MACHINE LEARNING-BASED DROPOUT PREDICTION FOR UNDERGRADUATES
DOI:
https://doi.org/10.29121/shodhkosh.v5.i5.2024.4551Keywords:
Student Dropout Prediction, Machine Learning, Educational Data Mining, Student Retention, Predictive AnalyticsAbstract [English]
Increasing rates of undergraduate dropout pose a danger to the credibility, financial stability, and future opportunities of higher education institutions. To address this critical issue, our study use machine learning to predict which students would withdraw from a course. Factors influencing student retention include socioeconomic status, degree of participation, and academic performance, according to our examination of institutional records and surveys. The research constructs prediction models by using neural networks, decision trees, random forests, and logistic regression. The accuracy, precision, recall, F1 score, and ROC-AUC are evaluated for these models, while the robustness and reliability are tested using cross-validation. Our study shows that student dropouts may be predicted by looking at academic indicators, social factors, and engagement metrics. The most effective strategy is providing schools with individualized interventions to boost retention rates. Educational data mining and predictive analytics are both advanced by this research, which offers administrators and legislators options to reduce dropout rates. This study adds to the growing body of evidence that machine learning algorithms have the potential to aid in the early detection and prompt intervention of children at risk. Despite its useful findings, the study acknowledges the limitations of its data collection methods and calls for more investigation into how to improve prediction models. It is possible that future studies may use more diverse datasets and more robust machine learning techniques to enhance the accuracy of predictions. As this research demonstrates, machine learning has the potential to revolutionize the educational system by opening the door to data-driven solutions that boost both student success and school resilience.
References
M. Segura, J. Mello, and A. Hernández, “Machine Learning Prediction of University Student Dropout: Does Preference Play a Key Role?,” Mathematics, vol. 10, no. 18, Sep. 2022, doi: 10.3390/math10183359. DOI: https://doi.org/10.3390/math10183359
D. Opazo, S. Moreno, E. Álvarez-Miranda, and J. Pereira, “Analysis of first-year university student dropout through machine learning models: A comparison between universities,” Mathematics, vol. 9, no. 20, Oct. 2021, doi: 10.3390/math9202599. DOI: https://doi.org/10.3390/math9202599
M. Vaarma and H. Li, “Predicting student dropouts with machine learning: An empirical study in Finnish higher education,” Technol Soc, vol. 76, Mar. 2024, doi: 10.1016/j.techsoc.2024.102474. DOI: https://doi.org/10.1016/j.techsoc.2024.102474
D. Delen, B. Davazdahemami, and E. Rasouli Dezfouli, “Predicting and Mitigating Freshmen Student Attrition: A Local-Explainable Machine Learning Framework,” Information Systems Frontiers, vol. 26, no. 2, pp. 641–662, Apr. 2024, doi: 10.1007/s10796-023-10397-3. DOI: https://doi.org/10.1007/s10796-023-10397-3
F. Dalipi, A. S. Imran, and Z. Kastrati, “MOOC dropout prediction using machine learning techniques: Review and research challenges,” in IEEE Global Engineering Education Conference, EDUCON, IEEE Computer Society, May 2018, pp. 1007–1014. doi: 10.1109/EDUCON.2018.8363340. DOI: https://doi.org/10.1109/EDUCON.2018.8363340
F. Del Bonifro, M. Gabbrielli, G. Lisanti, and S. P. Zingaro, “Student dropout prediction,” in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Springer, 2020, pp. 129–140. doi: 10.1007/978-3-030-52237-7_11. DOI: https://doi.org/10.1007/978-3-030-52237-7_11
B. Albreiki, N. Zaki, and H. Alashwal, “A systematic literature review of student’ performance prediction using machine learning techniques,” Educ Sci (Basel), vol. 11, no. 9, Sep. 2021, doi: 10.3390/educsci11090552. DOI: https://doi.org/10.3390/educsci11090552
S. B. Kotsiantis, C. J. Pierrakeas, and P. E. Pintelas, “Preventing student dropout in distance learning using machine learning techniques,” in Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science), Springer Verlag, 2003, pp. 267–274. doi: 10.1007/978-3-540-45226-3_37. DOI: https://doi.org/10.1007/978-3-540-45226-3_37
I. Lykourentzou, I. Giannoukos, V. Nikolopoulos, G. Mpardis, and V. Loumos, “Dropout prediction in e-learning courses through the combination of machine learning techniques,” ComputEduc, vol. 53, no. 3, pp. 950–965, Nov. 2009, doi: 10.1016/j.compedu.2009.05.010. DOI: https://doi.org/10.1016/j.compedu.2009.05.010
L. R. Pelima, Y. Sukmana, and Y. Rosmansyah, “Predicting University Student Graduation Using Academic Performance and Machine Learning: A Systematic Literature Review,” IEEE Access, vol. 12, pp. 23451–23465, 2024, doi: 10.1109/ACCESS.2024.3361479. DOI: https://doi.org/10.1109/ACCESS.2024.3361479
D. K. Dake and C. Buabeng-Andoh, “Using Machine Learning Techniques to Predict Learner Drop-out Rate in Higher Educational Institutions,” Mobile Information Systems, vol. 2022, 2022, doi: 10.1155/2022/2670562. DOI: https://doi.org/10.1155/2022/2670562
M. Delogu, R. Lagravinese, D. Paolini, and G. Resce, “Predicting dropout from higher education: Evidence from Italy,” Econ Model, vol. 130, Jan. 2024, doi: 10.1016/j.econmod.2023.106583. DOI: https://doi.org/10.1016/j.econmod.2023.106583
L. Aulck, N. Velagapudi, J. Blumenstock, and J. West, “Predicting Student Dropout in Higher Education,” Jun. 2016, [Online]. Available: http://arxiv.org/abs/1606.06364
S. Lakshmi and C. P. Maheswaran, “Effective deep learning based grade prediction system using gated recurrent unit (GRU) with feature optimization using analysis of variance (ANOVA),” Automatika, vol. 65, no. 2, pp. 425–440, 2024, doi: 10.1080/00051144.2023.2296790. DOI: https://doi.org/10.1080/00051144.2023.2296790
L. Vives et al., “Date of publication xxxx 00, 0000, date of current version xxxx 00, 0000. Prediction of Students’ Academic Performance in the Programming Fundamentals Course Using Long Short-Term Memory Neural Networks”, doi: 10.1109/ACCESS.2017.DOI.
L. H. Baniata, S. Kang, M. A. Alsharaiah, and M. H. Baniata, “Advanced Deep Learning Model for Predicting the Academic Performances of Students in Educational Institutions,” Applied Sciences, vol. 14, no. 5, p. 1963, Feb. 2024, doi: 10.3390/app14051963. DOI: https://doi.org/10.3390/app14051963
R. N. R, R. S. Mathusoothana Kumar, and B. C. L, “Date of publication xxxx 00, 0000, date of current version xxxx 00, 0000. Explainable Machine Learning Prediction for the Academic Performance of Deaf Scholars”, doi: 10.1109/ACCESS.2017.DOI.
K. Okoye, J. T. Nganji, J. Escamilla, and S. Hosseini, “Machine learning model (RG-DMML) and ensemble algorithm for prediction of students’ retention and graduation in education,” Computers and Education: Artificial Intelligence, vol. 6, Jun. 2024, doi: 10.1016/j.caeai.2024.100205. DOI: https://doi.org/10.1016/j.caeai.2024.100205
L. S. Maurya, M. S. Hussain, and S. Singh, “Developing Classifiers through Machine Learning Algorithms for Student Placement Prediction Based on Academic Performance,” Applied Artificial Intelligence, vol. 35, no. 6, pp. 403–420, 2021, doi: 10.1080/08839514.2021.1901032. DOI: https://doi.org/10.1080/08839514.2021.1901032
Y. Wang, L. Yang, J. Wu, Z. Song, and L. Shi, “Mining Campus Big Data: Prediction of Career Choice Using Interpretable Machine Learning Method,” Mathematics, vol. 10, no. 8, Apr. 2022, doi: 10.3390/math10081289. DOI: https://doi.org/10.3390/math10081289
D. Buenaño-Fernández, D. Gil, and S. Luján-Mora, “Application of machine learning in predicting performance for computer engineering students: A case study,” Sustainability (Switzerland), vol. 11, no. 10, May 2019, doi: 10.3390/su11102833. DOI: https://doi.org/10.3390/su11102833
C. Verma, Z. Illés, and D. Kumar, “An investigation of novel features for predicting student happiness in hybrid learning platforms – An exploration using experiments on trace data,” International Journal of Information Management Data Insights, vol. 4, no. 1, Apr. 2024, doi: 10.1016/j.jjimei.2024.100219. DOI: https://doi.org/10.1016/j.jjimei.2024.100219
W. Wang, “Application of deep learning algorithm in detecting and analyzing classroom behavior of art teaching,” Systems and Soft Computing, vol. 6, Dec. 2024, doi: 10.1016/j.sasc.2024.200082. DOI: https://doi.org/10.1016/j.sasc.2024.200082
W. Forero-Corba and F. N. Bennasar, “Techniques and applications of Machine Learning and Artificial Intelligence in education: a systematic review,” RIED-RevistaIberoamericana de Educacion a Distancia, vol. 27, no. 1, pp. 209–253, Jan. 2024, doi: 10.5944/ried.27.1.37491. DOI: https://doi.org/10.5944/ried.27.1.37491
D. Musleh et al., “Machine Learning Approaches for Predicting Risk of Cardiometabolic Disease among University Students,” Big Data and Cognitive Computing, vol. 8, no. 3, Mar. 2024, doi: 10.3390/bdcc8030031. DOI: https://doi.org/10.3390/bdcc8030031
M. Ouahi, S. Khoulji, and M. L. Kerkeb, “Analysis of Deep Learning Development Platforms and Their Applications in Sustainable Development within the Education Sector,” in E3S Web of Conferences, EDP Sciences, Jan. 2024. doi: 10.1051/e3sconf/202447700098. DOI: https://doi.org/10.1051/e3sconf/202447700098
G. Ibarra-Vazquez, M. S. Ramí¬rez-Montoya, and H. Terashima, “Gender prediction based on University students’ complex thinking competency: An analysis from machine learning approaches,” Educ Inf Technol (Dordr), vol. 29, no. 3, pp. 2721–2739, Feb. 2024, doi: 10.1007/s10639-023-11831-4. DOI: https://doi.org/10.1007/s10639-023-11831-4
J. A. Idowu, “Debiasing Education Algorithms,” Int J ArtifIntellEduc, 2024, doi: 10.1007/s40593-023-00389-4. DOI: https://doi.org/10.1007/s40593-023-00389-4
A. Villar and C. R. V. de Andrade, “Supervised machine learning algorithms for predicting student dropout and academic success: a comparative study,” Discover Artificial Intelligence, vol. 4, no. 1, Jan. 2024, doi: 10.1007/s44163-023-00079-z. DOI: https://doi.org/10.1007/s44163-023-00079-z
S. Ramos-Pulido, N. Hernández-Gress, and G. Torres-Delgado, “Exploring the Relationship between Career Satisfaction and University Learning Using Data Science Models,” Informatics, vol. 11, no. 1, Mar. 2024, doi: 10.3390/informatics11010006. DOI: https://doi.org/10.3390/informatics11010006
A. A. Imianvanet al., “Enhancing Job Recruitment Prediction through Supervised Learning and Structured Intelligent System: A Data Analytics Approach,” Journal of Advances in Mathematics and Computer Science, vol. 39, no. 2, pp. 72–88, Feb. 2024, doi: 10.9734/jamcs/2024/v39i21869. DOI: https://doi.org/10.9734/jamcs/2024/v39i21869
T. Revandi and H. Gunawan, “JURNAL MEDIA INFORMATIKA BUDIDARMA Classification of Company Level Based on Student Competencies in Tracer Study 2022 using SVM and XGBoost Method,” 2024, doi: 10.30865/mib.v8i1.7237. DOI: https://doi.org/10.30865/mib.v8i1.7237
C. Grace and M. Garces, “International Journal of INTELLIGENT SYSTEMS AND APPLICATIONS IN ENGINEERING A AI based Model for Achieving High Reliability Faculty Performance Using Various Machine Learning Algorithms.” [Online]. Available: www.ijisae.org
“A_Reinforcement_Learning_Based_RecommendationSystem_to_Improve_Performance_of_Students_in_Outcome_Based_Education_Model”.
K. Sankara Narayanan and A. Kumaravel, “International Journal of INTELLIGENT SYSTEMS AND APPLICATIONS IN ENGINEERING A Novel Chaotic Optimized Boost Long Short-Term Memory (COB-LSTM) Model for Students Academic Performance Prediction in Educational Sectors.” [Online]. Available: www.ijisae.org
Z. Ziyi, “Application of neural network algorithm based on sensor networks in performance evaluation simulation of rural teachers,” Measurement: Sensors, vol. 32, Apr. 2024, doi: 10.1016/j.measen.2024.101049 DOI: https://doi.org/10.1016/j.measen.2024.101049
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