Workload Overload? Late Enrollment Leads to Course Dropout
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Published
Feb 21, 2025
Conrad Borchers
Yinuo Xu
Zachary A. Pardos
https://orcid.org/0000-0003-3437-8979
Abstract
Transaction data documenting student course selection during academic planning pose novel opportunities for educational data mining to understand and predict student outcomes. These fine-grain data of student waitlist, add, and drop actions can explain the decision-making processes that lead to mismanaged course selection (e.g., dropping a course late with an institutional penalty). The present study investigates student late drops through the lens of late course enrollment. We document first-of-its-kind empirical differences in student academic planning behavior and their resulting workload. We then leverage records of consecutive student semesters to study the causality of the association between late course enrollment and late drops. Specifically, we apply cross-lagged panel models as a novel methodological lens on large-scale enrollment histories to test the specific directionality of the association over time. Results suggest that late course enrollment (17%) and late dropping (10%) are associated with a higher workload compared to students who do not enroll or drop late. This workload could not be explained by students being forced to take high-workload courses at the end of the academic planning period but rather by students' own volition of taking more courses. Further, we find that students were more likely to drop courses with a high workload late, suggesting that students who drop late are unprepared for their workloads, especially if they enroll late and exhibit less regular course planning activity. Our results align with a causal hypothesis for the link between delayed enrollment and late course dropping, estimating the effect to be about 0.8 SDs of more course drops per SD of late course enrollment. This link is slightly stronger for students who enroll in courses with a higher course load and is stable across years of study. There was no reinforcement of late course dropping and delayed enrollment, bolstering support for a causal hypothesis. We discuss the implications of our findings for academic advising, including potential interventions targeting early planning and academic preparedness for course workload to mitigate over-enrollment related to preference uncertainty during delayed decision-making. All data analysis code used for this study is publicly available (https://github.com/CAHLR/enrollment-procrastination-edm).
How to Cite
Borchers, C., Xu, Y., & Pardos, Z. A. (2025). Workload Overload? Late Enrollment Leads to Course Dropout. Journal of Educational Data Mining, 17(1), 126–156. https://doi.org/10.5281/zenodo.14907388
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Keywords
higher education, enrollment data, course transactions, course shopping, dropout, workload, cross-lagged panel models
References
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Bhattacharjee, A., Zeng, Y., Xu, S. Y., Kulzhabayeva, D., Ma, M., Kornfield, R., Ahmed, S. I., Mariakakis, A., Czerwinski, M. P., Kuzminykh, A., et al. 2024. Understanding the role of large language models in personalizing and scaffolding strategies to combat academic procrastination. In Proceedings of the CHI Conference on Human Factors in Computing Systems. 1–18.
Borchers, C. and Pardos, Z. A. 2023. Insights into undergraduate pathways using course load analytics. In LAK23: 13th International Learning Analytics and Knowledge Conference. 219–229.
Borchers, C. and Pardos, Z. A. under review. Course load analytics interventions on higher education course selection: Experimental evidence. Manuscript under review in Journal of Learning Analytics.
Borchers, C., Xu, Y., and Pardos, Z. A. 2024. Are you an early dropper or late shopper? Mining enrollment transaction data to study procrastination in higher education. In Proceedings of the 17th International Conference on Educational Data Mining, B. Paaßen and C. D. Epp, Eds. International Educational Data Mining Society, Atlanta, Georgia, USA, 426–433.
Botelho, A. F., Closser, A. H., Sales, A. C., Heffernan, N. T., and Vanacore, K. P. 2024. Causal inference in educational data mining.
Boyce, C. J., Wood, A. M., and Ferguson, E. 2016. Individual differences in loss aversion: Conscientiousness predicts how life satisfaction responds to losses versus gains in income. Personality and Social Psychology Bulletin 42, 4, 471–484.
Bryk, A. S. and Raudenbush, S. W. 1992. Hierarchical Linear Models: Applications and Data Analysis Methods. Sage Publications, Inc.
Chaturapruek, S., Dee, T. S., Johari, R., Kizilcec, R. F., and Stevens, M. L. 2018. How a data-driven course planning tool affects college students’ GPA: evidence from two field experiments. In Proceedings of the Fifth Annual ACM Conference on Learning at Scale. 1–10.
Chernev, A., Böckenholt, U., and Goodman, J. 2015. Choice overload: A conceptual review and meta-analysis. Journal of Consumer Psychology 25, 2, 333–358.
Cormack, S. H., Eagle, L. A., and Davies, M. S. 2020. A large-scale test of the relationship between procrastination and performance using learning analytics. Assessment & Evaluation in Higher Education 45, 7, 1046–1059.
Cox Jr, L. A. 2018. Modernizing the Bradford Hill criteria for assessing causal relationships in observational data. Critical Reviews in Toxicology 48, 8, 682–712.
De Paola, M. and Scoppa, V. 2015. Procrastination, academic success and the effectiveness of a remedial program. Journal of Economic Behavior & Organization 115, 217–236.
Duckworth, A. L., Tsukayama, E., and May, H. 2010. Establishing causality using longitudinal hierarchical linear modeling: An illustration predicting achievement from self-control. Social Psychological and Personality Science 1, 4, 311–317.
Fellner, G. and Sutter, M. 2009. Causes, consequences, and cures of myopic loss aversion–an experimental investigation. The Economic Journal 119, 537, 900–916.
Fischer, C., Pardos, Z. A., Baker, R. S., Williams, J. J., Smyth, P., Yu, R., Slater, S., Baker, R., and Warschauer, M. 2020. Mining big data in education: Affordances and challenges. Review of Research in Education 44, 1, 130–160.
Gal, D. and Rucker, D. D. 2018. The loss of loss aversion: Will it loom larger than its gain? Journal of Consumer Psychology 28, 3, 497–516.
Gerace, A. 2020. Internal and external attributions. In Encyclopedia of Personality and Individual Differences, V. Zeigler-Hill and T. K. Shackelford, Eds. Springer International Publishing, Cham, 2328–2334.
Hagedorn, L. S., Maxwell, W. E., Cypers, S., Moon, H. S., and Lester, J. 2007. Course shopping in urban community colleges: An analysis of student drop and add activities. The Journal of Higher Education 78, 4, 464–485.
Hamaker, E. L., Kuiper, R. M., and Grasman, R. P. 2015. A critique of the cross-lagged panel model. Psychological Methods 20, 1, 102.
Hen, M. and Goroshit, M. 2014. Academic self-efficacy, emotional intelligence, GPA and academic procrastination in higher education. Eurasian Journal of Social Sciences 2, 1, 1–10.
Huntington-Klein, N. and Gill, A. 2021. Semester course load and student performance. Research in Higher Education 62, 5, 623–650.
Ilies, R., Dimotakis, N., and De Pater, I. E. 2010. Psychological and physiological reactions to high workloads: Implications for well-being. Personnel Psychology 63, 2, 407–436.
Jiang, W. and Pardos, Z. A. 2020. Evaluating sources of course information and models of representation on a variety of institutional prediction tasks. Proceedings of the 13th International Conference on Educational Data Mining (EDM), 115–125.
Jiang, W., Pardos, Z. A., and Wei, Q. 2019. Goal-based course recommendation. In Proceedings of the 9th International Conference on Learning Analytics & Knowledge. 36–45.
Karimi-Haghighi, M., Castillo, C., and Hernández-Leo, D. 2022. A causal inference study on the effects of first year workload on the dropout rate of undergraduates. In International Conference on Artificial Intelligence in Education. Springer, 15–27.
Kim, K. R. and Seo, E. H. 2015. The relationship between procrastination and academic performance: A meta-analysis. Personality and Individual Differences 82, 26–33.
Lay, C. H. and Schouwenburg, H. C. 1993. Trait procrastination, time management. Journal of Social Behavior and Personality 8, 4, 647–662.
Lenchner, E. 2017. Mining Transactional Student-Level Data to Predict Community College Student Outcomes. Columbia University.
Li, J., Chai, L., Nordstrom, O., Tangpong, C., and Hung, K.-T. 2021. Development of a loss aversion scale. Journal of Managerial Issues 33, 1.
Lippke, S., Pomp, S., and Fleig, L. 2018. Rehabilitants’ conscientiousness as a moderator of the intention–planning-behavior chain. Rehabilitation Psychology 63, 3, 460.
Mackinnon, S. P. 2012. Perceived social support and academic achievement: Cross-lagged panel and bivariate growth curve analyses. Journal of Youth and Adolescence 41, 474–485.
McKinney, L., Novak, H., Hagedorn, L. S., and Luna-Torres, M. 2019. Giving up on a course: An analysis of course dropping behaviors among community college students. Research in Higher Education 60, 184–202.
Méndez, G. G., Galárraga, L., Chiluiza, K., and Mendoza, P. 2023. Impressions and strategies of academic advisors when using a grade prediction tool during term planning. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems. 1–18.
Mulder, J. D. and Hamaker, E. L. 2021. Three extensions of the random intercept cross-lagged panel model. Structural Equation Modeling: A Multidisciplinary Journal 28, 4, 638–648.
Paas, F., Van Gog, T., and Sweller, J. 2010. Cognitive load theory: New conceptualizations, specifications, and integrated research perspectives. Educational Psychology Review 22, 2, 115–121.
Pardos, Z. A., Borchers, C., and Yu, R. 2023. Credit hours is not enough: Explaining undergraduate perceptions of course workload using lms records. The Internet and Higher Education 56, 100882.
Pardos, Z. A., Fan, Z., and Jiang, W. 2019. Connectionist recommendation in the wild: on the utility and scrutability of neural networks for personalized course guidance. User Modeling and User-adapted Interaction 29, 487–525.
Pardos, Z. A. and Nam, A. J. H. 2020. A university map of course knowledge. PloS one 15, 9, e0233207.
R Core Team. 2024. R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria.
Reid, G. B. and Nygren, T. E. 1988. The subjective workload assessment technique: A scaling procedure for measuring mental workload. In Advances in Psychology. Vol. 52. Elsevier, 185–218.
Reuben, E., Sapienza, P., and Zingales, L. 2015. Procrastination and impatience. Journal of Behavioral and Experimental Economics 58, 63–76.
Rosseel, Y. 2012. lavaan: An R package for structural equation modeling. Journal of Statistical Software 48, 1–36.
Sabnis, S., Yu, R., and Kizilcec, R. F. 2022. Large-scale student data reveal sociodemographic gaps in procrastination behavior. In Proceedings of the Ninth ACM Conference on Learning@ Scale. 133–141.
Salazar-Fernandez, J. P., Sepúlveda, M., Munoz-Gama, J., and Nussbaum, M. 2021. Curricular analytics to characterize educational trajectories in high-failure rate courses that lead to late dropout. Applied Sciences 11, 4, 1436.
Salguero-Pazos, M. R. and Reyes-de Cózar, S. 2023. Interventions to reduce academic procrastination: A systematic review. International Journal of Educational Research 121, 102228.
Schouwenburg, H. C. 2004. Academic procrastination: Theoretical notions, measurement, and research. In Counseling the Procrastinator in Academic Settings, H. C. Schouwenburg, C. H. Lay, T. A. Pychyl, and J. R. Ferrari, Eds. American Psychological Association, Washington, DC, 3–17.
Schunk, D. H. 2023. Self-regulation of self-efficacy and attributions in academic settings. In Self-regulation of Learning and Performance. Routledge, 75–99.
Schwarz, G. 1978. Estimating the dimension of a model. The Annals of Statistics 6, 2, 461–464.
Scott, M. and Savage, D. A. 2022. Lemons in the university: Asymmetric information, academic shopping and subject selection. Higher Education Research & Development 41, 4, 1247–1261.
Scully, G. and Kerr, R. 2014. Student workload and assessment: Strategies to manage expectations and inform curriculum development. Accounting Education 23, 5, 443–466.
Seabold, S. and Perktold, J. 2010. Statsmodels: Econometric and Statistical Modeling with Python. In Proceedings of the 9th Python in Science Conference, Stéfan van der Walt and Jarrod Millman, Eds. 92 – 96.
Selig, J. P. and Little, T. D. 2012. Autoregressive and cross-lagged panel analysis for longitudinal data. In Handbook of Developmental Research Methods, B. Laursen, T. D. Little, and N. A. Card, Eds. The Guilford Press, 265–278.
Shao, E., Guo, S., and Pardos, Z. A. 2021. Degree planning with plan-bert: Multi-semester recommendation using future courses of interest. In Proceedings of the AAAI Conference on Artificial Intelligence. Vol. 35. 14920–14929.
Shedd, J. M. 2003. The history of the student credit hour. New Directions for Higher Education 2003, 122, 5–12.
Smith, A. P. 2019. Student workload, wellbeing and academic attainment. In International Symposium on Human Mental Workload: Models and Applications. Springer, 35–47.
Stavrinides, P. and Zuev, K. M. 2023. Course-prerequisite networks for analyzing and understanding academic curricula. Applied Network Science 8, 1, 19.
Steel, P. 2007. The nature of procrastination: A meta-analytic and theoretical review of quintessential self-regulatory failure. Psychological Bulletin 133, 1, 65.
Wang, X., Yu, X., Guo, L., Liu, F., and Xu, L. 2020. Student performance prediction with short-term sequential campus behaviors. Information 11, 4, 201.
Wäschle, K., Allgaier, A., Lachner, A., Fink, S., and Nückles, M. 2014. Procrastination and self-efficacy: Tracing vicious and virtuous circles in self-regulated learning. Learning and Instruction 29, 103–114.
Xu, Y. and Pardos, Z. A. 2023. Mining detailed course transaction records for semantic information. In Proceedings of the 16th International Conference on Educational Data Mining, M. Feng, T. Käser, and P. Talukdar, Eds. International Educational Data Mining Society, Bengaluru, India, 388–395.
You, J. W. 2015. Examining the effect of academic procrastination on achievement using LMS data in e-learning. Journal of Educational Technology & Society 18, 3, 64–74.
Zacks, S. and Hen, M. 2018. Academic interventions for academic procrastination: A review of the literature. Journal of Prevention & Intervention in the Community 46, 2, 117–130.
Bhattacharjee, A., Zeng, Y., Xu, S. Y., Kulzhabayeva, D., Ma, M., Kornfield, R., Ahmed, S. I., Mariakakis, A., Czerwinski, M. P., Kuzminykh, A., et al. 2024. Understanding the role of large language models in personalizing and scaffolding strategies to combat academic procrastination. In Proceedings of the CHI Conference on Human Factors in Computing Systems. 1–18.
Borchers, C. and Pardos, Z. A. 2023. Insights into undergraduate pathways using course load analytics. In LAK23: 13th International Learning Analytics and Knowledge Conference. 219–229.
Borchers, C. and Pardos, Z. A. under review. Course load analytics interventions on higher education course selection: Experimental evidence. Manuscript under review in Journal of Learning Analytics.
Borchers, C., Xu, Y., and Pardos, Z. A. 2024. Are you an early dropper or late shopper? Mining enrollment transaction data to study procrastination in higher education. In Proceedings of the 17th International Conference on Educational Data Mining, B. Paaßen and C. D. Epp, Eds. International Educational Data Mining Society, Atlanta, Georgia, USA, 426–433.
Botelho, A. F., Closser, A. H., Sales, A. C., Heffernan, N. T., and Vanacore, K. P. 2024. Causal inference in educational data mining.
Boyce, C. J., Wood, A. M., and Ferguson, E. 2016. Individual differences in loss aversion: Conscientiousness predicts how life satisfaction responds to losses versus gains in income. Personality and Social Psychology Bulletin 42, 4, 471–484.
Bryk, A. S. and Raudenbush, S. W. 1992. Hierarchical Linear Models: Applications and Data Analysis Methods. Sage Publications, Inc.
Chaturapruek, S., Dee, T. S., Johari, R., Kizilcec, R. F., and Stevens, M. L. 2018. How a data-driven course planning tool affects college students’ GPA: evidence from two field experiments. In Proceedings of the Fifth Annual ACM Conference on Learning at Scale. 1–10.
Chernev, A., Böckenholt, U., and Goodman, J. 2015. Choice overload: A conceptual review and meta-analysis. Journal of Consumer Psychology 25, 2, 333–358.
Cormack, S. H., Eagle, L. A., and Davies, M. S. 2020. A large-scale test of the relationship between procrastination and performance using learning analytics. Assessment & Evaluation in Higher Education 45, 7, 1046–1059.
Cox Jr, L. A. 2018. Modernizing the Bradford Hill criteria for assessing causal relationships in observational data. Critical Reviews in Toxicology 48, 8, 682–712.
De Paola, M. and Scoppa, V. 2015. Procrastination, academic success and the effectiveness of a remedial program. Journal of Economic Behavior & Organization 115, 217–236.
Duckworth, A. L., Tsukayama, E., and May, H. 2010. Establishing causality using longitudinal hierarchical linear modeling: An illustration predicting achievement from self-control. Social Psychological and Personality Science 1, 4, 311–317.
Fellner, G. and Sutter, M. 2009. Causes, consequences, and cures of myopic loss aversion–an experimental investigation. The Economic Journal 119, 537, 900–916.
Fischer, C., Pardos, Z. A., Baker, R. S., Williams, J. J., Smyth, P., Yu, R., Slater, S., Baker, R., and Warschauer, M. 2020. Mining big data in education: Affordances and challenges. Review of Research in Education 44, 1, 130–160.
Gal, D. and Rucker, D. D. 2018. The loss of loss aversion: Will it loom larger than its gain? Journal of Consumer Psychology 28, 3, 497–516.
Gerace, A. 2020. Internal and external attributions. In Encyclopedia of Personality and Individual Differences, V. Zeigler-Hill and T. K. Shackelford, Eds. Springer International Publishing, Cham, 2328–2334.
Hagedorn, L. S., Maxwell, W. E., Cypers, S., Moon, H. S., and Lester, J. 2007. Course shopping in urban community colleges: An analysis of student drop and add activities. The Journal of Higher Education 78, 4, 464–485.
Hamaker, E. L., Kuiper, R. M., and Grasman, R. P. 2015. A critique of the cross-lagged panel model. Psychological Methods 20, 1, 102.
Hen, M. and Goroshit, M. 2014. Academic self-efficacy, emotional intelligence, GPA and academic procrastination in higher education. Eurasian Journal of Social Sciences 2, 1, 1–10.
Huntington-Klein, N. and Gill, A. 2021. Semester course load and student performance. Research in Higher Education 62, 5, 623–650.
Ilies, R., Dimotakis, N., and De Pater, I. E. 2010. Psychological and physiological reactions to high workloads: Implications for well-being. Personnel Psychology 63, 2, 407–436.
Jiang, W. and Pardos, Z. A. 2020. Evaluating sources of course information and models of representation on a variety of institutional prediction tasks. Proceedings of the 13th International Conference on Educational Data Mining (EDM), 115–125.
Jiang, W., Pardos, Z. A., and Wei, Q. 2019. Goal-based course recommendation. In Proceedings of the 9th International Conference on Learning Analytics & Knowledge. 36–45.
Karimi-Haghighi, M., Castillo, C., and Hernández-Leo, D. 2022. A causal inference study on the effects of first year workload on the dropout rate of undergraduates. In International Conference on Artificial Intelligence in Education. Springer, 15–27.
Kim, K. R. and Seo, E. H. 2015. The relationship between procrastination and academic performance: A meta-analysis. Personality and Individual Differences 82, 26–33.
Lay, C. H. and Schouwenburg, H. C. 1993. Trait procrastination, time management. Journal of Social Behavior and Personality 8, 4, 647–662.
Lenchner, E. 2017. Mining Transactional Student-Level Data to Predict Community College Student Outcomes. Columbia University.
Li, J., Chai, L., Nordstrom, O., Tangpong, C., and Hung, K.-T. 2021. Development of a loss aversion scale. Journal of Managerial Issues 33, 1.
Lippke, S., Pomp, S., and Fleig, L. 2018. Rehabilitants’ conscientiousness as a moderator of the intention–planning-behavior chain. Rehabilitation Psychology 63, 3, 460.
Mackinnon, S. P. 2012. Perceived social support and academic achievement: Cross-lagged panel and bivariate growth curve analyses. Journal of Youth and Adolescence 41, 474–485.
McKinney, L., Novak, H., Hagedorn, L. S., and Luna-Torres, M. 2019. Giving up on a course: An analysis of course dropping behaviors among community college students. Research in Higher Education 60, 184–202.
Méndez, G. G., Galárraga, L., Chiluiza, K., and Mendoza, P. 2023. Impressions and strategies of academic advisors when using a grade prediction tool during term planning. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems. 1–18.
Mulder, J. D. and Hamaker, E. L. 2021. Three extensions of the random intercept cross-lagged panel model. Structural Equation Modeling: A Multidisciplinary Journal 28, 4, 638–648.
Paas, F., Van Gog, T., and Sweller, J. 2010. Cognitive load theory: New conceptualizations, specifications, and integrated research perspectives. Educational Psychology Review 22, 2, 115–121.
Pardos, Z. A., Borchers, C., and Yu, R. 2023. Credit hours is not enough: Explaining undergraduate perceptions of course workload using lms records. The Internet and Higher Education 56, 100882.
Pardos, Z. A., Fan, Z., and Jiang, W. 2019. Connectionist recommendation in the wild: on the utility and scrutability of neural networks for personalized course guidance. User Modeling and User-adapted Interaction 29, 487–525.
Pardos, Z. A. and Nam, A. J. H. 2020. A university map of course knowledge. PloS one 15, 9, e0233207.
R Core Team. 2024. R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria.
Reid, G. B. and Nygren, T. E. 1988. The subjective workload assessment technique: A scaling procedure for measuring mental workload. In Advances in Psychology. Vol. 52. Elsevier, 185–218.
Reuben, E., Sapienza, P., and Zingales, L. 2015. Procrastination and impatience. Journal of Behavioral and Experimental Economics 58, 63–76.
Rosseel, Y. 2012. lavaan: An R package for structural equation modeling. Journal of Statistical Software 48, 1–36.
Sabnis, S., Yu, R., and Kizilcec, R. F. 2022. Large-scale student data reveal sociodemographic gaps in procrastination behavior. In Proceedings of the Ninth ACM Conference on Learning@ Scale. 133–141.
Salazar-Fernandez, J. P., Sepúlveda, M., Munoz-Gama, J., and Nussbaum, M. 2021. Curricular analytics to characterize educational trajectories in high-failure rate courses that lead to late dropout. Applied Sciences 11, 4, 1436.
Salguero-Pazos, M. R. and Reyes-de Cózar, S. 2023. Interventions to reduce academic procrastination: A systematic review. International Journal of Educational Research 121, 102228.
Schouwenburg, H. C. 2004. Academic procrastination: Theoretical notions, measurement, and research. In Counseling the Procrastinator in Academic Settings, H. C. Schouwenburg, C. H. Lay, T. A. Pychyl, and J. R. Ferrari, Eds. American Psychological Association, Washington, DC, 3–17.
Schunk, D. H. 2023. Self-regulation of self-efficacy and attributions in academic settings. In Self-regulation of Learning and Performance. Routledge, 75–99.
Schwarz, G. 1978. Estimating the dimension of a model. The Annals of Statistics 6, 2, 461–464.
Scott, M. and Savage, D. A. 2022. Lemons in the university: Asymmetric information, academic shopping and subject selection. Higher Education Research & Development 41, 4, 1247–1261.
Scully, G. and Kerr, R. 2014. Student workload and assessment: Strategies to manage expectations and inform curriculum development. Accounting Education 23, 5, 443–466.
Seabold, S. and Perktold, J. 2010. Statsmodels: Econometric and Statistical Modeling with Python. In Proceedings of the 9th Python in Science Conference, Stéfan van der Walt and Jarrod Millman, Eds. 92 – 96.
Selig, J. P. and Little, T. D. 2012. Autoregressive and cross-lagged panel analysis for longitudinal data. In Handbook of Developmental Research Methods, B. Laursen, T. D. Little, and N. A. Card, Eds. The Guilford Press, 265–278.
Shao, E., Guo, S., and Pardos, Z. A. 2021. Degree planning with plan-bert: Multi-semester recommendation using future courses of interest. In Proceedings of the AAAI Conference on Artificial Intelligence. Vol. 35. 14920–14929.
Shedd, J. M. 2003. The history of the student credit hour. New Directions for Higher Education 2003, 122, 5–12.
Smith, A. P. 2019. Student workload, wellbeing and academic attainment. In International Symposium on Human Mental Workload: Models and Applications. Springer, 35–47.
Stavrinides, P. and Zuev, K. M. 2023. Course-prerequisite networks for analyzing and understanding academic curricula. Applied Network Science 8, 1, 19.
Steel, P. 2007. The nature of procrastination: A meta-analytic and theoretical review of quintessential self-regulatory failure. Psychological Bulletin 133, 1, 65.
Wang, X., Yu, X., Guo, L., Liu, F., and Xu, L. 2020. Student performance prediction with short-term sequential campus behaviors. Information 11, 4, 201.
Wäschle, K., Allgaier, A., Lachner, A., Fink, S., and Nückles, M. 2014. Procrastination and self-efficacy: Tracing vicious and virtuous circles in self-regulated learning. Learning and Instruction 29, 103–114.
Xu, Y. and Pardos, Z. A. 2023. Mining detailed course transaction records for semantic information. In Proceedings of the 16th International Conference on Educational Data Mining, M. Feng, T. Käser, and P. Talukdar, Eds. International Educational Data Mining Society, Bengaluru, India, 388–395.
You, J. W. 2015. Examining the effect of academic procrastination on achievement using LMS data in e-learning. Journal of Educational Technology & Society 18, 3, 64–74.
Zacks, S. and Hen, M. 2018. Academic interventions for academic procrastination: A review of the literature. Journal of Prevention & Intervention in the Community 46, 2, 117–130.
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