to see really strong engagement. 

And so schools that really excel, they do things even with the resources at their disposal. Like, make Back to School Night less about all the things that I want to do this year with your kids in my classroom, and more like, let me get to know you a little bit. There are schools where the teachers just walk around and they have parents introduce themselves to each other, to their parents. They open the floor. They just ask questions. 

You just build a relationship with another person. Schooling is fundamentally a human enterprise. So we get to know people for who they are and what they care about. You start by building relationships.

So some schools do home visits. To me, that's really powerful for teachers because it helps them conceptualize, like, what did I think about the kids and families that I've visited, and what did I actually observe in the home? And very often, it can start a conversation about some biases I thought-- I think I might have had about families.

Home visits are wonderful. Even if you don't do home visits, you can do like community visits. And one thing that I loved was just trust visits.

So the parents come to the school, but the point of the visit at the start of the year is just like, let's build a relationship. Let's get to know each other. At this one school that we featured in "Embracing a New Normal," they do welcoming meetings at the beginning of the year. 30 minutes, teachers meet with their students' parents for 30 minutes at the start of the year. That time is set aside by the schools.

In secondary schools, if I'm a math teacher in a high school, I can't meet with 150 parents at the start of the year. But oftentimes, there's advisories, or Zero Period. And so there are ways to chunk it out so that everybody does their part with just a few students. So that's one thing.

The second pillar is, this is all about student learning and well-being. I think a lot of times, what we see is that family engagement activities are more like feel-good. And that's not bad. In many cases, it can be really important for building a relationship.

But at the end of the day, we're all here for kids. And so family engagement is a means to an end. It's not an end in and of itself. The purpose should be, let's make sure we all have an accurate picture of how our children are performing in school.

So the organization that I work at, Learning Heroes, we do, practically every year, a national poll of parents and teachers and principals. The last data we have is from late in 2023, where we did a poll with Gallup. And again, we found that about 90% of parents in America think that their kids are at or above grade level in reading and in math.

Educators have one perspective. Families have a very different perspective.

EYAL BERGMAN: Families, for the most part, think that their kids are doing fine until they hear from the school that there was a problem.

JILL ANDERSON: Uh-huh.

EYAL BERGMAN: Well, also, more than 80% of parents in America report that their kids get A's and B's, or B's and higher, on their schools. So the primary method that families receive to let them know how their children are performing are report card grades. And report card grades have their benefit, but they're also deeply flawed. They're more of a reflection of effort and assignment completion than they are grade-level mastery-- or they can be-- and they're highly subjective.

So families have a distorted view of how their kids are doing in school. That has to change. And the best way to do that is to get on the same page, review data, make plans together, get together on a regular basis. 

So there are schools, plenty of schools, that have a fall conference where they review data, and they make a plan. And then, they meet again in the winter to review how that's going and adjust as necessary, and then get together in the spring to review progress and make a summer plan. So let's focus on student learning and well-being. The pillar is to anchor in student learning and well-being.

And then, the third pillar is to build an infrastructure, because you have to build a scaffold for this to actually function in schools and systems. Like, you can't just lop on a good idea on a system that isn't prepared to manage it or lead it. So there are some school districts, for instance, that have senior, cabinet-level leaders for family engagement.

In many places, the leader for family engagement at the district is nowhere near the senior-level leadership. And so that limits the ability of family engagement to be on the agenda and to be integrated into these systemic strategies of the district. So I'll give you two examples.

In Baltimore, Baltimore, Maryland, Baltimore City Public Schools, you've got a senior-level family engagement lead. And so what they did coming out of the pandemic is that the Chief for Engagement and the Chief Academic Officer were able to work together on a Pandemic Recovery Plan that included student learning plans for every student that were co-designed by the teacher, by the student, and by the parent. And you know, any new policy in a large system is going to take some time. It's going to take its lumps. It's going to need to evolve and adjust. But now, they're getting to the point where it's becoming common practice that teachers are supposed to-- or at least expected to-- work closely with parents in order to build those student learning plans.

In Richmond, Virginia, you have a Chief Engagement Officer that was able to reframe attendance as an engagement challenge and redeploy resources. She has a whole dashboard where schools are able to input and track all of the efforts that they do to engage families directly on attendance. And she's able to see that, and she's able to help schools improve. And so now, they're like slowly-- like they've gotten much better attendance outcomes because they have a whole system-wide strategy to address it from the engagement perspective.

So you have senior-level leadership. You have PD time allocated. You have actual time in the teachers' calendars so that they can build a relationship. And you help them build the skills, and get over fears, and be able to feel comfortable and confident and collaborate with one another so that they can start to have more robust engagement strategies in the school that actually lead to improvements for kids.

JILL ANDERSON: We all know that changing the system takes time, but I'm wondering, are there any low-hanging fruits that exist for family engagement that a school district should definitely be doing-- should start as soon as possible if they're not currently doing them?

EYAL BERGMAN: Look, I think at the very least, we should be calling parents and telling them, ‘Hey, I'm going to be your child's teacher this year. I'm really excited to have them in school. I would love to hear a little bit about how last school year went for you, and what you're looking forward to this year.’ And let's have the mindset that we're going to treat parents as humans and then actively reach out to the other side. 

There's a lot of barriers for parents to come up to the school, and it's not just that they're working two or three jobs. It's that school feels like a foreign place to them. It's that they're speaking to an authority. They're speaking to folks that they haven't necessarily had good experiences within their own lives. They may be coming with their own traumas.

So at the very basic, what we should be doing is taking it upon ourselves to reach out to parents. That's the most effective low-hanging fruit, is to reach out, build relationship on the front end. Any teacher will tell you that when you build a relationship with a family on the front end, it more than pays dividends throughout the year.

And so principals, what they can do is they can turn over a whole staff meeting and say, OK, instead of 60 minutes of us talking or me talking at you, I want you to call five parents and tell them how excited you are. And then, bring in PD, and build from there. There's plenty of apps that are helpful, also, for communication, but let's not assume that those apps that are for communication are going to necessarily build trust. We need to do one-on-one trust-building.

JILL ANDERSON: Eyal Bergman is the Senior Vice President at Learning Heroes. I'm Jill Anderson. This is the Harvard EdCast, produced by the Harvard Graduate School of Education. Thanks for listening. 

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The Morning

A crisis of school absences.

Fewer children are attending school, across rich and poor districts.

By Sarah Mervosh

A few years ago, a troubling phenomenon began to spread in U.S. education: Students were not showing up to school.

This was not particularly surprising. Schools had shut down in the spring of 2020, at the start of the pandemic, and some did not fully reopen until fall 2021. Quarantines for Covid symptoms and exposures were still common. It would take time, many thought, to re-establish daily routines.

What is surprising is how little the numbers have budged since, an issue my colleague Francesca Paris and I explore in depth in a new article published today .

Before the pandemic, about 15 percent of U.S. students were chronically absent, which typically means missing 18 days of the school year, for any reason. By the 2021-22 school year, that number had skyrocketed to 28 percent of students. Last school year, the most recent for which national estimates are available, it held stubbornly at 26 percent.

In interviews, many educators say the problem is continuing this school year.

Perhaps most strikingly, absenteeism has increased across demographic groups, according to research by Nat Malkus, a senior fellow at the American Enterprise Institute. Students are missing more school in districts rich and poor, big and small.

Increase in chronic absenteeism

All students

By child poverty rates

Richest districts

By lengh of school closures

Most remote

Most in-person

By length of school closures

Even the length of school closures during the pandemic was not a particularly useful predictor of absenteeism. On average, districts that were closed longest have experienced similar increases as those that opened sooner.

What is going on here?

I spoke with school leaders, counselors, researchers and parents. They offered many reasons for the absences: illness, mental health, transportation problems. But underlying it all is a fundamental shift in the value that families place on school, and in the culture of education during the pandemic.

“Our relationship with school became optional,” said Katie Rosanbalm, a psychologist and associate research professor at Duke University.

A cultural shift

To some degree, this is a problem facing society at large since the pandemic. Anyone who works in an office with a flexible remote-work policy will be familiar with the feeling: You diligently show up, but your co-workers aren’t there. What’s the point?

Something similar may be going on in schools.

Though school buildings are open, classes are in person and sports and other extracurricular activities are back in full, the stability of school seems to have shifted.

For one thing, teachers are also missing more school , often because of professional burnout or child care challenges — or because, since the pandemic, more people are actually staying home when they’re sick.

Some schools have kept their pandemic policies around online class work, giving the illusion that in-person attendance is not necessary.

And widespread absenteeism means less stability about which friends and classmates will be there. This can beget more absenteeism. For example, research has found that when 10 percent of a student’s classmates are absent on a given day, that student is nearly 20 percent more likely to be absent the following day. “We are seeing disengagement spreading,” said Michael A. Gottfried, a professor at the University of Pennsylvania who has studied this issue.

Sign of other problems

This cultural shift is not simply a hit to perfect attendance records.

The share of students missing many days of school helps explain why U.S. students, overall, are nowhere close to making up their learning losses from the pandemic . Students who are behind academically may resist going to school, but missing school also sets them further back. These effects are especially pernicious for low-income students, who lost more ground during the pandemic and who are more negatively affected by chronic absence.

Absenteeism is also closely linked to other challenges schools have faced since the pandemic, including a rise in student anxiety and behavioral problems.

“The pandemic increased stress in every way in our lives, but it really embedded ourselves in our stress response system — fight, flight or freeze,” Dr. Rosanbalm, the Duke psychologist, said.

An increase in behavioral problems in schools is an example of the “fight” response, she said. On the other hand, she added, “flight is absenteeism.”

For more: A tool in our article lets you see the absenteeism numbers for public school districts in most states.

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Barack Obama and Bill Clinton joined President Biden for a fund-raiser at Radio City Music Hall in New York. Biden also gathered major donors privately .

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The last two coal-fired power plants in New England are set to close . New England will be the second region in the U.S., after the Pacific Northwest, to stop burning coal.

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Here are columns by David Brooks on the rise and fall of liberalism and David French on minors using social media .

MORNING READS

Accent chair: See the most influential pieces of furniture from the last 100 years.

Hilarity and wonder: Meta’s glasses are becoming artificially intelligent. We tried them .

Dogs: A German breeding bill could lead to bans for the beloved Dachshund .

Modern Love: “ How I learned to trust (some) men .”

Lives Lived: Linda Bean was a granddaughter of L.L. Bean. She used her wealth to support right-wing causes and politicians, to amass paintings and properties associated with the Wyeth art family and to become an entrepreneur in her mid-60s. She died at 82 .

March Madness: Alabama upset the No. 1 seed North Carolina to reach its first men’s Elite Eight in 20 years.

M.L.B.: Commissioner Rob Manfred said he hoped the league’s investigation into the gambling allegations surrounding Shohei Ohtani’s former interpreter would be “short.”

N.H.L.: A Russian hockey player is expected to finally come to North America after being drafted nearly nine years ago. During the wait, he was arrested and forced into military service .

U.S. Soccer: Korbin Albert, a rising star, apologized yesterday after Megan Rapinoe criticized anti-L.G.B.T.Q. content that Albert had reposted on social media.

Caitlin Clark : The Iowa star was among the 14 players selected for Team USA’s training camp in Cleveland.

ARTS AND IDEAS

A new album: Beyoncé has gone country. Her just-released album, “Cowboy Carter,” has plucked banjos, lines about hoedowns and cameos from Dolly Parton and Willie Nelson.

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THE MORNING RECOMMENDS …

Roast salmon with peas and radishes for a quick dinner .

Jog with these strollers .

Give a good gift to a frequent traveler .

Make over your foyer .

Take our news quiz .

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And here are today’s Mini Crossword , Wordle , Sudoku and Connections .

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Sarah Mervosh covers education for The Times, focusing on K-12 schools. More about Sarah Mervosh

ORIGINAL RESEARCH article

Measuring school absenteeism: administrative attendance data collected by schools differ from self-reports in systematic ways.

• 1 Research Group TOR, Department of Sociology, Vrije Universiteit Brussel, Brussels, Belgium
• 2 Centre for Educational Effectiveness and Evaluation, Faculty of Psychology and Educational Sciences, KU Leuven, Leuven, Belgium

In order to use attendance monitoring within an integrative strategy for preventing, assessing and addressing cases of youth with school absenteeism, we need to know whether the attendance data collected by schools cover all students with (emerging) school attendance problems (SAPs). The current article addresses this issue by comparing administrative attendance data collected by schools with self-reported attendance data from the same group of students (age 15–16) in Flanders, the Dutch-speaking part of Belgium ( N = 4344). We seek to answer the following question: does an estimation of unauthorized absenteeism based on attendance data as collected by schools through electronic registration differ from self-reported unauthorized absenteeism and, if so, are the differences between administrative and self-reported unauthorized absenteeism systematic? Our results revealed a weak association between self-reported unauthorized school absenteeism and registered unauthorized school absenteeism. Boys, students in technical and vocational tracks and students who speak a foreign language at home, with a less-educated mother and who receive a school allowance, received more registered unauthorized absences than they reported themselves. In addition, pupils with school refusal and who were often authorized absent from school received more registered unauthorized absences compared to their self-reported unauthorized school absenteeism. In the discussion, we elaborate on the implications of our findings.

Introduction

School absenteeism is a serious problem among youth. Youth with school attendance problems (SAPs) report lower academic efficacy, poorer academic performances, more anxiety, more symptoms of depression and less self-esteem ( Kearney, 2008 ; Reid, 2014 ). In addition, school absenteeism is often embedded in a broader pattern of social deviant behavior: youth with attendance problems have an increased risk of stealing, getting involved in vandalism and are more likely to partake in behaviors at the risk of their health (e.g., smoking, substance use; Maynard et al., 2012 ; Reid, 2014 ). These specific problems may in turn reinforce long-term SAP and give rise to a vicious circle eventually increasing the risk of early school leaving and later unemployment ( Archambault et al., 2009 ; Rumberger, 2011 ; Cabus and De Witte, 2015 ). Hence, early identification of youths with relatively new absentee problems is paramount to prevent more severe and enduring SAPs ( Kearney and Graczyk, 2014 ; Ingul et al., 2019 ).

In order to optimize identification of youth with (relatively new) absentee problems, many countries invest in attendance monitoring through centralized student management systems. Daily monitoring of students’ attendance is used to ensure fast detection and to enable schools to adopt strategies to intervene when youth have emerging SAPs. More recently, it has been emphasized that in order to maximize early identification of attendance problems, schools need to make better use of their data by also analyzing their collected attendance data ( Reid, 2014 ; Kearney, 2016 ; Chu et al., 2019 ). Reid (2014) , for example, stresses that an analysis of school attendance data enables schools to identify the causes and school-specific issues of absenteeism. Attendance data can be produced weekly, monthly or yearly and can indicate trends between classes and types of attendance (e.g., seasonal attendance, luxury absenteeism). By using this information, schools can optimize early interventions and create tailor-made strategies. Similarly, Chu et al. (2019) assert that actively analyzing attendance data enables schools to provide attendance feedback to key stakeholders such as students, parents, and counselors. Accordingly, they can use this data to create individualized intervention plans for students or use the data as part of comprehensive school interventions. The extent to which schools maximize the potential of attendance data, however, depends on certain preconditions. This obviously includes the degree of data literacy of the school actors involved ( Mandinach, 2012 ), but also a good understanding of the collected data. Understanding the nature of absenteeism at a school is a crucial first step to appoint more targeted, individualized interventions. To ensure that this process runs efficiently, however, it is important to assess whether certain groups of students are more or less likely to be present in these registration data, compared to information they report themselves. Indeed, in order to apply attendance monitoring within an integrative strategy for preventing, assessing and addressing cases of youth with school absenteeism (cf. Kearney, 2016 ), we need to know whether the attendance data collected by schools covers all students with (emerging) SAPs.

This article contributes to the aforementioned literature by comparing administrative attendance data collected by schools with self-reported attendance data from the same group of students in Flanders, the Dutch-speaking part of Belgium ( N = 4344). As far as we know, this study is novel in investigating this relationship. The key questions concern whether an estimation of unauthorized absenteeism based on attendance data as collected by schools through electronic registration differs from self-reported unauthorized absenteeism. And if so, whether any differences between administrative and self-reported unauthorized absenteeism are systematic? In other words, are there specific groups of students who are systematically under or overrepresented according to the chosen measurement technique? The latter would indicate that certain types of (emerging) SAPs are more or less prevalent in administrative attendance data when compared to self-reported data.

Strengths and Limitations of Administrative and Self-Report Attendance Data

School absenteeism is generally measured by means of one out of three different types of data collection strategies: surveys, registration data from school administration or through secondary sources (parents, peers). In this study we focus on self-reported school absenteeism and administrative school attendance data. This section briefly reviews the strengths and limitations of both measurement techniques. Rather than providing a general overview of the strengths and limitations of the data types, we primarily aim to inventory reasons to expect that attendance data as collected by schools (will not) cover all students with (emerging) SAPs. This focus on registration data is justified by the fact if schools aim to include data in their school policies, they are most likely to rely on registration data. Furthermore, we want to know which specific groups of students are more or less likely to be present according to the measurement technique.

Administrative Data on School Attendance

Analyses on administrative data of school attendance rely on absences that are recorded by the school staff. In most countries, teachers register school attendance for all students per lesson or per (half) school day. Attendance is monitored by administrative assistants who define whether an absence is (un)authorized and notify school counselors when students exceed a certain threshold of unauthorized absences. Obviously, only those absences that are effectively detected by the school (and defined as unauthorized) are included in administrative data. One strength of administrative data is that they are collected for all students. This implies, for example, that unlike self-reported survey data (see next section), administrative data on school attendance also contains information on groups of students who represent only a very small percentage of the total student population (i.e., students with a specific ethnic background or special needs). Nevertheless, administrative data suffer from at least two limitations.

First, in certain situations, a registered unauthorized absence has little to do with a young person not going to school while having the opportunity to do so. This concerns, for example, absences due to illness which are not justified through a doctor’s note and/or parental consent for the absence. In particular, the latter might apply to children living in low income households due to the financial costs of medical consultation. In such cases, administrative school attendance data are likely to overestimate the level of unauthorized absences from school in a non-random way.

Secondly, there are also indications that official statistics underestimate the amount of absenteeism which is taking place in schools because certain categories of absenteeism remain undetected or are falsely reported as authorized. The first category concerns pre-planned school absenteeism during specific lessons or with specific teachers for which the risk of getting caught is known to be limited. In this context, Reid (1999) distinguishes between specific lessons absenteeism and post-registration truancy. Specific lesson absenteeism refers to the chronic skipping of a specific subject area due to content or the instructor. According to Reid (1999) , specific lesson absences originate from a negative student-teacher relationship or dislike of the subject. Keppens and Spruyt (2016 , 2017a) argue that it may also be due to an estimated low probability of getting caught whereby some students take advantage of teachers who are sloppier in the registration of absences. Post-registration truancy refers to truancy that occurs after students are registered as being present at school ( O’Keefe, 1993 ; Reid, 1999 ; Keppens and Spruyt, 2016 ). Hence, post-registration truancy can be considered a specific type of pre-planned specific lesson absence.

A second category of a type of school absenteeism that is more likely to be registered as an authorized absence from school is due to parental consent for the absence. In the first place, this concerns school withdrawal, defined by Heyne et al. (2019 , p. 23) as an absence which is (a) not concealed from the parent(s) and (b) attributable to active parental effort to keep the young person at home, or little or no parental effort to get the young person to school. Absenteeism with parents’ knowledge but not consent is called school refusal. The latter refers to a refusal to attend school (a) in conjunction with emotional distress, (b) with parents’ knowledge, (c) without display of antisocial behavior or (d) when parents have made reasonable efforts or express their intention to secure attendance at school ( Heyne et al., 2019 , pp. 22–23).

Self-Reported Attendance Data

In the literature, school absenteeism is most often measured through self-reported data ( Maynard et al., 2012 ; Havik et al., 2015 ; Keppens and Spruyt, 2016 ), irrespective of whether it is combined with reports from the parents ( Kearney and Silverman, 1993 ; Kearney, 2002 ). In these studies, young people themselves indicate whether or not they missed school. One of the main strengths of the self-report method is the capacity to investigate the etiology of school absenteeism by means of collecting comprehensive information on individual, familial, school and societal characteristics and influences. The self-report method allows differentiation between different types (e.g., truancy, school refusal, specific lesson absence, school withdrawal), and reasons for (the maintenance of) SAPs ( Kearney, 2007 ; Keppens and Spruyt, 2016 ; Heyne et al., 2019 ). This enables one to grasp certain types of school absenteeism (e.g., pre-planned truancy, school refusal) which are difficult to detect in registration data. Hence, one could argue that the measurement of school absenteeism through the self-report method complements administrative school attendance data. However, authors also indicate that self-reported measures of school absenteeism are plagued with a number of problems, resulting in under- or over-reporting.

First, measuring unauthorized school absenteeism through the self-report method may introduce problems because the aim is to gauge behavior that is deviant or delinquent. For example, truancy, defined by Heyne et al. (2019 , p. 23) as an absence which occurs (a) when a young person is absent from school for an entire day or part of the day, or at school but absent from the proper location, (b) without the permission of the school authorities and (c) when the young person tries to conceal the absence from their parents, is considered a status offense ( Zhang et al., 2007 ). Hence, respondents are more likely to conceal or fail to recall their truancy out of fear of the consequences, resulting in an underestimation of the actual truancy rate. In this context, research suggests that this underestimation is structurally higher among ethnic minority youth ( Kirk, 2006 ; van Batenburg-Eddes et al., 2012 ). For example, a Dutch study investigating the discrepancy between self-reported juvenile delinquency and official police statistics found that, in particular, Moroccan youth are less inclined to admit delinquent behavior. The study also showed that this is due to (a) discrimination by the police and (b) a higher level of suspicion toward the authorities due to higher feelings of stigmatization ( van Batenburg-Eddes et al., 2012 ). The same reasoning may apply to the self-reporting of unauthorized absenteeism, and particularly truancy. Zhang (2003) , for example, problematizes the subjectivity in authorizing absences since the attendance regulations stipulate that it is up to the school staff to decide which absence should be authorized. In these circumstances, it is plausible that certain students (whose school absenteeism is accompanied by other school misbehavior) or certain types of absences (truancy) are more easily registered as unauthorized than others. Skiba et al. (2011) , for example, show that ethnic minorities in the United States are more likely to be referred for truancy as compared to their white peers (African American youths in grade 6 to 9 are 4.40 times more likely to be referred for truancy than their white peers; Hispanic/Latino youth in grade 6 to grade 9 are 2.44 times more likely to be referred for truancy than their white peers). Skiba et al. (2011) also demonstrated that ethnic minorities are more likely than their white peers to receive expulsion or out of school suspension as a consequence of referred truancy. Hence, ethnic minorities might (compared to their peers without a migration background) be overrepresented in administrative data on absenteeism because of discrimination by the school staff. However, at the same time, ethnic minorities might also be underrepresented in the self-reported school absenteeism data due to feelings of suspicion toward the school authorities when filling in self-reported questionnaires on deviant behavior.

A second limitation of the self-report technique is that it relies on students’ recollections of their absenteeism and this might undermine the reliability of the data. This applies in particular to self-report measures that rely on longer time frames. The longer this period, the greater the chance that the self-reported absenteeism will deviate from the real absenteeism rate ( Stone et al., 2000 ; Kirk, 2006 ). However, it should also be noted that self-reported measures that use a shorter reference period to measure absenteeism (for example, 2 weeks) may lead to an underestimation of school absenteeism. When the reference period is short, there will likely be an underreporting of students who are only absent a few times a year ( Keppens and Spruyt, 2017b ).

The Current Study

The preceding arguments suggest that self-reported data and administrative data on school absenteeism are each associated with some advantages and disadvantages due to their specificity. The added value of self-reported data on school absenteeism is that it enables stakeholders to assess absenteeism in more detail. Certain types of absences that remain invisible in administrative data on absenteeism are more likely to be grasped with the self-report technique. In this way, self-reported data on school absenteeism provide an indication of the extent to which administrative data on absenteeism cover all students with (emerging) SAPs. Against this background, this paper is the first study that compares self-reported data on school absenteeism with administrative data of unauthorized absences among (the same group of) students from the fourth year of secondary education in Flanders. More specifically, we investigate: (1) the extent to which self-reported data on school absenteeism and administrative data of unauthorized absences gauge the same behavior, and (2) the extent to which possible discrepancies are related to the type of school absenteeism (e.g., truancy, school refusal, school withdrawal, pre-planned truancy and authorized school absenteeism) and students’ characteristics (in particular, ethnicity and SES).

Materials and Methods

Study design.

To answer our research questions, we merged self-reported data on school absenteeism from the longitudinal LiSO (Educational Trajectories in Secondary Education) project with data from the administrative database on absences from the Flemish Ministry of Education and Training (named DISCIMUS in the remainder of this paper).

The LiSO project follows a cohort of 6457 students in 57 schools who started secondary education in the school year 2013–2014 ( Stevens et al., 2015 ). A regional sampling strategy was used whereby nearly all students in the targeted cohort who attended school in the target geographic region were included in the study ( Dockx et al., 2019 ). For the present study, data were used from wave 4 (T4) which was gathered at the end of the fourth year (May 2017) of secondary education (age 15–16). T4 is the only wave that included items gauging self-reported school absenteeism. The total sample of students in T4 consisted of 6545 students in 53 schools. Within this sample, 4344 students completed the questionnaire in a valid way resulting in a total response rate of 66.69%.

Registration data on absences among all students in primary and secondary education are collected by the Flemish Agency for Educational services (AGODI). In Flanders, school attendance is registered twice a day. There are many reasons why a student is absent from school. Absences due to illness (and authorized by a doctor or through a parental note) 1 , a funeral of a relative or religious holidays are authorized. When a student has no justified reason for his/her absence (i.e., has an unauthorized absence from school), s/he receives, per half school day, a so-called “B-code”. Schools automatically exchange these registered absences (all absences including unauthorized absences) within a centralized database (DISCIMUS). This enables the Flemish Ministry of Education and Training to link the collected data to other student characteristics. At any time, schools can request the absences they have registered. As a result, the registration data on school absenteeism in Flanders is not only used to intervene at the level of the students 2 , but also to gain insight into the distribution of all absences across different classes and school years. In general, Flanders can be considered as one of the forerunners in Europe when it comes to the accurate and systematic collection of data on school absenteeism among students who follow compulsory education ( European Commission, 2013 ).

In DISCIMUS, each student has a unique identification number. In this paper, we used this unique identification number to merge data from the DISCIMUS database with data from the LiSO database. Only registrations of unauthorized absences that occurred before filling in the LiSO questionnaire were considered.

Because this study involved students in Flemish secondary education and was an initiative of the Flemish government, approval was required of the Belgian Commissie voor de bescherming van de persoonlijke levenssfeer (Commission for the protection of the personal privacy). The Commission approved the data collection of the LiSO-project. Parents and students have been informed yearly, with a personal letter and the schoolreglement (school charter). A schoolreglement in Flanders is a document that contains the specific regulations of the school and its pedagogical project. It needs to be signed by the parents and the student to declare that they agree with the regulations and pedagogical project of the school. By signing this document, they also agree to participate with the LiSO-project and other studies that the school had chosen to participate in.

However, even after signing to agree with the school charter, parents and students can still choose to opt out of a study. This procedure was also approved by the Commissie voor de bescherming van de persoonlijke levenssfeer. The linking of the data of the LiSO-project and DISCIMUS poses no specific issues, for the Commissie voor de bescherming van de persoonlijke levenssfeer approved that the data can be linked to other datasets. Furthermore, parents and students were informed in the personal letter and the school charter that such linking of data would occur.

Questionnaire Data

Self-reported unauthorized school absenteeism was measured through the following question: “How many times did you skip school without a valid reason in the current school year?” Students who reported to have skipped school at least once were asked about whether their parents knew about the absence and if so whether they approved the absence. These characteristics allowed us to differentiate between three types of SAP: truancy, school refusal and school withdrawal ( Heyne et al., 2019 ). In this study, and following Heyne et al. (2019) , unauthorized absences that are concealed from the parents were labeled as truancy . Unauthorized absences that occurred with knowledge of parents, but without consent were labeled as school refusal . Unauthorized absences that occurred with approval of the parents were labeled as school withdrawal . In addition, information was gathered on pre-planned truancy and self-reported authorized absenteeism. Pre-planned truancy was measured by asking students who reported to have skipped at least once whether their unauthorized absences were discovered by the school staff. Self-reported authorized absenteeism was measured by asking: “How often were you absent from school for a valid reason this school year due to family or personal reasons (e.g., death of a friend or family member) or illness (I had a valid note from my parents or the doctor)”. Respondents answered on a Likert-scale ranging from 1 (never) to 5 (more than 10 times).

Administrative Data

Registered unauthorized absences are measured through the number of “B-codes” in the DISCIMUS dataset. A student receives a B-code for each half school day of unauthorized absence. In other words, a student who had an unauthorized absence for a whole school day receives 2 B-codes. The school year 2016–2017 in fulltime secondary education counted 316 half school days, which equals the maximum number of B-codes a student can receive for that school year. The rate of B-codes among the students in our sample ranged from 0 to 101 ( M = 2.41, SD = 6.75). To compare the registered and self-reported unauthorized absences, the following procedure was used. First, every day on which a student was absent for the whole school day (i.e., for which s/he received 2 B-codes) was recoded to 1. Since the self-reported measure of unauthorized absenteeism asks respondents to report how many times they skipped school, students who were absent for a whole school day will likely report this as one time. Next, we recoded the number of B-codes to match the categories used in the self-report measure: none, once, 2 times, 3 times, 4 times, 5 times, 6 times, 7 times, 8 times, 9 times, 10 to 15 times, 15 to 20 times, or more than 20 times. In addition, information on the characteristics of the students were obtained, including gender, ethnicity (speaks foreign language at home), age, educational track (general/arts or technical/vocational) and SES. The latter is measured through the educational level of the mother and whether the student receives an education allowance.

Statistical Analyses

In this study we conducted Poisson multilevel regression analyses (with STATA 14) with the prevalence of registered unauthorized school absences as dependent variable to assess the relationship between self-reported and registered unauthorized school absenteeism. A Poisson model is the most suitable technique since our measures of unauthorized school absenteeism are count variables that are bounded by zero (one cannot be absent from school less than 0 times) and not normally distributed ( Cameron and Trivedi, 2013 ). The multilevel structure enabled us to control for differences between schools (e.g., whether schools are more or less strict in their registration and detection of unauthorized absences). The first model included the sociodemographic variables gender, ethnicity, age, educational level and SES that are known to relate to school absenteeism ( Kearney, 2008 ; Reid, 2014 ). In the second model we added the prevalence of self-reported unauthorized school absenteeism. This allowed us to assess whether the administrative data under or overestimated the degree of unauthorized school absenteeism of particular social groups, compared to the self-report data. The latter would be the case when some of the sociodemographic variables remained significant after taking into account the self-reported absences. Model 2a examines these associations for our total sample ( N = 4344). Model 2b examines these associations only for those students who reported to have an unauthorized absence from school at least once ( N = 777). This subsample included students who had valid answers on the self-reported question on unauthorized school absenteeism and all subsequent measures concerning the type of SAPs. In the third model, we analyzed whether the administrative data under or overestimated (when compared to the self-report data) the degree of unauthorized school absenteeism of certain types of school absenteeism by adding the typology of SAPs, pre-planned truancy and authorized school absenteeism.

Non-response

For the non-response analysis, students who did not (adequately) complete the questionnaire were compared with students who did. Students who did not complete the questionnaire could not because they were absent when their classmates filled in the questionnaires. Some schools were also less motivated to give students sufficient time to properly fill out the questionnaire. Students who failed to complete the questionnaire had statistically more unauthorized absences from school than students who completed a questionnaire, respectively, 13.51 to 2.62 [ F (1) = 737.58, p < 0.001].

Tables 1 , 2 present the characteristics of the study population based upon, respectively, the questionnaire data and the administrative data: 50.4% of the participants were boys, 10.5% spoke a foreign language at home, 18.1% had a less educated mother (not finished secondary education), 23.4% received a school allowance and 50.5% was enrolled in technical or vocational education. The prevalence of registered unauthorized school absenteeism was higher (39.1%) than the prevalence of self-reported school absenteeism (19.2%). Among the group of students who reported to have at least once been unauthorized absent from school, 49.4% could be categorized as truancy, 17.4% as school refusal and 33.2% as school withdrawal. Additionally, 57.8% of the students reported that their unauthorized school absenteeism was never discovered.

Table 1. Sample characteristics based upon questionnaire data.

Table 2. Sample characteristics based upon administrative data.

Table 3 shows the correlation between self-reported and registered unauthorized school absenteeism and helps to answer our first research question. We observed a weak but significant positive correlation ( r s = 0.23, p < 0.001). The strength of this correlation increased when it was re-estimated among the subsample of students who reported to have an unauthorized absence from school at least once ( r s = 0.40, p < 0.001). The same observation applies for the group of students who reported to have at least one unauthorized absence from school and who have been registered with at least 1 B-code ( r s = 0.44, p < 0.001). This indicates that the rather weak association between self-reported and registered unauthorized school absenteeism is mainly due to students who have been registered with at least one B-code but do not report to have skipped school. When we omitted this group of students, we found a medium-strong association between self-reported and registered unauthorized school absenteeism.

Table 3. Spearman correlation coefficients between self-reported and registered unauthorized school absenteeism.

Multivariate analyses enabled us to answer our second research question: whether the observed discrepancies between registration and self-reported data are related to the type of school absenteeism or the student’s characteristics ( Table 4 ). Model 1 confirms earlier research showing that unauthorized school absenteeism is more prevalent among boys, students in technical and vocational tracks and students who speak a foreign language at home and with a low SES ( Kearney, 2008 ; Reid, 2014 ). Model 2 shows significant associations between all of our inserted student characteristics and registered unauthorized school absenteeism after controlling for self-reported unauthorized school absenteeism. In other words, boys, students in the technical and vocational tracks and students who speak a foreign language at home, with a low-educated mother and who received a school allowance received more B-codes than they reported themselves. The same applied for older students. For model 2b, only students who reported to have an unauthorized absence from school at least once were selected ( N = 777). We observed no large discrepancies between model 2a and 2b, except for gender 3 . Model 3 indicates that, in particular, students with school refusal received more B-codes compared to their self-reported rate of unauthorized school absenteeism. The same applied for authorized school absenteeism. Students who (often) had authorized absences from school received more B-codes compared to their self-reported unauthorized school absenteeism. Finally, we found that students who pre-planned their school absenteeism and reported that their absenteeism had never been discovered received less B-codes when compared to the rate of unauthorized school absenteeism that they reported themselves.

Table 4. Results of Poisson multilevel analyses on the association between registered unauthorized school absenteeism, self-reported unauthorized school absenteeism, student’s characteristics and the type of school absenteeism.

Early identification and intervention of SAPs is crucial to restoring regular school attendance and limiting the long-term impact of these SAPs on students’ educational trajectories. In the literature, much attention has been devoted to so-called Response to Intervention frameworks (RtI), sometimes also referred to as Multi-tiered Systems of Support frameworks (MTTS) ( Kearney and Graczyk, 2014 ; Kearney, 2016 ; Chu et al., 2019 ; Heyne, 2019 ; Ingul et al., 2019 ). RtI refers to a systematic and hierarchical decision-making process to assign evidence-based strategies based on students’ needs and in accordance with regular progress monitoring. A RtI framework applied to school attendance promotes regular attendance for all students at TIER 1, targeted interventions for at-risk students at TIER 2, and intense and individualized interventions for students with regular absenteeism at TIER 3 ( Kearney and Graczyk, 2014 ; Kearney, 2016 ). In order to work successfully, the RtI framework relies strongly on a valid and reliable identification and detection system. Only when a new absentee problem is identified, early intervention can be initiated in order to prevent absenteeism becoming more severe and chronic. In the present study, we built on this perspective by assessing the systematic (mis)match between absenteeism as registered by schools compared to self-reports. Based on unique survey data among 4344 students (aged 15–16) that could be linked to administrative data we found a weak correlation between measures of unexcused school absenteeism. Moreover, the mismatch between registration and self-report data was systematic with boys, students in technical and vocational tracks and students who speak a foreign language at home, with a less-educated mother and who receive a school allowance having consistently higher rates of registered unauthorized absenteeism compared to what they reported themselves. In addition, pupils with school refusal and who were often authorized absent from school received more registered unauthorized absences compared to their self-reported unauthorized school absenteeism. What implications do these two key findings have?

First, regarding the weak association between self-reported unauthorized school absenteeism and registered unauthorized school absenteeism, the rate of registered unauthorized school absenteeism was approximately twice as large compared to the rate of self-reported school absenteeism. Several mechanisms may help to explain this discrepancy. Some students pre-plan their truancy and do everything to avoid being caught ( Keppens and Spruyt, 2017a ). Other students might be more suspicious when they report their unauthorized absences and consequently provide fewer valid responses in a questionnaire. In other cases, the observed discrepancy may be due to biased school staff when deciding whether or not an absence is authorized or due to parents who legitimize the (unauthorized) absences of their children. At the same time, our findings also suggest that in order to optimize the validity and reliability of school attendance identification systems, schools need to actively analyze their attendance data. Indeed, this paper shows that to maximize the potential of attendance data and to ensure that students do not fall between the cracks of the registration system, the mere collecting and monitoring of attendance data is insufficient. Schools also need to analyze their collected data. Only by analyzing the data, trends between types of students and types of attendances can be identified. It is therefore surprising to find that the question “ how to use attendance data at a school level, within a multitier framework ” remains a largely unanswered question in the extant literature. Given the large number of youth with absences [11% of adolescents in the United States between the ages of 12–17 reported skipping school in the past 30 days and 17.82% of the 15-year-old students in the EU reported skipped school in the past 2 weeks ( Maynard et al., 2017 ; Keppens and Spruyt, 2018 )], the use of technology to enhance early identification is indispensable. Failing to answer the question how attendance data can be used at schools within a multitier framework may lead to an accountability culture in which the registration of absenteeism becomes and end in itself rather than a starting point to critically reflect on and gain more insight in to the meaning of (emerging) SAPs. This may lead to a situation in which schools are urged to implement registration systems, but lack the sufficient resources and support to guide students with SAPs in a customized way.

Second, in the context of discussions concerning interventions to reduce school absenteeism many authors lament about the lack of a unified approach to differentiate between youth with SAPs ( Heyne et al., 2019 ; Tonge and Silverman, 2019 ). According to Heyne et al. (2019) , differentiation is beneficial because SAPs are heterogenous, varying in etiology and presentation, while having associations with a broad array of risk factors. The authors argue that risk and protective factors associated with the development, maintenance, and prevention of SAPs are likely to be different for different types of SAPs. The most effective interventions might indeed be those that target the factors relevant to a particular type of SAP (see also Heyne, 2019 ). In order to integrate these perspectives within the RtI framework, we must examine whether certain specific interventions are more effective according to the type of SAP ( Tonge and Silverman, 2019 ). Following the same reasoning, we must also ensure that all types of SAPs are identified in a timely manner through attendance tracking. Concerning the latter, our results suggest that there is a particular discrepancy between self-reported unauthorized school absenteeism and registered unauthorized school absenteeism among students with school refusal. Students with school refusal received more B-codes compared to the rate of unauthorized school absenteeism that they reported themselves. A plausible explanation for this observation is that these students do not perceive their absences as unauthorized and consequently do not report them as such in self-reported questionnaires. In this paper, we measured unauthorized absenteeism by means of an item asking youth whether they have skipped school without a valid reason. As Heyne et al. (2019 , p. 7) already pointed out, the notion of skipping school without a valid reason is open to broad interpretation. Students with school refusal could have interpreted their general fear of school as a valid reason to skip school. Interestingly, we did not observe a different association between self-reported absenteeism and registered school absenteeism among students with truancy and students with school withdrawal. For both types of school absenteeism, we expected to find higher rates of self-reported absenteeism compared to the rate of registered school absenteeism. Among students who truant, the association between self-reported and registered school absenteeism is likely interrupted due to pre-planned and premeditated truancy. For those students who withdraw from school, it is probable that the association between self-reported and registered school absenteeism is interrupted by parents legitimizing their children’s absences.

Finally, we acknowledge the limitations of this study. First, as mentioned earlier, this study examines the relationship between self-reported and registered unauthorized school absenteeism while knowing in advance that both are not completely the same. A student who is ill but does not have a doctor’s note will not report that absence as unauthorized, yet it will be registered by the school staff as such. Within the same line of reasoning, some students might perceive reasons for absences as “legitimate” while these are not defined as such by the school. That is why we did not use statistical indicators which measure the degree of agreement (e.g., Kappa’s coefficient) which are often used in criminological research to compare police statistics with self-reported delinquency. In this paper, we primarily focused on the association between self-reported and registered absences and, in particular, on whether some subgroups of students or types of absence are more prevalent in some types of data. The advantage of that strategy (by means of Poisson regression analysis) is that modifications and recoding of the rate of registered absences (see section “Administrative Data”) had no effect on our conclusions. After all, we only divided the rate of unregistered absences through a constant factor. Second, relying on whether parents knew and/or approved of the absence to measure the type of absenteeism may not be optimal. Generally, truancy is characterized by a lack of parental knowledge of the absence, school refusal by parental knowledge without consent, and school withdrawal by a lack of parental consent. However, Heyne et al. (2019) note that in some cases, students with school refusal conceal their non-attendance from their parents (see also: Elliott, 1999 ). In other cases, parents might be more ambivalent toward their child with school refusal due to “overprotectiveness” of parents who are afraid of pressuring their child too much ( Heyne et al., 2019 , p. 26). Ideally, questions about a student’s reluctance or refusal to attend school are needed to more accurately differentiate between truancy, school refusal and school withdrawal. Unfortunately, these questions were not included in the self-reported questionnaire. However, these limitations do not alter the fact that this paper is among the first to gauge the prevalence of different types of absences on a large representative sample ( N = 4344). While the latter was not the objective of this paper, this research suggests, in agreement with research from Berg (2002) and Egger et al. (2003) , that the rate of school refusal is less common than truancy. In addition, the results also suggest that the rate of school withdrawal is more prevalent, compared to school refusal and slightly less than truancy. Future research on the prevalence of these types of school absenteeism is needed to strengthen the claims in this paper.

This study’s main finding is the weak association between self-reported unauthorized school absenteeism and registered unauthorized school absenteeism. The rate of registered unauthorized school absenteeism was approximately twice as large compared to the rate of self-reported school absenteeism. Boys, students in the technical and vocational tracks and students who spoke a foreign language at home, with a low-educated mother and who received a school allowance received more B-codes than they reported themselves. The same applied for school refusal and authorized school absenteeism. Students who pre-planned their truancy, on the other hand, received less B-codes than they reported themselves. More understanding of these discrepancies through future research is needed because it suggests that (1) researchers should be cautious with generalizing scientific research about school absenteeism between self-reported and administered data and (2) school staff and other stakeholders might not reach all students with SAPs when interventions and counseling are exclusively based on the registration of unauthorized absences.

Data Availability Statement

The datasets generated for this study are available on request to the corresponding author.

Ethics Statement

Ethical review and approval was not required for the study on human participants in accordance with the local legislation and institutional requirements. Written informed consent to participate in this study was provided by the participants’ legal guardian/next of kin.

Author Contributions

GK and BS designed and planned the study. GK and JD structured and analyzed the data. GK wrote the manuscript. All authors interpreted the data, took responsibility for the integrity and accuracy of the data analysis and the decision to submit this manuscript for publication, read, and approved the final manuscript.

This study was supported by the Ministry of Education and Training in Flanders (Belgium).

Conflict of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

• ^ For an absence up to three consecutive calendar days, a note from the parents is sufficient. A medical certificate from the doctor is required in the following cases: (1) if the student is ill for four or more consecutive calendar days (e.g., Friday, Saturday, Sunday and Monday = medical certificate; (2) for every absence due to illness, no matter how short, if the student was already absent four times in the same school year legitimized through a parental note; and (3) if the student is ill during exam periods.
• ^ In Flanders, schools screen the nature of each half school day of unauthorized absence from school. When this absence is regarded as high-risk or when students receive at least 5 B-codes, school counselors start up a more individualized approach.
• ^ Among the subsample of students who reported to have an unauthorized absence from school at least once, in particular, girls seem to have more B-codes compared to what they reported themselves. Subsequent analyses showed an interaction-effect between gender and self-reported unauthorized school absenteeism (results available on request). When the rate of self-reported unauthorized school absenteeism increases, the relationship between self-reported and registered unauthorized school absenteeism is stronger for girls than for boys. A possible explanation is that among students with more severe SAPs, boys are less likely to admit their “deviant” behavior.

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Keywords : school attendance problems, early identification, truancy, school refusal, school withdrawal, attendance data

Citation: Keppens G, Spruyt B and Dockx J (2019) Measuring School Absenteeism: Administrative Attendance Data Collected by Schools Differ From Self-Reports in Systematic Ways. Front. Psychol. 10:2623. doi: 10.3389/fpsyg.2019.02623

Received: 26 June 2019; Accepted: 06 November 2019; Published: 03 December 2019.

Reviewed by:

Copyright © 2019 Keppens, Spruyt and Dockx. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Gil Keppens, [email protected]

Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.

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What public k-12 teachers want americans to know about teaching.

Illustrations by Hokyoung Kim

At a time when most teachers are feeling stressed and overwhelmed in their jobs, we asked 2,531 public K-12 teachers this open-ended question:

If there’s one thing you’d want the public to know about teachers, what would it be?

We also asked Americans what they think about teachers to compare with teachers’ perceptions of how the public views them.

Related: What’s It Like To Be a Teacher in America Today?

Pew Research Center conducted this analysis to better understand what public K-12 teachers would like Americans to know about their profession. We also wanted to learn how the public thinks about teachers.

For the open-end question, we surveyed 2,531 U.S. public K-12 teachers from Oct. 17 to Nov. 14, 2023. The teachers surveyed are members of RAND’s American Teacher Panel, a nationally representative panel of public K-12 school teachers recruited through MDR Education. Survey data is weighted to state and national teacher characteristics to account for differences in sampling and response to ensure they are representative of the target population.

Overall, 96% of surveyed teachers provided an answer to the open-ended question. Center researchers developed a coding scheme categorizing the responses, coded all responses, and then grouped them into the six themes explored in the data essay.

For the questions for the general public, we surveyed 5,029 U.S. adults from Nov. 9 to Nov. 16, 2023. The adults surveyed are members of the Ipsos KnowledgePanel, a nationally representative online survey panel. Panel members are randomly recruited through probability-based sampling, and households are provided with access to the Internet and hardware if needed. To ensure that the results of this survey reflect a balanced cross section of the nation, the data is weighted to match the U.S. adult population by gender, age, education, race and ethnicity and other categories.

Here are the questions used for this analysis , along with responses, the teacher survey methodology and the general public survey methodology .

Most of the responses to the open-ended question fell into one of these six themes:

Teaching is a hard job

About half of teachers (51%) said they want the public to know that teaching is a difficult job and that teachers are hardworking. Within this share, many mentioned that they have roles and responsibilities in the classroom besides teaching, which makes the job stressful. Many also talked about working long hours, beyond those they’re contracted for.

“Teachers serve multiple roles other than being responsible for teaching curriculum. We are counselors, behavioral specialists and parents for students who need us to fill those roles. We sacrifice a lot to give all of ourselves to the role as teacher.”

– Elementary school teacher

“The amount of extra hours that teachers have to put in beyond the contractual time is ridiculous. Arriving 30 minutes before and leaving an hour after is just the tip of the iceberg. … And as far as ‘having summers off,’ most of August is taken up with preparing materials for the upcoming school year or attending three, four, seven days’ worth of unpaid development training.”

– High school teacher

Teachers care about their students

The next most common theme: 22% of teachers brought up how fulfilling teaching is and how much teachers care about their students. Many gave examples of the hardships of teaching but reaffirmed that they do their job because they love the kids and helping them succeed. 

“We are passionate about what we do. Every child we teach is important to us and we look out for them like they are our own.”

– Middle school teacher

“We are in it for the kids, and the most incredible moments are when children make connections with learning.”

Teachers are undervalued and disrespected

Some 17% of teachers want the public to know that they feel undervalued and disrespected, and that they need more public support. Some mentioned that they are well-educated professionals but are not treated as such. And many teachers in this category responded with a general plea for support from the public, which they don’t feel they’re getting now.

“We feel undervalued. The public and many parents of my students treat me and my peers as if we do not know as much as they do, as if we are uneducated.”

“The public attitudes toward teachers have been degrading, and it is making it impossible for well-qualified teachers to be found. People are simply not wanting to go into the profession because of public sentiments.”

Teachers are underpaid

A similar share of teachers (15%) want the public to know that teachers are underpaid. Many teachers said their salary doesn’t account for the effort and care they put into their students’ education and believe that their pay should reflect this.

“We are sorely underpaid for the amount of hours we work and the education level we have attained.”

Teachers need support and resources from government and administrators

About one-in-ten teachers (9%) said they need more support from the government, their administrators and other key stakeholders. Many mentioned working in understaffed schools, not having enough funding and paying for supplies out of pocket. Some teachers also expressed that they have little control over the curriculum that they teach.

“The world-class education we used to be proud of does not exist because of all the red tape we are constantly navigating. If you want to see real change in the classroom, advocate for smaller class sizes for your child, push your district to cap class sizes at a reasonable level and have real, authentic conversations with your child’s teacher about what is going on in the classroom if you’re curious.”

Teachers need more support from parents

Roughly the same share of teachers (8%) want the public to know that teachers need more support from parents, emphasizing that the parent-teacher relationship is strained. Many view parents as partners in their child’s education and believe that a strong relationship improves kids’ overall social and emotional development.

“Teachers help students to reach their potential. However, that job is near impossible if parents/guardians do not take an active part in their student’s education.”

How the U.S. public views teachers

While the top response from teachers in the open-ended question is that they want the public to know that teaching is a hard job, most Americans already see it that way. Two-thirds of U.S. adults say being a public K-12 teacher is harder than most other jobs, with 33% saying it’s a lot harder.

And about three-quarters of Americans (74%) say teachers should be paid more than they are now, including 39% who say teachers should be paid a lot more.

Americans are about evenly divided on whether the public generally looks up to (32%) or down on (30%) public K-12 teachers. Some 37% say Americans neither look up to or down on public K-12 teachers.

In addition to the open-ended question about what they want the public to know about them, we asked teachers how much they think most Americans trust public K-12 teachers to do their job well. We also asked the public how much they trust teachers. Answers differ considerably.

Nearly half of public K-12 teachers (47%) say most Americans don’t trust teachers much or at all. A third say most Americans trust teachers some, and 18% say the public trusts teachers a great deal or a fair amount.

In contrast, a majority of Americans (57%) say they do trust public K-12 teachers to do their job well a great deal or a fair amount. About a quarter (26%) say they trust teachers some, and 17% say they don’t trust teachers much or at all.

Related: About half of Americans say public K-12 education is going in the wrong direction

How the public’s views differ by party

There are sizable party differences in Americans’ views of teachers. In particular, Democrats and Democratic-leaning independents are more likely than Republicans and Republican leaners to say:

• They trust teachers to do their job well a great deal or a fair amount (70% vs. 44%)
• Teaching is a lot or somewhat harder when compared with most other jobs (77% vs. 59%)
• Teachers should be paid a lot or somewhat more than they are now (86% vs. 63%)

In their own words

Below, we have a selection of quotes that describe what teachers want the public to know about them and their profession.

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About Pew Research Center Pew Research Center is a nonpartisan fact tank that informs the public about the issues, attitudes and trends shaping the world. It conducts public opinion polling, demographic research, media content analysis and other empirical social science research. Pew Research Center does not take policy positions. It is a subsidiary of The Pew Charitable Trusts .