Mitigating Academic Institution Dropout Rates with Predictive Analytics Algorithms
DOI:
https://doi.org/10.47747/ijets.v3i1.866Keywords:
Predictive Analytics Algorithms, Mitigating Dropout Rates, Classifiers, Learning Institutions, North AmericaAbstract
Multiple studies show student dropout rates across rich, low and middle-income countries. With the development of machine learning, various learning institutions have initiated predictive analytics algorithms to solve this issue. However, there is limited knowledge on how this strategy has been employed to optimize retention rates. The research aims to comprehensively review PAAs Strategies and demonstrate how they have been effectively implemented in optimizing retention rates with a case study focusing on North America. The study employed a quantitative research methodology and thematic study design to review the existing literature. The result shows that learning institutions used a combination of data mining classifiers such as k-Nearest Neighbor, Neural Networks, Decision Tree, and Naive Bayes to categorize student dropout rates and optimize retention rates. The PAAs approach has been utilized by various educational institutions across North America, applying multiple predicting algorithms to ensure efficiency.
Keywords: Predictive analytics algorithms, mitigating dropout rates, retention rates, classifiers, Learning institutions, North America
References
Agrusti, F., Bonavolontà, G., & Mezzini, M. (2019). University dropout prediction through educational data mining techniques: A systematic review. Journal of e-learning and knowledge society, 15(3), 161-182. https://doi.org/10.20368/1971-8829/1135017
Alban, M., & Mauricio, D. (2019). Neural networks to predict dropout at universities. International Journal of Machine Learning and Computing, 9(2), 149-153. https://web.archive.org/web/20200307075313id_/http://www.ijmlc.org/vol9/779-ML0074.pdf
Aldowah, H., Al-Samarraie, H., Alzahrani, A. I., & Alalwan, N. (2020). Factors affecting student dropout in MOOCs: a cause and effect decision‐making model. Journal of Computing in Higher Education, 32(2), 429-454. https://doi.org/10.1007/s12528-019-09241-y
Ba'abbad, I., Althubiti, T., Alharbi, A., Alfarsi, K., & Rasheed, S. (2021). A Short Review of Classification Algorithms Accuracy for Data Prediction in Data Mining Applications. Journal of Data Analysis and Information Processing, 9(3), 162-174. https://doi.org/10.4236/jdaip.2021.93011
Baker, R. S., Berning, A. W., Gowda, S. M., Zhang, S., & Hawn, A. (2020). Predicting K-12 dropout. Journal of Education for Students Placed at Risk (JESPER), 25(1), 28-54. https://doi.org/10.1080/10824669.2019.1670065
Balkis, M. (2018). Academic motivation and intention to school dropout: the mediation role of academic achievement and absenteeism. Asia Pacific Journal of Education, 38(2), 257-270. https://doi.org/10.1080/02188791.2018.1460258
Barshay, J., & Aslanian, S. (2019). Under a watchful eye: Colleges use big data to track students to boost graduation rates, but it comes at a cost (APM Reports). https://www. apmreports.org/story/2019/08/06/college-data-tracking-students-graduation
Becker, M. A. S., Schelbe, L., Romano, K., & Spinelli, C. (2017). Promoting first-generation college students' mental well-being: Student perceptions of an academic enrichment program. Journal of College Student Development, 58(8), 1166-1183. https://doi.org/10.1353/csd.2017.0092
Berrar, D. (2019). Cross-Validation. https://doi.org/10.1016/B978-0-12-809633-8.20349-X
Bird, Castleman and Mable (2021) Bringing Transparency to Predictive Analytics: A Systematic Comparison http://journals.sagepub.com January-December 2021, Vol. 7, No. 1, pp. 1-19 DOI: 10.1177/23328584211037630
Blazer, C., & Gonzalez Hernandez, V. (2018). Student Dropout: Risk Factors, Impact of Prevention Programs, and Effective Strategies. Research Brief. Volume 1708. Research Services, Miami-Dade County Public Schools. http://files.eric.ed.gov/fulltext/ED587683.pdf
Boyaci, A. (2019). Exploring the Factors Associated with the School Dropout. International Electronic Journal of Elementary Education, 12(2), 145-156. https://www.iejee.com/index.php/IEJEE/index
Carlson, S. E. (2018). Identifying students at risk of dropping out: indicators and thresholds using ROC analysis. https://digitalcommons.georgefox.edu/edd/114/
Coussement, K., Phan, M., De Caigny, A., Benoit, D. F., & Raes, A. (2020). Predicting student dropout in subscription-based online learning environments: The beneficial impact of the logit leaf model. Decision Support Systems, 135, 113325. https://doi.org/10.1016/j.dss.2020.113325
Dalipi, F., Imran, A. S., & Kastrati, Z. (2018, April). MOOC dropout prediction using machine learning techniques: Review and research challenges. In 2018 IEEE global engineering education conference (EDUCON) (pp. 1007-1014). IEEE. https://doi.org/10.1109/EDUCON.2018.8363340
Ekowo, M., & Palmer, I. (2016, October). The promise and peril of predictive analytics in higher education: A landscape analysis (New America Policy Paper). https://www.newamerica.org/ education-policy/policy-papers/promise-and-peril-predictive analytics-higher-education/
Evans, W. N., Kearney, M. S., Perry, B., & Sullivan, J. X. (2020). Increasing Community College Completion Rates Among Low‐Income Students: Evidence from a Randomized Controlled Trial Evaluation of a Case‐Management Intervention. Journal of Policy Analysis and Management, 39(4), 930-965. https://doi.org/10.1002/pam.22256
Faria, A. M., Sorensen, N., Heppen, J., Bowdon, J., Taylor, S., Eisner, R., & Foster, S. (2017). Getting Students on Track for Graduation: Impacts of the Early Warning Intervention and Monitoring System after One Year. REL 2017-272. Regional Educational Laboratory Midwest. http://files.eric.ed.gov/fulltext/ED573814.pdf
Gkontzis, A. F., Kotsiantis, S., Panagiotakopoulos, C. T., & Verykios, V. S. (2019). A predictive analytics framework as a countermeasure for attrition of students. Interactive Learning Environments, 1-16. https://doi.org/10.1080/10494820.2019.1709209
Gu, Z., Jamjoom, H., Su, D., Huang, H., Zhang, J., Ma, T., ... & Molloy, I. (2019, June). Reaching data confidentiality and model accountability on the Caltrain. In 2019 49th Annual IEEE/IFIP International Conference on Dependable Systems and Networks (DSN) (pp. 336-348). IEEE. https://doi.org/10.1109/DSN.2019.00044
Gubbels, J., van der Put, C. E., & Assink, M. (2019). Risk factors for school absenteeism and dropout: a meta-analytic review. Journal of youth and adolescence, 48(9), 1637-1667. https://doi.org/10.1007/s10964-019-01072-5
Hagedorn, L. S., Maxwell, W., & Hampton, P. R. E. S. T. O. N. (2019). Correlates of retention for African-American males in community colleges. In Minority Student Retention (pp. 7-27). Routledge. http://interwork.sdsu.edu/sp/m2c3/wp-content/blogs.dir/2/files/2012/10/Hagedorn-Maxwell-Hampton-2001-02.pdf
Hlioui, F., Aloui, N., & Gargouri, F. (2021). A Withdrawal Prediction Model of At-Risk Learners Based on Behavioural Indicators. International Journal of Web-Based Learning and Teaching Technologies (IJWLTT), 16(2), 32-53. https://www.igi-global.com/journal/international-journal-web-based-learning/1081
Hodges, J., McIntosh, J., & Gentry, M. (2017). The effect of an out-of-school enrichment program on the academic achievement of high-potential students from low-income families. Journal of Advanced Academics, 28(3), 204-224. https://doi.org/10.1177%2F1932202X17715304
Imran, A. S., Dalipi, F., & Kastrati, Z. (2019, April). Predicting student dropout in a MOOC: An evaluation of a deep neural network model. In Proceedings of the 2019 5th International Conference on Computing and Artificial Intelligence (pp. 190-195). https://doi.org/10.1145/3330482.3330514
Kabathova, J., & Drlik, M. (2021). Towards predicting student dropout in university courses using different machine learning techniques. Applied Sciences, 11(7), 3130. https://doi.org/10.3390/app11073130
Kemper, L., Vorhoff, G., & Wigger, B. U. (2020). Predicting student dropout: A machine learning approach. European Journal of Higher Education, 10(1), 28-47. https://doi.org/10.1080/21568235.2020.1718520
Ketija, D. (2018). Reasons behind children dropping out of primary schools with unequal socio-economic preconditions: A comparative case study of three primary schools in Babati Town Council, Tanzania. https://www.diva-portal.org/smash/get/diva2:1215119/FULLTEXT01.pdf
Ketija, D. (2018). Reasons behind children dropping out of primary schools with unequal socioeconomic preconditions: A comparative case study of three primary schools in Babati Town Council, Tanzania. https://www.diva-portal.org/smash/get/diva2:1215119/FULLTEXT01.pdf
Lee, S., & Chung, J. Y. (2019). The machine learning-based dropout early warning system improves the performance of dropout prediction. Applied Sciences, 9(15), 3093. https://doi.org/10.3390/app9153093
Lough, C. (2022, March 17). Degree course dropout rates fall to the lowest on record. Independent. https://www.independent.co.uk/news/uk/university-of-cambridge-university-of-oxford-russell-group-higher-education-statistics-agency-open-university-b2037939.html
Marôco, J., Assunção, H., Harju-Luukkainen, H., Lin, S. W., Sit, P. S., Cheung, K. C., ... & Campos, J. A. (2020). Predictors of academic efficacy and dropout intention in university students: Can engagement suppress burnout?. PloS One, 15(10), e0239816. https://doi.org/10.1371/journal.pone.0239816
Mason, C., Twomey, J., Wright, D., & Whitman, L. (2018). Predicting engineering student attrition risk using a probabilistic neural network and comparing results with a backpropagation neural network and logistic regression. Research in Higher Education, 59(3), 382-400. https://doi.org/10.1007/s11162-017-9473-z
McFarland, J., & Cui, J., Rathbun, A., & Holmes, J. (2020). Trends in High School Dropout and Completion Rates in the United States: 2018. Compendium Report. NCES 2019-117. National Center for Education Statistics.https://nces.ed.gov/pubs2020/2020117.pdf
McFarland, J., Cui, J., Rathbun, A., & Holmes, J. (2018). Trends in High School Dropout and Completion Rates in the United States: 2018. Compendium Report. NCES 2019-117. National Center for Education Statistics. https://nces.ed.gov/pubs2020/2020117.pdf
Millea, M., Wills, R., Elder, A., & Molina, D. (2018). What matters in college student success? Determinants of college retention and graduation rates. Education, 138(4), 309-322. https://www.ingentaconnect.com/content/prin/ed/2018/00000138/00000004/art00003
Mohamed, M. H., & Waguih, H. M. (2018). A proposed academic advisor model based on data mining classification techniques. International Journal of Advanced Computer Research, 8(36), 129-136. http://dx.doi.org/10.19101/IJACR.2018.836003
Nanayakkara, A. C., Kumara, B. T. G. S., & Rathnayaka, R. M. K. T. (2021). A Survey of Finding Trends in Data Mining Techniques for Social Media Analysis. Sri Lanka Journal of Social Sciences and Humanities, 1(2). http://doi.org/10.4038/sljssh.v1i2.36
NCES. (2021). Dropout Rates. National Center for Education Statistics. Fact Sheets. https://nces.ed.gov/fastfacts/display.asp?id=16
Rastrollo-Guerrero, J. L., Gómez-Pulido, J. A., & Durán-Domínguez, A. (2020). Analyzing and predicting students' performance using machine learning: A review. Applied sciences, 10(3), 1042. https://doi.org/10.3390/app10031042
Rienties, B., Cross, S., & Zdrahal, Z. (2017). Implementing a learning analytics intervention and evaluation framework: What works? Big data and learning analytics in higher education (pp. 147-166). Springer, Cham. https://doi.org/10.1007/978-3-319-06520-5_10
Sáiz-Manzanares, M. C., Marticorena-Sánchez, R., & García-Osorio, C. I. (2020). Monitoring students at the university: Design and application of a moodle plugin. Applied Sciences, 10(10), 3469. https://doi.org/10.3390/app10103469
Solichin, A. (2019, September). Comparison of Decision Tree, Naïve Bayes, and K-Nearest Neighbors for Predicting Thesis Graduation. In 2019 6th International Conference on Electrical Engineering, Computer Science and Informatics (EECSI) (pp. 217-222). IEEE. http://doi.org/10.23919/EECSI48112.2019.8977081
Taylor, R. D., Oberle, E., Durlak, J. A., & Weissberg, R. P. (2017). Promoting positive youth development through school‐based social and emotional learning interventions: A meta‐analysis of follow‐up effects. Child Development, 88(4), 1156-1171. https://doi.org/10.1111/cdev.12864
Treaster, J. B. (2017, February 2). Will you graduate? Ask big data. The New York Times. https://www.nytimes.com/2017/02/02/ education/edlife/will-you-graduate-ask-big-data.HTML?_r=1
UNESCO Institute for Statistics. (2022). UIS Glossary: Education. UNESCO UIS. https://uis.unesco.org/sites/default/files/documents/uis_glossary_education_20200921.pdf
Valkov, P. (2018). School dropout and substance use: Consequence or predictor. Trakia Journal of Sciences, 16(2), 95. https://pdfs.semanticscholar.org/7bee/306c6f4b286993be90f35151a80af1d12a9c.pdf
Wild, S., & Heuling, L. S. (2020). Student dropout and retention: An event history analysis among students in cooperative higher education. International Journal of Educational Research, 104, 101687. https://doi.org/10.1016/j.ijer.2020.101687
Wood, L., Kiperman, S., Esch, R. C., Leroux, A. J., & Truscott, S. D. (2017). Predicting dropout using student-and school-level factors: An ecological perspective. School Psychology Quarterly, 32(1), 35. https://psycnet.apa.org/record/2016-15743-001
Wu, Y. C., & Feng, J. W. (2018). Development and application of artificial neural networks. Wireless Personal Communications, 102(2), 1645-1656. https://doi.org/10.1007/s11277-017-5224-x
Xing, W., & Du, D. (2019). Dropout prediction in MOOCs: Using deep learning for personalized intervention. Journal of Educational Computing Research, 57(3), 547-570. https://doi.org/10.1177%2F0735633118757015
Yang, S. J., Lu, O. H., Huang, A. Y., Huang, J. C., Ogata, H., & Lin, A. J. (2018). Predicting students' academic performance using multiple linear regression and principal component analysis. Journal of Information Processing, 26, 170-176. https://doi.org/10.2197/ipsjjip.26.170
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