research paper about school dropouts fact or fallacy

• DOI: 10.1016/J.EDUREV.2013.05.002
• Corpus ID: 58891750

A Critical Review of the Literature on School Dropout

• K. Witte , S. Cabus , +2 authors H. Brink
• Published 1 December 2013
• Education, Sociology
• Educational Research Review

Tables from this paper

291 Citations

Risk factors for dropping out of high school: a review of contemporary, international empirical research, typologies of early school leavers from secondary education: a review study, mentoring as prevention of early school leaving: a qualitative systematic literature review, school dropout as the result of a complex interplay between individual and environmental factors: a study on the perspectives of support workers, an economic perspective on school dropout prevention using microeconometric techniques, what do young adults’ educational experiences tell us about early school leaving processes.

• Highly Influenced

Combating early school leaving: A qualitative study of compulsory training in Italy

A qualitative study of unveiling school dropout complexity in türkiye, supply-side antecedents of dropout rates in mba programs, 93 references, influence of high school size on dropout rate.

• Highly Influential

Large School Systems' Dropout Reports: An Analysis of Definitions, Procedures, and Findings

Withdrawing from school, dropout from secondary education: all's well that begins well, dropouts from higher education: toward an empirical model, dropping out of high school: the role of school organization and structure, dropout rates after high-stakes testing in elementary school: a study of the contradictory effects of chicago’s efforts to end social promotion, dropouts and turnover: the synthesis and test of a causal model of student attrition, dropping out of high school: the influence of race, sex, and family background, early school leaving in australia: findings from the 1995 year 9 lsay cohort. longitudinal surveys of australian youth research report., related papers.

Showing 1 through 3 of 0 Related Papers

High School Dropout, Graduation, and Completion Rates: Better Data, Better Measures, Better Decisions (2011)

Chapter: 1 introduction, 1 introduction.

H igh school graduation and dropout rates have long been used as a central indicator of education system productivity and effectiveness and of social and economic well-being. Today, interest in the accuracy and usefulness of these statistics is particularly acute owing to a confluence of circumstances, including changing demographics, new legislative mandates, and heightened political pressures to reduce the incidence of dropping out. The population of American school-age children is shifting from native whites toward minorities and immigrants, populations that have a higher risk of dropping out; the new regime of educational accountability, especially the movement toward testing for promotion and graduation, has raised fears of a secondary effect on school dropout rates. In other words, students who are unable to pass these assessments may simply leave school before graduating. In addition, the No Child Left Behind (NCLB) Act of 2002 specifically requires an indicator of educational progress other than test scores at the high school level. Timely high school graduation appears to be the indicator of choice.

HIGH SCHOOL DROPOUT AND GRADUATION RATES

Despite the strong need for sound and reliable measures of high school dropout and completion, there has been widespread disagreement among researchers, statisticians, and policy analysts about the “true” rates, how they are best measured, and what trends are evident over time. Recently, a number of analysts have argued that the growing importance of alternative high school credentials, combined with various technical problems and political pressures,

has led to serious overreporting of “official” high school graduation rates. Their analyses produce national graduation rates of about 70 percent overall and 50 percent for minorities, numbers that are lower than those reported on the basis of official government sources (e.g., Education Week , 2009; Greene and Winters, 2002; Warren, 2004). Some researchers also contend that this problem of overreporting the graduation rate has been getting worse over time (Heckman and LaFontaine, 2008, 2010). Others (e.g., Mishel and Roy, 2006) counter that these analyses are incorrect and that the graduation rate, while still unacceptably low, has been accurately reported in national government surveys and has not changed appreciably over the past 20 years. Similar discrepancies, depending on data sources and the analyses conducted, exist in dropout and graduation estimates at state and local levels. At a time when policy makers are vitally interested in tracking the incidence of dropping out of school, they are faced with choosing among substantially discrepant estimates that would lead them to different conclusions regarding both the size of the dropout problem and how it has changed in recent years.

DATA SOURCES USED FOR ESTIMATES

Estimates of these rates are derived from a variety of sources using a variety of procedures. National estimates are derived from both cross-sectional and longitudinal sample surveys. The Current Population Survey (CPS) conducted by the U.S. Census Bureau is a nationally representative cross-sectional household survey that asks detailed questions about educational enrollment and experiences in October of each year. The National Center for Education Statistics (NCES) and the Bureau of Labor Statistics periodically conduct longitudinal surveys that track representative samples of youth through the usual high school years and beyond.

School administrative records on enrollments, dropouts, and diplomas have typically been used by states and school districts for reporting these rates. These data are reported annually to NCES as part of the Common Core of Data (CCD) collection of information on public schools in the country and have also been used to generate national, state, and district estimates of dropout and completion rates. Many states and school districts now have longitudinal unit-record administrative data systems that allow them to track the progress of individual students over time. However, decisions about ways to handle specific groups of students (e.g., students who transfer or who leave school but obtain a high school equivalency credential, like the General Educational Development [GED]) can affect the statistics that are calculated, even when the same formulas are used to calculate the rates.

Each data source brings with it a unique set of issues that can substantially affect the quality and usefulness of dropout rate statistics. Rates derived from sample-based surveys (both cross-sectional and longitudinal) have

been criticized because they rely on respondent self-reports (Heckman and LaFontaine, 2008, 2010), and some have questioned the degree to which longitudinal data accurately track disadvantaged populations (see National Research Council, 2010). Rates estimated from aggregated counts in administrative data systems have been questioned when adjustments are not made to control for repeating ninth graders or to account for transfer students (Warren, 2005). The ways that states and local school districts classify students as dropouts, graduates, or completers can significantly affect the rates that are calculated.

Whatever the data source, there are also major questions in defining both an appropriate numerator and a denominator in calculating these rates. For example, should it include private school enrollees? Recent émigrés enrolled in U.S. schools but who spent most of their education outside the U.S. education system? GED recipients? Special education students? “On-time” graduates only? Obviously, these choices should be driven by the policy questions being addressed as well as the availability of the desired data. However, until recently, no standard conventions for data inclusion or exclusion have been widely accepted in the education research and policy community. Efforts by the National Governors Association represent some progress toward standardizing methods for estimating graduation rates (National Governors Association Task Force on State High School Graduation Data, 2005). Nevertheless, there remains a lack of understanding about which calculation methods and which data are most appropriate for different policy questions, and often the best data sources may not be available for the calculations.

COMMITTEE CHARGE

The Committee for Improved Measurement of High School Dropout and Completion Rates was formed to convene a workshop and to make recommendations about these issues. Specifically, the steering committee was asked to address the following questions:

What are the available measures of dropout and completion rates, how are they determined, and what are their strengths and limitations?

To what extent do current and proposed measures attain the necessary levels of accuracy, given the types of policy decision that they inform?

What is the state of the art with respect to constructing longitudinal student accounting systems for measuring dropout and completion rates? What is the feasibility and desirability of moving to such systems? What are some of the issues that need to be considered when designing these data systems?

In what ways can the analysis of data from current and proposed systems for measuring dropout and completion rates be used to help understand changes in the rates?

How can this information be used to improve practice at the local level and improve public policies at the state and national levels?

In response to the charge, the committee organized a workshop designed to explore the strengths and weaknesses of various kinds of rates, the policy decisions based on them, and the kinds of data required to inform those policy decisions. The committee began this task by conducting a review of the literature. The topic addressed by this project—dropping out of high school—is one that has been studied in great depth, and the literature base is quite expansive. A review of the entire literature base was beyond the scope and resources of this study. The committee therefore focused its review on research explicitly related to its charge: studies on the calculation of dropout and completion rates, the information needed to calculate them, and the policy uses of these rates. The committee also conducted a limited review of research on the relationships between education attainment and social and economic outcomes. This review was designed to provide context for the work and to document the value of reporting dropout and completion rates, but it was not intended to be an exhaustive review of the literature on this topic. Based on this review, the committee identified the researchers who have been actively pursuing this line of study and invited a subset of them to participate in the workshop. The committee also recruited a set of policy makers, practitioners, and stakeholders to discuss these issues during the workshop.

The workshop was held on October 23 and 24, 2008, and consisted of four panels of speakers. The first panel focused on policy uses of these rates, and panelists represented different administrative levels of the education system in this country (i.e., national, state, district, and school). The second panel made presentations about methods for calculating the rates, including discussion of the decisions required and the strengths and weaknesses of the methods. The third panel focused on development of longitudinal databases and included representatives from state and local school districts, who talked about their work to develop these systems. The final panel addressed the issue of how these data systems can be used to improve policy and practice. This panel focused specifically on early indicators of students at risk of dropping out and how this research could be used to better inform policy and practice. The workshop agenda appears in Appendix A , along with a list of workshop participants and guests. The papers and presentations from this workshop, the research that the presenters referenced, and the information that the committee gathered as part of its own literature review served as the basis for this report and the committee’s recommendations.

IMPORTANT TERMS

Throughout this report, we use several terms that warrant clarification. We use the term “graduate” to refer to a student who earns a regular high school diploma and “graduation rate” as an indicator of the percentage of students in a given population who earned a regular high school diploma. We note, however, that the definition of “regular diploma” may vary as well as the time allowed to complete it. We use the term “completer” as the all-encompassing term to refer to a student who finished high school via one of multiple ways, such as by earning a regular high school diploma, a GED, or another type of certificate (a certificate of attendance, certificate of completion, etc.). Likewise, “completion rate” indicates the percentage of students in a given population who finished high school in any of these ways. We use the term “dropout” to refer to a student who did not complete high school and “dropout rate” as an indicator of the percentage of students in a given population who did not complete high school. Dropouts may include those who earn a GED or an alternative credential (depending on the specific indicator or the purpose of the indicator), but the group does not include students still enrolled in school after they were expected to complete. There are a number of policy definitions of these terms that further specify them (e.g., NCLB specifies that the graduation rate should include only on-time diploma earners, and it classifies GED recipients with dropouts). Unless otherwise specified in the report, we use the terms in their most general sense.

There are four general categories of dropout/completion indicators, which are defined below.

Individual cohort rate: a rate derived from longitudinal data on a population of individuals who share a common characteristic at one point in time, such as entering high school. The rate is based on tracking the students over the 4 years of high school or more to determine which of them graduated and which of them dropped out.

Aggregate cohort rate: a rate designed to approximate an individual cohort rate when longitudinal data are not available by using aggregate counts of students (e.g., number of ninth graders in a given year, number of graduates in a given year). For instance, an aggregate cohort rate might compare the number of students who graduate in one year with the number of students who entered high school 4 years earlier.

Status rate: a rate that represents the fraction of a population that falls into a certain category at a given point of time (e.g., the percentage of the total U.S. population that does not have a high school diploma).

Event rate: a rate that is the fraction of a population that experiences a particular event over a given time interval. For instance, the event dropout rate indicates the percentage of students who exit school during a specific academic year without having earned a diploma.

ORGANIZATION OF THE REPORT

This report summarizes the proceedings from the workshop. Following this introduction, Chapter 2 draws on the presentations from the first panel and explains why these rates are important and how they are used for policy purposes. Based on information presented during the second panel discussion, Chapter 3 discusses the decisions that must be made in calculating these rates, and Chapter 4 explores the different types of rates and their uses. An important use of dropout and completion rates is to identify which students are likely to drop out and when they are most at risk in order to implement programs and/or interventions aimed at keeping students in school. Chapter 5 draws from several of the workshop presentations and discusses the research on early indicators of dropping out as well as on building data systems that incorporate these indicators to enable early identification of at-risk students. Chapter 6 continues the discussion of database development and summarizes the presentations made by state and district representatives participating in the third panel. Chapter 7 lays out ways the data systems can be used to improve policy and practice. The committee’s conclusions and recommendations are presented at the end of each chapter and are summarized in Chapter 8 . The workshop agenda appears in Appendix A , along with a list of workshop participants and guests. Biographical sketches of committee members and staff appear in Appendix B .

High school graduation and dropout rates have long been used as indicators of educational system productivity and effectiveness and of social and economic well being. While determining these rates may seem like a straightforward task, their calculation is in fact quite complicated. How does one count a student who leaves a regular high school but later completes a GED? How does one count a student who spends most of his/her high school years at one school and then transfers to another? If the student graduates, which school should receive credit? If the student drops out, which school should take responsibility?

High School Dropout, Graduation, and Completion Rates addresses these issues and to examine (1) the strengths, limitations, accuracy, and utility of the available dropout and completion measures; (2) the state of the art with respect to longitudinal data systems; and (3) ways that dropout and completion rates can be used to improve policy and practice.

READ FREE ONLINE

Welcome to OpenBook!

You're looking at OpenBook, NAP.edu's online reading room since 1999. Based on feedback from you, our users, we've made some improvements that make it easier than ever to read thousands of publications on our website.

Do you want to take a quick tour of the OpenBook's features?

Show this book's table of contents , where you can jump to any chapter by name.

...or use these buttons to go back to the previous chapter or skip to the next one.

Jump up to the previous page or down to the next one. Also, you can type in a page number and press Enter to go directly to that page in the book.

Switch between the Original Pages , where you can read the report as it appeared in print, and Text Pages for the web version, where you can highlight and search the text.

To search the entire text of this book, type in your search term here and press Enter .

Share a link to this book page on your preferred social network or via email.

View our suggested citation for this chapter.

Ready to take your reading offline? Click here to buy this book in print or download it as a free PDF, if available.

Get Email Updates

Do you enjoy reading reports from the Academies online for free ? Sign up for email notifications and we'll let you know about new publications in your areas of interest when they're released.

Exploring statistical approaches for predicting student dropout in education: a systematic review and meta-analysis

• Survey Article
• Published: 29 November 2023

Cite this article

• Raghul Gandhi Venkatesan   ORCID: orcid.org/0000-0001-8624-8282 1 ,
• Dhivya Karmegam   ORCID: orcid.org/0000-0003-3307-8704 2 &
• Bagavandas Mappillairaju   ORCID: orcid.org/0000-0003-4794-6250 3  

383 Accesses

Explore all metrics

Student dropout is non-attendance from school or college for an extended period for no apparent cause. Tending to this issue necessitates a careful comprehension of the basic issues as well as an appropriate intervention strategy. Statistical approaches have acquired much importance in recent years in resolving the issue of student dropout. This is due to the fact that statistical techniques can efficiently and effectively identify children at risk and plan interventions at the right time. Thirty-six studies in total were reviewed to compile, arrange, and combine current information about statistical techniques applied to predict student dropout from various academic databases between 2000 and 2023. Our findings revealed that the Random Forest in 23 studies and the Decision Tree in 16 studies were among the most widely adopted statistical techniques. Accuracy and Area Under the Curve were the frequently used evaluation metrics that are available in existing studies. However, it is notable that the majority of these techniques have been developed and tested within the context of developed nations, raising questions about their applicability in different global settings. Moreover, our meta-analysis estimated a pooled proportion of overall dropouts of 0.2061 (95% confidence interval: 0.1845–0.2278), revealing significant heterogeneity among the selected studies. As a result, this systematic review and meta-analysis provide a brief overview of statistical techniques focusing on strategies for predicting student dropout. In addition, this review highlights unsolved problems like data imbalance, interpretability, and geographic disparities that might lead to new research in the future.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Subscribe and save.

• Get 10 units per month
• Download Article/Chapter or Ebook
• 1 Unit = 1 Article or 1 Chapter
• Cancel anytime

Price includes VAT (Russian Federation)

Instant access to the full article PDF.

Rent this article via DeepDyve

Institutional subscriptions

Similar content being viewed by others

Risk Factors for School Absenteeism and Dropout: A Meta-Analytic Review

A Tutorial on Applying the Difference-in-Differences Method to Health Data

Protecting against researcher bias in secondary data analysis: challenges and potential solutions

Data availability.

All relevant data are within the manuscript and its supplementary information files.

Yukselturk, E., Ozekes, S., & Turel, Y. K. (2014). Predicting dropout student: An application of data mining methods in an online education program. European Journal of Open, Distance and E-Learning., 17 (1), 118–133.

Article   Google Scholar  

Lin, J. J. J., Imbrie ,P. K., & Reid, K. J. (2009). Student retention modelling: An evaluation of different methods and their impact on prediction results. In 2009 Research in Engineering Education Symposium REES 2009 (January).

Hu, Y.-H., Lo, C.-L., & Shih, S.-P. (2014). Developing early warning systems to predict students’ online learning performance. Computers in Human Behavior, 36 , 469–478.

Jia, P., & Maloney, T. (2015). Using predictive modelling to identify students at risk of poor university outcomes. Higher Education, 70 (1), 127–149.

Chun-Teck, L. (2010). Predicting preuniversity students’ mathematics achievement (published conference proceedings style). In: International conference on mathematics education research, multimedia university , Malaysia (pp. 299–306).

Adhatrao, K., Gaykar, A., Dhawan, A., Jha, R., & Honrao, V. (2013). Predicting students’ performance using ID3 and C4.5 classification algorithms. arXiv Preprint http://arxiv.org/abs/1310.2071

Durairaj, M., & Vijitha, C. (2014). Educational data mining for prediction of student performance using clustering algorithms. International Journal of Computer Science and Information Technologies, 5 (4), 5987–5991.

Google Scholar  

Chen, J.-F., Hsieh, H.-N., & Do, Q. H. (2014). Predicting student academic performance: A comparison of two meta-heuristic algorithms inspired by cuckoo birds for training neural networks. Algorithms, 7 (4), 538–553.

Sales, A., Balby, L., & Cajueiro, A. (2016). Exploiting academic records for predicting student drop out: A case study in Brazilian higher education. Journal of Data, Information and Management, 7 (2), 166.

Ameri, S., Fard, M. J., Chinnam, R. B., & Reddy, C. K. (2016). Survival analysis based framework for early prediction of student dropouts. In International conference on information and knowledge management , 24–28 October (pp. 903–12).

Kitchenham, B., & Charters, S. (2007). Guidelines for performing Systematic Literature reviews in SoftwareEngineering Version 2.3. Engineering , 45 (4), 1051.

Okoli, C., & Schabram, K. (2012). A Guide to Conducting a Systematic Literature Review of Information Systems Research. SSRN Electron J [Internet]. . https://doi.org/10.2139/ssrn.1954824 .

Liberati, A., Altman, D. G., Tetzlaff, J., Mulrow, C., Gøtzsche, P. C., Ioannidis, J. P. A., et al. (2009). The PRISMA statement for reporting systematic reviews and meta-analyses of studies that evaluate health care interventions: Explanation and elaboration. Journal of Clinical Epidemiology, 62 (10), e1-34.

Moola, S. (2017). Checklist for analytical cross sectional studies. Joanna Briggs Institute Rev Man. (pp. 1–7). http://joannabriggs.org/research/critical-appraisal-tools .

Karmegam, D., Ramamoorthy, T., & Mappillairajan, B. (2019). A systematic review of techniques employed for determining mental health using social media in psychological surveillance during disasters. Disaster Medicine and Public Health Preparedness, 14 (2), 265–272.

Lamote, C., Van Damme, J., Van Den Noortgate, W., Speybroeck, S., Boonen, T., & de Bilde, J. (2013). Dropout in secondary education: An application of a multilevel discrete-time hazard model accounting for school changes. Quality & Quantity, 47 (5), 2425–2446.

Márquez-Vera, C., Romero Morales, C., & Ventura, S. S. (2013). Predicting school failure and dropout by using data mining techniques. Revista Iberoamericana de Tecnologias del Aprendizaje, 8 (1), 7–14.

Şara, N. B., Halland, R., Igel, C., & Alstrup, S. (2015). High-school dropout prediction using machine learning: A Danish large-scale study. In 23rd European symposium on artificial neural networks, computational intelligence and machine learning ESANN 2015—Proc eedings 2015 (pp. 319–324).

Costa, E. B., Fonseca, B., Santana, M. A., de Araújo, F. F., & Rego, J. (2017). Evaluating the effectiveness of educational data mining techniques for early prediction of students’ academic failure in introductory programming courses. Computers in Human Behavior, 73 , 247–256.

Aulck, L., Velagapudi, N., Blumenstock, J., & West, J. (2016). Predicting student dropout in higher education. http://arxiv.org/abs/1606.06364

Weybright, E. H., Caldwell, L. L., Xie, H., Wegner, L., & Smith, E. A. (2017). Predicting secondary school dropout among South African adolescents: A survival analysis approach. South African Journal of Education, 37 (2), 1–11.

Adelman, M., Haimovich, F., Ham, A., & Vazquez, E. (2018). Predicting school dropout with administrative data: New evidence from Guatemala and Honduras. Education Economics, 26 (4), 356–372. https://doi.org/10.1080/09645292.2018.1433127

Berens, J., Schneider, K., Görtz, S., Oster, S., & Burghoff, J. (2021). Early detection of students at risk—Predicting student dropouts using administrative student data and machine learning methods. SSRN Electronic Journal, 11 (3), 1–41.

Hegde, V., & Prageeth, P. P. (2018). Higher education student dropout prediction and analysis through educational data mining. In: 2018 2nd international conference on inventive systems and control (ICISC) . IEEE [cited 2021 Oct 14]. https://ieeexplore.ieee.org/document/8398887/

Nagy, M., & Molontay, R. (2018). Predicting dropout in higher education based on secondary school performance. In 2018 IEEE 22nd international conference on intelligent engineering systems (INES) . IEEE [cited 2021 Oct 14]. https://ieeexplore.ieee.org/document/8523888/

Lee S, Chung JY. The machine learning-based dropout early warning system for improving the performance of dropout prediction. Appl Sci. 2019;9(15).

da Silva, P. M., Lima, M. N. C. A., Soares, W. L., Silva, I. R. R., de Fagundes, R. A. A., de Souza, F. F. (2019). Ensemble regression models applied to dropout in higher education. In 2019 8th Brazilian conference on intelligent systems (BRACIS) . IEEE [cited 2021 Oct 14]. https://ieeexplore.ieee.org/document/8923655/

Al-Shabandar, R., Hussain, A. J., Liatsis, P., & Keight, R. (2019). Detecting at-risk students with early interventions using machine learning techniques. IEEE Access., 7 , 149464–149478.

Barros, T. M., Neto, P. A. S., Silva, I., & Guedes, L. A. (2019). Predictive models for imbalanced data: A school dropout perspective. Education Sciences, 9 (4), 275.

Biswas, A. A., Majumder, A., Mia, M. J., Nowrin, I., & Ritu, N. A. (2019). Predicting the enrollment and dropout of students in the post-graduation degree using machine learning classifier. International Journal of Innovative Technology and Exploring Engineering, 8 (11), 3083–3088.

Del Bonifro, F., Gabbrielli, M., Lisanti, G., & Zingaro, S. P. (2020). Student dropout prediction. Vol. 12163 LNAI, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) . Springer International Publishing (pp. 129–140). https://doi.org/10.1007/978-3-030-52237-7_11

Tenpipat, W., & Akkarajitsakul, K. (2020). Student dropout prediction: A KMUTT case study. In 2020 1st international conference on big data analytics and practices (IBDAP) . IEEE [cited 2021 Oct 14]. https://ieeexplore.ieee.org/document/9245457/

Patacsil, F. F. (2020). Survival analysis approach for early prediction of student dropout using enrollment student data and ensemble models. Universal Journal of Educational Research, 8 (9), 4036–4047.

Baranyi, M., Nagy, M., & Molontay, R. (2020). Interpretable deep learning for university dropout prediction. In SIGITE 2020—Proceedings 21st annual conference on information technology education (pp. 13–9).

Nangia, S., Anurag, J., & Gambhir, I. (2020). A machine learning approach to identity the students at the risk of dropping out of secondary education in India. Advances in Intelligent Systems and Computing . https://doi.org/10.1007/978-981-15-2475-2_51

Lottering, R., Hans, R., & Lall, M. (2020). A machine learning approach to identifying students at risk of dropout: A case study. International Journal of Advanced Computer Science and Applications, 11 (10), 417–422.

Freitas, F. A. D., Vasconcelos, F. F. X., Peixoto, S. A., Hassan, M. M., Dewan, M. A. A., de Albuquerque, V. H. C., et al. (2020). IoT system for school dropout prediction using machine learning techniques based on socioeconomic data. Electronics, 9 (10), 1613.

Maldonado, S., Miranda, J., Olaya, D., Vásquez, J., & Verbeke, W. (2021). Redefining profit metrics for boosting student retention in higher education. Decision Support Systems, 143 (August 2020), 113493.

Opazo, D., Moreno, S., Álvarez-Miranda, E., & Pereira, J. (2021). Analysis of first-year university student dropout through machine learning models: A comparison between universities. Mathematics., 9 (20), 1–27.

Fernandez-Garcia, A. J., Preciado, J. C., Melchor, F., Rodriguez-Echeverria, R., Conejero, J. M., & Sanchez-Figueroa, F. (2021). A real-life machine learning experience for predicting university dropout at different stages using academic data. IEEE Access., 9 , 133076–133090.

Queiroga, E. M., Batista Machado, M. F., Paragarino, V. R., Primo, T. T., & Cechinel, C. (2022). Early prediction of at-risk students in secondary education: A countrywide K-12 learning analytics initiative in Uruguay. Information, 13 (9), 1–25.

Segura, M., Mello, J., & Hernandez, A. (2022). Machine learning prediction of university student dropout: Does preference play a key role? Mathematics., 10 (18), 3359.

Moreira da Silva, D. E., Solteiro Pires, E. J., Reis, A., de Moura Oliveira, P. B., & Barroso, J. (2022). Forecasting students dropout: A UTAD university study. Future Internet., 14 (3), 1–14.

Mnyawami, Y. N., Maziku, H. H., & Mushi, J. C. (2022). Comparative study of AutoML approach, conventional ensemble learning method, and KNearest Oracle-AutoML model for predicting student dropouts in Sub-Saharan African countries. Applied Artificial Intelligence, 36 (1), 2145632.

Dake, D. K., & Buabeng-Andoh, C. (2022). Using machine learning techniques to predict learner drop-out rate in higher educational institutions. Mobile Information Systems, 2022 , 1–9.

Niyogisubizo, J., Liao, L., Nziyumva, E., Murwanashyaka, E., & Nshimyumukiza, P. C. (2022). Predicting student’s dropout in university classes using two-layer ensemble machine learning approach: A novel stacked generalization. Computers and Education: Artificial Intelligence., 3 (March), 100066. https://doi.org/10.1016/j.caeai.2022.100066

Flores, V., Heras, S., & Julian, V. (2022). Comparison of predictive models with balanced classes using the SMOTE method for the forecast of student dropout in higher education. Electronics, 11 (3), 457.

Garcia-Zanabria, G., Gutierrez-Pachas, D. A., Camara-Chavez, G., Poco, J., & Gomez-Nieto, E. (2022). SDA-Vis: A visualization system for student dropout analysis based on counterfactual exploration. Applied Sciences, 12 (12), 5785.

Selim, K. S., & Rezk, S. S. (2023). On predicting school dropouts in Egypt: A machine learning approach. Education and Information Technologies, 28 , 9235–9266. https://doi.org/10.1007/s10639-022-11571-x

Song, Z. H., Sung, S. H., Park, D., & Park, B. K. (2023). All-year dropout prediction modeling and analysis for university students. Applied Sciences, 13 (2), 1143.

Wang, N. (2016). How to conduct a meta-analysis of proportions in R: A comprehensive tutorial . John Jay College Criminal Justice (June):1–63.

Zheng, Z., Cai, Y., & Li, Y. (2015). Oversampling method for imbalanced classification. Computer Informatics., 34 (5), 1017–1037.

Learned-Miller, E. G. (2014). Introduction to Supervised Learning (p. 3). Amherst, MA, USA: Department of Computer Science, University of Massachusetts. https://people.cs.umass.edu/~elm/Teaching/Docs/supervised2014a.pdf

Kumar, M., Singh, A. J., & Handa, D. (2017). Literature survey on educational dropout prediction. International Journal of Education and Management Engineering, 7 (2), 8.

Shahiri, A. M., Husain, W., & Rashid, N. A. (2015). A review on predicting student’s performance using data mining techniques. Procedia Computer Science., 72 , 414–422. https://doi.org/10.1016/j.procs.2015.12.157

Gray, G., McGuinness, C., & Owende, P. (2014). An application of classification models to predict learner progression in tertiary education. In 2014 IEEE international advance computing conference (IACC) . IEEE (pp. 549–554).

Arsad, P. M., & Buniyamin, N. (2013). A neural network students’ performance prediction model (NNSPPM). In 2013 IEEE international conference on smart instrumentation, measurement and applications (ICSIMA) . IEEE (pp. 1–5).

Sathya, R., & Abraham, A. (2013). Comparison of supervised and unsupervised learning algorithms for pattern classification. International Journal of Advanced Research in Artificial Intelligence, 2 (2), 34–38.

Natek, S., & Zwilling, M. (2014). Student data mining solution-knowledge management system related to higher education institutions. Expert Systems with Applications, 41 (14), 6400–6407.

Kartal, O. O. (2015). Using survival analysis to investigate the persistence of students in an introductory information technology course at METU . Middle East Technical University.

Li, Y., Yang, T., Zhou, J., & Ye, J. (2018). A multi-task learning formulation for survival analysis. Proceedings of SIGKDD . https://doi.org/10.1137/1.9781611975321.33

Bani, M. J., & Haji, M. (2017). College student retention: When do we losing them? arXiv Preprint http://arxiv.org/abs/1707.06210

Zohair, L. M. A. (2019). Prediction of student’s performance by modelling small dataset size. International Journal of Educational Technology in Higher Education, 16 (1), 1–18.

Hellas, A., Ihantola, P., Petersen, A., Ajanovski, V. V., Gutica, M., Hynninen, T., Knutas, A., Leinonen, J., Messom, C., & Liao, S. N. Predicting academic performance: A systematic literature review. In Proceedings companion of the 23rd annual ACM conference on innovation and technology in computer science education (pp. 175–99).

Kaliannan, M., & Chandran, S. D. (2012). Empowering Students through Outcome-Based Education (OBE). Res Educ[Internet]. , 87 (1), 50–63.

Arroway, P., Morgan, G., O’Keefe, M., & Yanosky, R. (2016). Learning analytics in higher education . Research report. ECAR, Louisville, CO.

Rajak, A., Shrivastava, A. K., & Shrivastava, D. P. (2018). Automating outcome based education for the attainment of course and program outcomes. In 2018 Fifth HCT Information Technology Trends (ITT) . IEEE (pp. 373–376).

Namoun, A., & Alshanqiti, A. (2021). Predicting student performance using data mining and learning analytics techniques: A systematic literature review. Applied Sciences, 11 (1), 1–28.

Chen, J., Fang, B., Zhang, H., & Xue, X. (2022). A systematic review for MOOC dropout prediction from the perspective of machine learning. Interactive Learning Environments . https://doi.org/10.1080/10494820.2022.2124425

Manjarres, A. V., Sandoval, L. G. M., & Suárez, M. S. (2018). Data mining techniques applied in educational environments: Literature review. Digital Education Review, 33 , 235–266.

Romero, C., & Ventura, S. (2020). Educational data mining and learning analytics: An updated survey. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 10 (3), e1355.

Shmueli, G. (2010). To explain or to predict? Statistical Science, 25 (3), 289–310.

Thammasiri, D., Delen, D., Meesad, P., & Kasap, N. (2014). A critical assessment of imbalanced class distribution problem: The case of predicting freshmen student attrition. Expert Systems with Applications, 41 (2), 321–330.

Kaur, H., Pannu, H. S., & Malhi, A. K. (2019). A systematic review on imbalanced data challenges in machine learning: Applications and solutions. ACM Computing Surveys, 52 (4), 1–36.

Mduma, N. (2023). Data balancing techniques for predicting student dropout using machine learning. Data, 8 (3), 49.

Mgala, M., & Mbogho, A. (2015). Data-driven intervention-level prediction modeling for academic performance. In Proceedings of the seventh international conference on information and communication technologies and development (pp. 1–8).

Adadi, A., & Berrada, M. (2020). Peeking inside the black-box: A survey on explainable artificial intelligence (XAI). IEEE Access., 2018 (6), 52138–52160.

Sghir, N., Adadi, A., & Lahmer, M. (2023). Recent advances in predictive learning analytics: A decade systematic review (2012–2022). Education and Information Technologies, 28 , 8299–8333. https://doi.org/10.1007/s10639-022-11536-0

Lakkaraju, H., Aguiar, E., Shan, C., Miller, D., Bhanpuri, N., Ghani, R., & Addison, K. L. A machine learning framework to identify students at risk of adverse academic outcomes. In Proceedings of the 21th ACM SIGKDD international conference on knowledge discovery and data mining (pp. 1909–1918).

Mduma, N., Kalegele, K., & Machuve, D. (2019). A survey of machine learning approaches and techniques for student dropout prediction. Data Science Journal, 18 (1), 1–10.

Download references

Acknowledgements

We thank Mr. Naman Gupta, Research Assistant, Department of Ophthalmology, Visual, and Anatomical Sciences, Wayne State University, Detroit, Michigan for his valuable support for database access throughout the process. We also thank Ms. Supriya Sathish Kumar, Research Scholar, Translational Medicine and Research, SRM Institute of Science and Technology, and reviewers for comments that greatly improved the manuscript.

Author information

Authors and affiliations.

Department of Mathematics, SRM Institute of Science and Technology, Kattankulathur, Tamil Nadu, 603203, India

Raghul Gandhi Venkatesan

School of Public Health, SRM Institute of Science and Technology, Kattankulathur, Tamil Nadu, 603203, India

Dhivya Karmegam

Centre for Statistics, SRM Institute of Science and Technology, Kattankulathur, Tamil Nadu, 603203, India

Bagavandas Mappillairaju

You can also search for this author in PubMed   Google Scholar

Corresponding author

Correspondence to Bagavandas Mappillairaju .

Ethics declarations

Conflict of interest.

On behalf of all authors, the corresponding author states that there is no conflict of interest.

Additional information

Publisher's note.

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Below is the link to the electronic supplementary material.

Supplementary file1 (DOCX 14 KB)

Supplementary file2 (docx 25 kb), supplementary file3 (docx 14 kb), supplementary file4 (docx 33 kb), supplementary file5 (docx 15 kb), supplementary file6 (docx 19 kb), supplementary file7 (docx 778 kb), rights and permissions.

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Venkatesan, R.G., Karmegam, D. & Mappillairaju, B. Exploring statistical approaches for predicting student dropout in education: a systematic review and meta-analysis. J Comput Soc Sc (2023). https://doi.org/10.1007/s42001-023-00231-w

Download citation

Received : 17 June 2023

Accepted : 23 October 2023

Published : 29 November 2023

DOI : https://doi.org/10.1007/s42001-023-00231-w

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• Dropout prediction
• Supervised learning
• Unsupervised learning
• Meta-analysis
• Find a journal
• Publish with us
• Track your research

An official website of the United States government

The .gov means it’s official. Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

The site is secure. The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

• Publications
• Account settings

Preview improvements coming to the PMC website in October 2024. Learn More or Try it out now .

• Advanced Search
• Journal List
• Indian J Psychol Med
• v.34(4); Oct-Dec 2012

School Dropouts: Examining the Space of Reasons

Arun n. r. kishore.

Consultant Psychiatrist, Sussex Partnership NHS Foundation Trust, Sussex, United Kingdom

K. S. Shaji

1 Department of Psychiatry, Medical College, Thrissur, Kerala, India

Background:

Dropping out of school is a worldwide phenomenon with drastic mental health consequences for children, families and society.

Aim and Materials & Methods:

This study examines school dropouts in one district in Kerala with an emphasis on looking at multiple reasons for the problem.

The most common “reason” was various Physical disorders (80, 21.8%) followed by Mental Retardation (77, 20.9%). Child labour (Employment) came last (30, 8.1%) as a “reason” while financial issues constituted 50 (13.6%). Family issues accounted for 63 (17.1%) and School-related issues 68 (18.5%).

Conclusion:

This study highlights the need to examine a space of reasons for this phenomenon with active involvement and coordination of multiple agencies to examine and support getting children back to school and prevent dropouts.

INTRODUCTION

Every year, a large number of students drop out of school worldwide. A significant number of them go on to become unemployed, living in poverty, receiving public assistance, in prison, unhealthy, divorced, and single parents of children who are likely to repeat the cycle themselves.[ 1 , 2 ]

In 1993, 27 million children entered school in Class 1 in India but only 10 million (37%) of them reached Class 10 in 2003. Dropout rates peak in the transition between Class 1 and 2 and again in Classes 8, 9 and 10. Dropout rates have remained negative between Classes 4 and 5. The state of Pondicherry improved its performance with regards to school dropouts from the fourth place in 1991 to the first in 2001, displacing Kerala as the best performing state. The states of Bihar, Jharkhand, Uttar Pradesh, and Arunachal Pradesh perform poorly in this ranking.

Government data indicate improvement in the rates of school enrolment. However, there may be problems in looking at enrolment data without attention to attendance and retention rates. Thus, the actual rates of dropout from schools may be much higher than those depicted.[ 3 ]

School dropouts in Kerala

Kerala has the unique distinction of having few school dropouts. Educational standards are reported to be high within the state. Several reasons have been quoted for Kerala's high educational achievement. Historically, social movements against the caste system, the pioneering efforts made by Christian missionaries and the educational focus of the princely states served to set a good base for education. Later, investment on education, provision of free education supported by the state, access to schools, female literacy and education, good transport facilities and remittances from abroad have added to these factors.[ 4 ]

Kerala-rates of school dropout in different classes

This graph [ Figure 1 ], based on data from the annual economic report brought out by the Government of Kerala on school dropouts,[ 5 ] shows that trends have remained the same through the years 2005 to 2009 with the overall rate remaining fairly constant. The rate in Lower Primary has hovered between 0.42% and 0.6%, Upper Primary from 0.4% to 0.52% and High School from 1.2% to 1.4%. Of late, there has been growing interest in studying and tackling the problem of school dropouts by governments.[ 6 ] There is paucity of recent published research in this area from India.

Kerala – rates of school dropout in different classes

Lack of interest in studies, poverty, poor quality of education and failure in examinations have been frequently cited as explanations for dropping out of school.[ 7 ] Child developmental factors are thought to play a role in mediating the link between dropout from school, poor scholastic performance, and poverty.[ 8 ]

Dropping out of school is a good example of an issue where a biopsychosocial perspective could be useful; where there is a confluence of biological (various neurodevelopmental issues), psychological (cognitive issues and issues connected to intelligence and learning), and social (issues of poverty, social opportunities, health provisions) factors that come into play.[ 9 , 10 ] Unfortunately, health systems have not taken this into account and have not formed partnerships with social services or Government departments.[ 2 ] School dropouts should be seen as a public health issue. There is a need for partnerships between the sectors of mental health, education, and public health to address this complex issue. This paper emphasises this by looking at the problem through different lenses.

MATERIALS AND METHODS

This study was done in Thrissur District, Kerala, as part of a programme titled “Total Primary Education” conducted by the District Administration. The aim was to identify all children who had been enrolled in government schools but failed to attend class over the past year to identify reasons for their dropout and attempt remediation. There was collaborative effort from the departments of education, revenue, health, police and a medical team. Psychiatrists from the Department of Psychiatry, Medical College Thrissur and Special educators from the NGO, ALDI (Association for Learning Disabilities, India) formed the “Medical Team.”

Stage 1: Children who had failed to attend class in the past year were identified by school teachers and Block Education units. Children above the age of 14 years were screened out, since they did not fall under the remit of the programme. Rigorous efforts were made to contact parents of those below the age of 14 years. Teachers and the parents identified a predominant “reason” for dropout (see diagram below). These “reasons” were identified from review of literature examining factors correlated with school dropout.

Stage 2: 368 children attended camps held in various panchayaths in the district with their parents or care takers. The medical team assessed children using a proforma to gather information focusing on developmental issues and assigned a diagnosis if relevant. This sometimes resulted in a reassignment of the “reason” for dropout if a medical or psychiatric disorder had been missed in the first screening by teachers. Psychosocial issues were examined in detail with the assistance of social workers.

A management and follow-up plan was outlined following discussions between the various departments. The outcome of the interventions was followed up by local Block Educational Officers.

Stage 3: Children assigned to the categories of “Physical problems,” “Mental Retardation,” “School issues,” and “Family issues” were referred to the outpatient department at the Medical College. 52 attended and were assessed and investigated in different departments within the medical college. Qualitative data were gathered from them. The flow chart for the study is given below [ Figure 2 ].

Flowchart of the study

Of the 781 school dropouts, 159 (20%) were above the age of 14 years and hence excluded from the programme. 253 (33%) children could not be traced. The rest 368 (47%) were seen in the camps in Stage 2. Of these, 246 were boys and 122 girls.

Age at dropout

The maximum number of dropouts occur between the ages of 12 and 14 years [ Figure 3 ] which is well in keeping with State and National data (Kerala State Planning Board 2005-09, NCERT 2005).

Reasons for dropout stage 2

The reasons correlated with dropouts are depicted in Figure 4 . It was difficult to categorise children under one “reason” as we often found multiple “reasons” operating at the same time. “Financial” reasons often played a role in most cases and there was overlap between “School issues” and “Family Issues.” In such cases the predominant “reason” was decided by the team and the child was then classified under that. This was done during Stage 2 when the Medical team assessed the children.

Reasons for drop out at Stage 2

The most common “reason” was various Physical disorders (80, 21.8%) followed by Mental Retardation (77, 20.9%). Child labour (Employment) came last (30, 8.1%) as a “reason” while financial issues constituted (50, 13.6%). Family issues accounted for 63 (17.1%) and School related issues 68 (18.5%).

Physical disorders leading to dropout

Several children had one form of physical disorder or another, often severe enough to prevent them from attending school [ Figure 5 ]. Disability due to cerebral palsy and post polio paralysis were the reasons in 33%. Some, who used a tricycle to get to school stopped attending when this broke down.

About 12% were mentally retarded and had physical mobility problems in addition. They had been placed in the Physical disability category in Stage 1 and were reassigned to the category of Mental Retardation in Stage 2. 21% of the children were deaf and attended special school. 10% of students were blind; some attended special schools.

Children with severe, some congenital, cardiac problems were kept at home on the recommendation of their doctors. One child who had diabetes attended the local primary care clinic for insulin injections twice a day and missed school.

4% had severe skin lesions (psoriasis), considered as contagious by the family and teachers and hence missed school.

Lack of money for treatment, poor parental literacy, and a general lack of alternatives could be cited as adding on to this “reason” for dropout.

Mental Retardation: Most had moderate or severe mental retardation with additional problems such as cardiac disorders and epilepsy. A few among these children had severe behavioural problems often repetitive behaviours such as rocking, head banging, and aggression.

Family issues

There were several strands in the narrative around family issues and dropout from school [ Figure 6 ]. Parental separation and ill heath often led to the need for girl children to work or stay back at home to care for younger siblings. Older boys dropped out to find work. Children who were orphans found foster homes with relatives. However, these were often short lived with the children being moved from home to home. Education was the loser in these cases. Alcohol abuse, dependency, and illicit brewing of alcohol by the parents were issues in some. The outcome was family bickering, quarrels, and the development of problems in children. A few children were from families who led a nomadic existence, moving from place to place seeking employment resulting in the child moving from school to school.

Issues related to school

Some families pointed out issues such as an inability to buy textbooks and a lack of transport to attend school. Several had failed a class and dropped out of school in subsequently. Some were moved to a different school and later stopped attending. There was reason to suspect academic backwardness in most of these children. All of them were given an opportunity to attend the outpatient department of the medical college for a more detailed evaluation. 14 attended and 9 of them were thought to have Specific Developmental disorders of Scholastic skills. This could not be confirmed since all of them had poor opportunities for schooling and a general deprivation making the diagnosis uncertain.

This constituted the largest group amongst reasons given for dropout at Stage 1 of screening. In Stage 2, financial issues fell to the fifth place (13.6%) as a reason for school dropout. This occurred because another, more proximal and predominant, “reason” was found for the dropout. However, it must be stated that financial issues remained significant in most cases of dropout.

This remained a significant reason for dropout accounting for 17% of the cohort. The problem was commoner in older males (girls accounted for less than 20%). Dropout occurred at a later age as compared to other groups.

Change in “reasons for dropout”

In Stage 2 of the programme, children were assessed by the Medical team. As a result, 51 (13.9%) children were reassigned other “reasons” [ Figure 7 ].

Reassignment of “reasons” for dropping out

The darker line (Stage 1) in Figure 7 shows “reasons” assigned in the first stage of the programme and the lighter grey line (Stage 2) shows reassigned “reasons” after assessment by the Medical team. The net “losers” were Financial (−36%), Family (−3%), and Physical (−5%) while the net “gainers” were Mental Retardation (+31%), Employment (+25%), and School (+17%).

A total of 341 children were readmitted to school [ Figure 8 ]. Children who were diagnosed with Mental Retardation were given a choice of admission to a special school or a local government school. The decision was based on the degree of retardation, presence of behavioural problems, and accompanying physical disability.

Children with predominant physical problems were directed to the relevant departments at the Government Medical College, Thrissur. Those with mobility issues were given assistance by the social services department.

Those who were employed were screened and readmitted to school.[ 5 , 11 , 12 ] Cases were registered against the employers under the CLP Act. Those with Financial problems were given assistance as well as advice, as deemed appropriate, by the Revenue officials. Children with problems at school and those with family-related issues were referred for further assessment to the Department of Psychiatry at the Government Medical College, Thrissur.

This study formed a part of a social programme aimed at returning children back to school by helping remediate what was perceived as the predominant reason for dropout. There was a great degree of overlap between parents' and teachers' perception on “reasons” at Stage 1. In Stage 2, 51 (13.9%) had a reassignment of these “reasons.” It would be important to unpick this. Of these 51, 25% were in employment, a fact that had been hidden from teachers at stage 1. Most parents feared reprisal and action by law enforcement agencies. Some were ashamed to admit that their children were working to supplement family income.

31% were diagnosed with Mental Retardation in the mild category in Stage 2. This had not been recognized by teachers or parents. 17% with school-related issues were children who were suspected to have some form of learning difficulty. Children in these two groups reported recurrent failures in examination though they were not retained in a class. This led to truancy and finally a refusal to go to school. Some of these children had been reenrolled in schools for mentally retarded children later. A small number of children who were in the mild category dropped out due to an inability to cope with the curriculum in mainstream schools.

Various developmental disorders have been implicated as a reason for dropout from school.[ 3 , 8 ] In the NFHS III survey (IIPS 2007),[ 13 ] “lack of interest” was cited as the most common reason for dropping out of school (36% boys and 21% girls). In an earlier NSSO survey (1998), 24.4% of respondents gave this as a reason for dropping out of school.[ 12 , 14 ] In this study, we had combined the two “reasons”—“problems at school” and “lack of motivation” of which the latter is similar to “lack of interest.” This study has shown that lack of motivation is determined by complex dynamics beyond sociodemographic factors. The role of poor academic achievement related to learning difficulties, poor physical health, exclusion due to perceived “slowness in learning,” and nutrition would need to be elucidated further.[ 15 – 17 ] The PROBE[ 18 ] survey suggests that if a child is unwilling to go to school, it is often difficult for the parents to overcome her reluctance (just as it is hard for a child to attend school against his parents' wishes). The fact that school participation is contingent on the motivation of the child is another reason why various aspects of “school quality” are likely to matter.

Physical disorders of various types accounted for the largest amongst the “reasons” for dropout and this calls for action from health departments and social service agencies. A third of children, though capable of attending, could not because of mobility issues. Children with specific disabilities of vision or hearing benefitted from special schools.

The link between child labour and dropout from school has been studied from different perspectives. It is thought that children drop out of school due to a need to supplement family income through work.[ 19 ] In Kerala, children prefer less arduous work and choose ones they believe will get them some skills such as diamond polishing or gold smithy.[ 20 ] Thus, this “reason” for dropout is more complex than a direct connection between child labour and school dropout. Basu and Van argue that the issue of poverty and child labour needs to be disaggregated. Otherwise, poverty alleviation alone would be seen as a solution. Lack of finances combined with a lack of access to credit when faced with a need to buy books, uniforms, and pay school fees could lead to dropout from school. This in turn could lead to child labour. On the other hand, once a child drops out of school, poor parental motivation combined with lack of perception of the benefits of accruing literacy and numeracy, could lead to child labour. These findings imply that easier access to credit could help reduce child labour and improve school attendance.[ 21 ] Dreze and Kingdom[ 22 ] considered parental decision making and the household situation to play an influential role in sustaining school access for the child. When children do not want to attend school, parents find it difficult to make them continue. Often, there is no cost benefit analysis of the benefits of attaining cognitive skills. The best available alternative is often chosen (girl children looking after a younger child, boys earning money through employment).

In this study, financial “reasons” though seen as predominant in 13.6% of children, actually ran as a common factor in most of the other “reasons.”

Issues in families accounted for 17% in this cohort. The narrative around this points to an intimate link between issues in families, financial issues, and child employment, calling for action from health and social sectors.

Thus, one could argue that school dropout is a phenomenon or symptoms which could be explained based on a variety of “reasons,” none of which are watertight compartments. There is relatively little research into determining the reasons why so many children drop out of schools in India. This in turn leads to a tendency to highlight single causes or explanations.[ 3 , 23 – 25 ] In Kerala, attention to pedagogical factors has increased retention of children in schools and it is perhaps time to look at other approaches to reduce dropouts further.

It might be better to think of “proximal mediating risk factors” as associated with school dropouts.[ 8 ] We would advocate that in examining the causes for dropping out of school, a “space of reasons” is examined. In this “space of reasons,” we would include poverty and lack of finances being associated with childhood developmental factors (such as learning difficulties, intellectual disorders, ADHD) and school pedagogical factors (access to school, irrelevant curricula, and poor parental perception of these issues). Thus, one would need to approach the issue from different angles or through many lenses. A multipronged approach would work better.

Source of Support: Nil

Conflict of Interest: None.

A Critical Review of the Literature on School Dropout

• December 2013
• Educational Research Review

• Independent Researcher

• Maastricht University

Discover the world's research

• 25+ million members
• 160+ million publication pages
• 2.3+ billion citations

• Kassie Wubet Ebabu

• Zahriah Zahriah

• Nosipho Mavuso
• Nobert Jere
• Nelly Sharpley

• Christian Dustmann

• Paul Smeyers

• Ruth B. Ekstrom
• Margaret E. Goertz
• Judith M. Pollack
• S.L. Christenson

• Floyd Morgan Hammack
• Soc Work Educ
• Recruit researchers
• Join for free
• Login Email Tip: Most researchers use their institutional email address as their ResearchGate login Password Forgot password? Keep me logged in Log in or Continue with Google Welcome back! Please log in. Email · Hint Tip: Most researchers use their institutional email address as their ResearchGate login Password Forgot password? Keep me logged in Log in or Continue with Google No account? Sign up

Academia.edu no longer supports Internet Explorer.

To browse Academia.edu and the wider internet faster and more securely, please take a few seconds to  upgrade your browser .

Enter the email address you signed up with and we'll email you a reset link.

• We're Hiring!
• Help Center

School Dropouts – A Theoretical Framework

http://indusedu.org/

The following article is a fragment of a PhD thesis and is aimed at outlining the main traits of the school dropout phenomenon starting from the different perspectives of the authors in defining the concept, explaining the causes, understanding the consequences. The summary of an important collection of studies on the subject is meant to serve as theoretical basis for researches in the field and to offer the premises for elaborating prevention and intervention strategies.

Related Papers

Sirous Tabrizi

Washington Office of Superintendent of Public Instruction

Pete Bylsma

Children and Youth Services Review

Cuthbert Tukundane , Alexander Minnaert

Tanja Grbesic

Indus Foundation International Journals UGC Approved , Olusegun Akinbote

In Nigeria, inclusion practice is emerging and has been required by federal law in Nigeria and advocated for by professionals. However, much of the responsibility for the successful inclusion of children with special needs rests upon the shoulders of teachers. This paper examined the pre-primary and primary school teachers' perception of and attitude towards inclusion in Ibadan, Oyo State, Nigeria. The study adopted descriptive research design. A multi-stage sampling procedure was adopted in the selection o f 200 pre-primary and primary school teachers from 15 schools (10 regular and 5 special schools) to explore their perception of and attitude towards inclusive education. Teachers' Perception of and Attitude towards Inclusion (α=0.87) was the instrument use d to collect the data. The data collected were analyzed using frequency count, simple percentage and t-test. Four research questions were answered. The results revealed that majority of the teachers have positive perception of and attitude towards inclusion respectively. The findings of this study also showed no significant difference between the special and regular school teachers in their perception of and attitude towards inclusion. The implications of the findings were discussed and recommendations were made.

Nicholas D Hartlep

Capella University Dissertation - School of Education (UMI)

Doc Debi Ash

Despite efforts to provide instructional intervention programs for students who are at risk of non-completion or who have left school without graduating, many programs are not achieving consistent success. To assess this situation, the nature of the instructional design strategies deployed within these programs was investigated, with a focus on whether participatory design principles and the student voice would enhance levels of engagement and motivation and increase the chances of graduating from high school. A mixed method, post-test two group research design using dependent samples, two tailed t-test quantitative analysis was implemented using asynchronous groups, inventories of motivation and engagement, and written observations as data collection instruments. Three distinct phases using a traditionally based lesson, participants voices in the redesign of said traditional lesson, and implementation of the redesigned lesson, led to the null hypothesis “there is no significant difference in motivation between standard and participatory design courses” failing to be rejected. Despite the failure to reject the null hypothesis in motivation, overall levels were improved in 100% of the individual categories; suggesting that participating in the design process can lead to improvements in motivation. The null hypothesis of “there is no significant difference in engagement between standard and participatory design courses” was rejected; as there was a significant increase (p =<.05) of engagement upon implementation of participatory design principles indicating that end users‘ needs are crucial in the promotion of engagement and motivation and that implementation of participatory design principles can provide what traditional instructional models, to date, have not. This study provides instructional designers, educators, and administrators with data to support the redesign of current intervention programs in order to bridge the graduation gap by utilizing the voice of the most important stakeholders: the actual students.

Journal of At-Risk Issues

David Chorney

National Dropout Prevention Center Network

Cathy Hammond , J. Smink

Loading Preview

Sorry, preview is currently unavailable. You can download the paper by clicking the button above.

RELATED PAPERS

Indus Foundation International Journals UGC Approved

Gjergji D Theodhosi

Delia Duminică

Women, Gender, and Families of Color

Dorothy E Hines

ANITA WADHWA

International Journal on School Disaffection

Mohamad Bilal Ali

Mitsi Giazitzi

Claire Gordon , marina cino

Oirc Journals

Roxana Brișcariu (Toader)

Aimee Whiteside , Somer Lewis , Amy Garrett Dikkers

Educational Sciences Theory&Practice

Mary Elizabeth Ochea

Soribel Genao

Majid Shirvani

Graham Daniel

International Journal of Educational Development

Anies Al-Hroub

Fumo Almasi

Children & Schools

Johnny S. Kim , Cynthia Franklin

Julie Tagle

Magdalena Balica , Delia Goia , Oana Benga , Oana Iftode

Ciprian Fartusnic

Flavius Marcau

Educational Research Review

Sofie Cabus

Irina Ilisei

Cuthbert Tukundane , Alexander Minnaert , Jacques Zeelen , Peter Kanyandago

Fondazione Hallgarten - Franchetti Centro Studi Villa Montesca

Intellectual and Developmental Disabilities

Courtney Wilt , Mary E . Morningstar

Journal of Applied Technical and Educational Sciences

Csilla Marianna Szabó

Vitomir Jovanovic

Alina Preda

Raphael Cohen-almagor

Daniel Caro

Kate Sirota

IOSR Journal Of Humanities And Social Science (IOSR-JHSS)

Esayas Teshome

Hanna Tomaszewska

Omar Ihssane Nouairi

RELATED TOPICS

•   We're Hiring!
•   Help Center
• Find new research papers in:
• Health Sciences
• Earth Sciences
• Cognitive Science
• Mathematics
• Computer Science
• Academia ©2024