Predicción de repitencias en estudiantes a nivel escolar usando Machine Learning: una revisión sistemática
DOI:
https://doi.org/10.17162/au.v13i2.1438Palabras clave:
Métodos de predicción, Repitencia escolar, Machine learning, Deserción escolar, EducaciónResumen
El objetivo principal de la investigación es determinar el estado del arte de la investigación acerca de la Predicción de repitencias en estudiantes a nivel escolar usando Machine Learning. Los resultados obtenidos se han centrado en estudios relacionados a los algoritmos y herramientas de Machine Learning más eficientes para la predicción de repitencia estudiantil. Esto se llevó a cabo mediante una revisión sistemática de la literatura (RSL) en base a Machine Learning, para la predicción de estudiantes con repitencia escolar entre los años 2017-2021. La estrategia de búsqueda identificó 47,490 artículos de bibliotecas digitales como ACM Digital Library, ERIC, Google Scholar, IEEE Xplore, Microsoft Academic, Science Direct y Taylor & Francis Online; de las cuales 90 fueron identificados y seleccionados como adecuados para la revisión. En cuanto a las conclusiones, estas presentan respuestas sobre las categorías de variables más aplicadas en la predicción de repitencia escolar en estudiantes, las métricas utilizadas para evaluar los resultados de la predicción de repitencia escolar, autores con mayor productividad en la predicción de repitencia escolar, y los artículos más citados cuyas discusiones y conclusiones se caracterizan por su objetividad y polaridad en las investigaciones sobre la predicción de estudiantes con repitencia escolar usando Machine Learning.Descargas
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Abu Saa, A., Al-Emran, M., & Shaalan, K. (2019). Factors Affecting Students’ Performance in Higher Education: A Systematic Review of Predictive Data Mining Techniques. In Technology, Knowledge and Learning, 24 (4). Springer Netherlands. https://doi.org/10.1007/s10758-019-09408-7
Abu Zohair, L. M. (2019). Prediction of Student’s performance by modelling small dataset size. International Journal of Educational Technology in Higher Education, 16 (1). https://doi.org/10.1186/s41239-019-0160-3
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
Adnan, M., Habib, A., Ashraf, J., Mussadiq, S., Raza, A. A., Abid, M., Bashir, M., & Khan, S. U. (2021). Predicting at-Risk Students at Different Percentages of Course Length for Early Intervention Using Machine Learning Models. IEEE Access, 9, 7519–7539. https://doi.org/10.1109/ACCESS.2021.3049446
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). Predicting University Dropout trough Data Mining: A systematic Literature. Indian Journal of Science and Technology, 12 (4), 1–12. https://doi.org/10.17485/ijst/2019/v12i4/139729
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. https://doi.org/10.1109/ACCESS.2019.2943351
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, 25 (1), 28–54. https://doi.org/10.1080/10824669.2019.1670065
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. https://doi.org/10.2139/ssrn.3275433
Bertolini, R., Finch, S. J., & Nehm, R. H. (2021). Testing the Impact of Novel Assessment Sources and Machine Learning Methods on Predictive Outcome Modeling in Undergraduate Biology. Journal of Science Education and Technology, 30 (2), 193–209. https://doi.org/10.1007/s10956-020-09888-8
Borrella, I., Caballero-Caballero, S., & Ponce-Cueto, E. (2019). Predict and intervene: Addressing the dropout problem in a MOOC-based program. Proceedings of the 6th 2019 ACM Conference on Learning at Scale, L@S 2019. https://doi.org/10.1145/3330430.3333634
Botelho, A. F., Varatharaj, A., Patikorn, T., Doherty, Di., Adjei, S. A., & Beck, J. E. (2019). Developing Early Detectors of Student Attrition and Wheel Spinning Using Deep Learning. IEEE Transactions on Learning Technologies, 12 (2), 158–170. https://doi.org/10.1109/TLT.2019.2912162
Cagliero, L., Canale, L., Farinetti, L., Baralis, E., & Venuto, E. (2021). Predicting student academic performance by means of associative classification. Applied Sciences (Switzerland), 11 (4), 1–22. https://doi.org/10.3390/app11041420
Çam, E., & Özdağ, M. E. (2020). Discovery of Course Success Using Unsupervised Machine Learning Algorithms. Malaysian Online Journal of Educational Technology, 9 (1), 26–47. https://doi.org/10.17220/mojet.2021.9.1.242
Çetinkaya, A., & Baykan, Ö. K. (2020). Prediction of middle school students’ programming talent using artificial neural networks. Engineering Science and Technology, an International Journal, 23 (6), 1301–1307. https://doi.org/10.1016/j.jestch.2020.07.005
Chen, Y., & Zhang, M. (2017). MOOC student dropout: Pattern and prevention. ACM International Conference Proceeding Series, Part F1277. https://doi.org/10.1145/3063955.3063959
Chien, H., Kwok, O.-M., Yeh, Y.-C., Sweany, N. W., Baek, E., & McIntosh, W. A. (2020). Identifying At-Risk Online Learners by Psychological Variables Using Machine Learning Techniques. Online Learning, 24 (4), 131–146. https://doi.org/10.24059/olj.v24i4.2320
Chung, J. Y., & Lee, S. (2019). Dropout early warning systems for high school students using machine learning. Children and Youth Services Review, 96, 346–353. https://doi.org/10.1016/j.childyouth.2018.11.030
Cornell-Farrow, S., & Garrard, R. (2020). Machine learning classifiers do not improve the prediction of academic risk: Evidence from Australia. Communications in Statistics Case Studies Data Analysis and Applications, 6 (2), 228–246. https://doi.org/10.1080/23737484.2020.1752849
Corry, M., Dardick, W., & Stella, J. (2017). An examination of dropout rates for Hispanic or Latino students enrolled in online K-12 schools. Education and Information Technologies, 22 (5), 2001–2012. https://doi.org/10.1007/s10639-016-9530-9
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. https://doi.org/10.1016/j.chb.2017.01.047
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 (12), 113325. https://doi.org/10.1016/j.dss.2020.113325
de la Fuente-Mella, H., Gutiérrez, C. G., Crawford, K., Foschino, G., Crawford, B., Soto, R., de la Barra, C. L., Caneo, F. C., Monfroy, E., Becerra-Rozas, M., & Elórtegui-Gómez, C. (2020). Analysis and prediction of engineering student behavior and their relation to academic performance using data analytics techniques. Applied Sciences (Switzerland), 10 (20), 1–11. https://doi.org/10.3390/app10207114
De Melo, G., Vasconcelos-Filho, E. P., Oliveira, S. M., Calixto, W. P., Ferreira, C. C., & Furriel, G. P. (2017). Evaluation techniques of machine learning in task of reprovation prediction of technical high school students. 2017 CHILEAN Conference on Electrical, Electronics Engineering, Information and Communication Technologies, CHILECON 2017 - Proceedings, 2017-Janua (Ml), 1–7. https://doi.org/10.1109/CHILECON.2017.8229739
Del Bonifro, F., Gabbrielli, M., Lisanti, G., & Zingaro, S. P. (2020). Student dropout prediction. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 12163 LNAI, 129–140. https://doi.org/10.1007/978-3-030-52237-7_11
Do Nascimento, R. L. S., Fagundes, R. A. A., & MacIel, A. M. A. (2019). Prediction of school efficiency rates through ensemble regression application. Proceedings - IEEE 19th International Conference on Advanced Learning Technologies, ICALT 2019, 2161-377X (4), 194–198. https://doi.org/10.1109/ICALT.2019.00050
Ezz, M., & Elshenawy, A. (2020). Adaptive recommendation system using machine learning algorithms for predicting student’s best academic program. Education and Information Technologies, 25 (4), 2733–2746. https://doi.org/10.1007/s10639-019-10049-7
F. Gontzis, A., Kotsiantis, S., T. Panagiotakopoulos, C., & Verykios, V. S. (2022). A predictive analytics framework as a countermeasure for attrition of students. Interactive Learning Environments, 30 (3), 568–582. https://doi.org/10.1080/10494820.2019.1674884
Figueroa-Canas, J., & Sancho-Vinuesa, T. (2020). Early prediction of dropout and final exam performance in an online statistics course. Revista Iberoamericana de Tecnologias Del Aprendizaje, 15 (2), 86–94. https://doi.org/10.1109/RITA.2020.2987727
Freitas, F. A., Vasconcelos, F. F. X., Peixoto, S. A., Hassan, M. M., Ali Akber Dewan, M., de Albuquerque, V. H. C., & Rebouças Filho, P. P. (2020). IoT system for school dropout prediction using machine learning techniques based on socioeconomic data. Electronics (Switzerland), 9 (10), 1–14. https://doi.org/10.3390/electronics9101613
Gitinabard, N., Khoshnevisan, F., Lynch, C. F., & Wang, E. Y. (2018). Your actions or your associates? Predicting certification and dropout in MOOCs with behavioral and social features. Proceedings of the 11th International Conference on Educational Data Mining, EDM 2018.
Gkontzis, A. F., Kotsiantis, S., Tsoni, R., & Verykios, V. S. (2018). An effective LA approach to predict student achievement. ACM International Conference Proceeding Series, 76–81. https://doi.org/10.1145/3291533.3291551
Gómez-Pulido, J. A., Durán-Domínguez, A., & Pajuelo-Holguera, F. (2020). Optimizing latent factors and collaborative filtering for students’ performance prediction. Applied Sciences (Switzerland), 10 (16). https://doi.org/10.3390/app10165601
Goopio, J., & Cheung, C. (2021). The MOOC dropout phenomenon and retention strategies. Journal of Teaching in Travel and Tourism, 21 (2), 177–197. https://doi.org/10.1080/15313220.2020.1809050
Hai-tao, P., Ming-qu, F., Hong-bin, Z., Bi-zhen, Y., Jin-jiao, L., Chun-fang, L., Yan-ze, Z., & Rui, S. (2021). Predicting academic performance of students in Chinese-foreign cooperation in running schools with graph convolutional network. Neural Computing and Applications, 33 (2), 637–645. https://doi.org/10.1007/s00521-020-05045-9
Helal, S., Li, J., Liu, L., Ebrahimie, E., Dawson, S., & Murray, D. J. (2019). Identifying key factors of student academic performance by subgroup discovery. International Journal of Data Science and Analytics, 7 (3), 227–245. https://doi.org/10.1007/s41060-018-0141-y
Hellas, A., Ihantola, P., Petersen, A., Ajanovski, V. V., Gutica, M., Hynninen, T., Knutas, A., Leinonen, J., Messom, C., & Liao, S. N. (2018). Predicting academic performance: a systematic literature review. Proceedings Companion of the 23rd Annual ACM Conference on Innovation and Technology in Computer Science Education, 175–199. https://doi.org/10.1145/3293881.3295783
Herodotou, C., Hlosta, M., Boroowa, A., Rienties, B., Zdrahal, Z., & Mangafa, C. (2019). Empowering online teachers through predictive learning analytics. British Journal of Educational Technology, 50 (6), 3064–3079. https://doi.org/10.1111/bjet.12853
Hlosta, M., Zdrahal, Z., & Zendulka, J. (2017). Ouroboros: Early identification of at-risk students without models based on legacy data. ACM International Conference Proceeding Series, 6–15. https://doi.org/10.1145/3027385.3027449
Hmedna, B., El Mezouary, A., & Baz, O. (2020). A predictive model for the identification of learning styles in MOOC environments. Cluster Computing, 23 (2), 1303–1328. https://doi.org/10.1007/s10586-019-02992-4
Hoffait, A. S., & Schyns, M. (2017). Early detection of university students with potential difficulties. Decision Support Systems, 101, 1–11. https://doi.org/10.1016/j.dss.2017.05.003
Huang, A. Y. Q., Lu, O. H. T., Huang, J. C. H., Yin, C. J., & Yang, S. J. H. (2020). Predicting students’ academic performance by using educational big data and learning analytics: evaluation of classification methods and learning logs. Interactive Learning Environments, 28 (2), 206–230. https://doi.org/10.1080/10494820.2019.1636086
Huberts, L. C. E., Schoonhoven, M., & Does, R. J. M. M. (2020). Multilevel process monitoring: A case study to predict student success or failure. Journal of Quality Technology, 54 (2), 1–17. https://doi.org/10.1080/00224065.2020.1828008
Huo, H., Cui, J., Hein, S., Padgett, Z., Ossolinski, M., Raim, R., & Zhang, J. (2020). Predicting Dropout for Nontraditional Undergraduate Students: A Machine Learning Approach. Journal of College Student Retention: Research, Theory and Practice, 24 (4). https://doi.org/10.1177/1521025120963821
Iatrellis, O., Savvas, I., Fitsilis, P., & Gerogiannis, V. C. (2021). A two-phase machine learning approach for predicting student outcomes. Education and Information Technologies, 26 (1), 69–88. https://doi.org/10.1007/s10639-020-10260-x
Imran, A. S., Dalipi, F., & Kastrati, Z. (2019). Predicting Student Dropout in a MOOC. 190–195. https://doi.org/10.1145/3330482.3330514
Irfan, M., Alam, C. N., & Tresna, D. (2019). Implementation of Fuzzy Mamdani Logic Method for Student Drop Out Status Analytics. Journal of Physics: Conference Series, 1363 (1). https://doi.org/10.1088/1742-6596/1363/1/012056
Jin, C. (2020). MOOC student dropout prediction model based on learning behavior features and parameter optimization. Interactive Learning Environments, 1–19. https://doi.org/10.1080/10494820.2020.1802300
Jokhan, A., Sharma, B., & Singh, S. (2019). Early warning system as a predictor for student performance in higher education blended courses. Studies in Higher Education, 44 (11), 1900–1911. https://doi.org/10.1080/03075079.2018.1466872
Karimi-Haghighi, M., Castillo, C., Hernandez-Leo, D., & Oliver, V. M. (2021). Predicting Early Dropout: Calibration and Algorithmic Fairness Considerations. Companion Proceedings 11th International Conference on Learning Analytics & Knowledge, Ml, 1–10. https://arxiv.org/abs/2103.09068v1
Kartal, E., Özyaprak, M., Özen, Z., Şimşek, İ., Köse Biber, S., Biber, M., & Can, T. (2020). Bir Öğrenciyi Üstün Zekâlı ve Yetenekli Olarak Aday Göstermek İçin Doğru Soruları Sormak: Bir Makine Öğrenmesi Yaklaşımı. Bilişim Teknolojileri Dergisi, 13 (4), 385–400. https://doi.org/10.17671/gazibtd.591158
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
Kiss, B., Nagy, M., Molontay, R., & Csabay, B. (2019). Predicting dropout using high school and first-semester academic achievement measures. ICETA 2019 - 17th IEEE International Conference on Emerging ELearning Technologies and Applications, Proceedings, 383–389. https://doi.org/10.1109/ICETA48886.2019.9040158
Kitchenham, B. A., & Charters, S. (2007). Guidelines for performing Systematic Literature Reviews in Software Engineering. EBSE Technical Report EBSE-2007-01. School of Computer Science and Mathematics, Keele University. January, 1–57
Ko, C. Y., & Leu, F. Y. (2021). Examining Successful Attributes for Undergraduate Students by Applying Machine Learning Techniques. IEEE Transactions on Education, 64 (1), 50–57. https://doi.org/10.1109/TE.2020.3004596
Lacave, C., Molina, A. I., & Cruz-Lemus, J. A. (2018). Learning Analytics to identify dropout factors of Computer Science studies through Bayesian networks. Behaviour and Information Technology, 37 (10–11), 993–1007. https://doi.org/10.1080/0144929X.2018.1485053
Lee, S., & Chung, J. Y. (2019). The machine learning-based dropout early warning system for improving the performance of dropout prediction. Applied Sciences (Switzerland), 9 (15). https://doi.org/10.3390/app9153093
Lee, Y., Shin, D., Loh, H. Bin, Lee, J., Chae, P., Cho, J., Park, S., Lee, J., Baek, J., Kim, B., & Choi, Y. (2020). Deep attentive study session dropout prediction in mobile learning environment. CSEDU 2020 - Proceedings of the 12th International Conference on Computer Supported Education, 1, 26–35. https://doi.org/10.5220/0009347700260035
Lemay, D. J., & Doleck, T. (2020). Predicting completion of massive open online course (MOOC) assignments from video viewing behavior. Interactive Learning Environments, 30 (10), 1–12. https://doi.org/10.1080/10494820.2020.1746673
Liao, S. N., Zingaro, D., Thai, K., Alvarado, C., Griswold, W. G., & Porter, L. (2019). A robust machine learning technique to predict low-performing students. ACM Transactions on Computing Education, 19 (3), 1–19. https://doi.org/10.1145/3277569
Lincke, A., Jansen, M., Milrad, M., & Berge, E. (2021). The performance of some machine learning approaches and a rich context model in student answer prediction. Research and Practice in Technology Enhanced Learning, 16 (1). https://doi.org/10.1186/s41039-021-00159-7
Livieris, I. E., Drakopoulou, K., Tampakas, V. T., Mikropoulos, T. A., & Pintelas, P. (2019). Predicting Secondary School Students’ Performance Utilizing a Semi-supervised Learning Approach. Journal of Educational Computing Research, 57 (2), 448–470. https://doi.org/10.1177/0735633117752614
Lu, D. N., Le, H. Q., & Vu, T. H. (2020). The factors affecting acceptance of e-learning: A machine learning algorithm approach. Education Sciences, 10 (10), 1–13. https://doi.org/10.3390/educsci10100270
Ma, X., Yang, Y., & Zhou, Z. (2018). Using machine learning algorithm to predict student pass rates in online education. ACM International Conference Proceeding Series, 156–161. https://doi.org/10.1145/3220162.3220188
Martínez-Abad, F. (2019). Identification of Factors Associated With School Effectiveness With Data Mining Techniques: Testing a New Approach. Frontiers in Psychology, 10 (11), 1–13. https://doi.org/10.3389/fpsyg.2019.02583
Moreno-Marcos, P. M., Muñoz-Merino, P. J., Alario-Hoyos, C., Estévez-Ayres, I., & Delgado Kloos, C. (2018). Analysing the predictive power for anticipating assignment grades in a massive open online course. Behaviour and Information Technology, 37 (10–11), 1021–1036. https://doi.org/10.1080/0144929X.2018.1458904
Moreno-Marcos, P. M., Alario-Hoyos, C., Munoz-Merino, P. J., & Kloos, C. D. (2019). Prediction in MOOCs: A Review and Future Research Directions. IEEE Transactions on Learning Technologies, 12 (3), 384–401. https://doi.org/10.1109/TLT.2018.2856808
Mourdi, Y., Sadgal, M., El Kabtane, H., & Berrada Fathi, W. (2019). A machine learning-based methodology to predict learners’ dropout, success or failure in MOOCs. International Journal of Web Information Systems, 15 (5), 489–509. https://doi.org/10.1108/IJWIS-11-2018-0080
Mubarak, A. A., Cao, H., & Zhang, W. (2020). Prediction of students’ early dropout based on their interaction logs in online learning environment. Interactive Learning Environments, 30 (8), 1–20. https://doi.org/10.1080/10494820.2020.1727529
Musso, M. F., Hernández, C. F. R., & Cascallar, E. C. (2020). Predicting key educational outcomes in academic trajectories: a machine-learning approach. Higher Education, 80 (5), 875–894. https://doi.org/10.1007/s10734-020-00520-7
Naicker, N., Adeliyi, T., & Wing, J. (2020). Linear Support Vector Machines for Prediction of Student Performance in School-Based Education. Mathematical Problems in Engineering, 2020. https://doi.org/10.1155/2020/4761468
Ninrutsirikun, U., Imai, H., Watanapa, B., & Arpnikanondt, C. (2020). Principal Component Clustered Factors for Determining Study Performance in Computer Programming Class. Wireless Personal Communications, 115 (4), 2897–2916. https://doi.org/10.1007/s11277-020-07194-5
Niu, Z., Li, W., Yan, X., & Wu, N. (2018). Exploring causes for the dropout on massive open online courses. ACM International Conference Proceeding Series, 47–52. https://doi.org/10.1145/3210713.3210727
Oeda, S., & Hashimoto, G. (2017). Log-Data Clustering Analysis for Dropout Prediction in Beginner Programming Classes. Procedia Computer Science, 112, 614–621. https://doi.org/10.1016/j.procs.2017.08.088
Orellana, D., Segovia, N., & Cánovas, B. R. (2020). El abandono estudiantil en programas de educación superior virtual: revisión de literatura. Revista de la Educación Superior, 49 (194), 45–62. https://doi.org/10.36857/resu.2020.194.1124
Pillutla, V. S., Tawfik, A. A., & Giabbanelli, P. J. (2020). Detecting the Depth and Progression of Learning in Massive Open Online Courses by Mining Discussion Data. Technology, Knowledge and Learning, 25 (4), 881–898. https://doi.org/10.1007/s10758-020-09434-w
Qazdar, A., Er-Raha, B., Cherkaoui, C., & Mammass, D. (2019). A machine learning algorithm framework for predicting students performance: A case study of baccalaureate students in Morocco. Education and Information Technologies, 24 (6), 3577–3589. https://doi.org/10.1007/s10639-019-09946-8
Rastrollo-Guerrero, J. L., Gómez-Pulido, J. A., & Durán-Domínguez, A. (2020). Analyzing and predicting students’ performance by means of machine learning: A review. Applied Sciences (Switzerland), 10 (3). https://doi.org/10.3390/app10031042
Sabri, M., El Bouhdidi, J., & Chkouri, M. Y. (2021). A proposal for a deep learning model to enhance student guidance and reduce dropout. Lecture Notes in Networks and Systems, 144, 158–165. https://doi.org/10.1007/978-3-030-53970-2_15
Shakil Ahamed, A. T. M., Mahmood, N. T., & Rahman, R. M. (2017). An intelligent system to predict academic performance based on different factors during adolescence. Journal of Information and Telecommunication, 1 (2), 155–175. https://doi.org/10.1080/24751839.2017.1323488
Shelton, B. E., Yang, J., Hung, J. L., & Du, X. (2018). Two-stage predictive modeling for identifying at-risk students. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 11003 LNCS, 578–583. https://doi.org/10.1007/978-3-319-99737-7_61
Tamada, M. M., Netto, J. F. D. M., & De Lima, D. P. R. (2019). Predicting and Reducing Dropout in Virtual Learning using Machine Learning Techniques: A Systematic Review. Proceedings - Frontiers in Education Conference, FIE, 2019-Octob(October). https://doi.org/10.1109/FIE43999.2019.9028545
Thomas, J. J., & Ali, A. M. (2020). Dispositional Learning Analytics Structure Integrated with Recurrent Neural Networks in Predicting Students Performance. Advances in Intelligent Systems and Computing, 1072, 446–456. https://doi.org/10.1007/978-3-030-33585-4_44
Von Hippel, P. T., & Hofflinger, A. (2021). The data revolution comes to higher education: identifying students at risk of dropout in Chile. Journal of Higher Education Policy and Management, 43 (1), 2–23. https://doi.org/10.1080/1360080X.2020.1739800
Waheed, H., Hassan, S. U., Aljohani, N. R., Hardman, J., Alelyani, S., & Nawaz, R. (2020). Predicting academic performance of students from VLE big data using deep learning models. Computers in Human Behavior, 104. https://doi.org/10.1016/j.chb.2019.106189
Wang, H., Li, G., Wang, G., & Lin, L. (2019). CamDrop: A new explanation of dropout and a guided regularization method for deep neural networks. International Conference on Information and Knowledge Management, Proceedings, 1141–1149. https://doi.org/10.1145/3357384.3357999
Wang, W., Yu, H., & Miao, C. (2017). Deep model for dropout prediction in MOOCs. ACM International Conference Proceeding Series, Part F1306, 26–32. https://doi.org/10.1145/3126973.3126990
Wang, X., Schneider, H., & Walsh, K. R. (2020). A Predictive Analytics Approach to Building a Decision Support System for Improving Graduation Rates at a Four-Year College. Journal of Organizational and End User Computing, 32 (4), 43–62. https://doi.org/10.4018/joeuc.2020100103
Whitehill, J., Mohan, K., Seaton, D., Rosen, Y., & Tingley, D. (2017). MOOC Dropout Prediction. 161–164. https://doi.org/10.1145/3051457.3053974
Wu, N. (2019). CLMS - Net : Dropout Prediction in MOOCs with Deep Learning. https://doi.org/10.1145/3321408.3322848
Yair, G., Rotem, N., & Shustak, E. (2020). The riddle of the existential dropout: lessons from an institutional study of student attrition. European Journal of Higher Education, 10 (4), 436–453. https://doi.org/10.1080/21568235.2020.1718518
Yang, J., Devore, S., Hewagallage, D., Miller, P., Ryan, Q. X., & Stewart, J. (2020). Using machine learning to identify the most at-risk students in physics classes. Physical Review Physics Education Research, 16 (2), 20130. https://doi.org/10.1103/PhysRevPhysEducRes.16.020130
Yang, Z., Yang, J., Rice, K., Hung, J. L., & Du, X. (2020). Using Convolutional Neural Network to Recognize Learning Images for Early Warning of At-Risk Students. IEEE Transactions on Learning Technologies, 13 (3), 617–630. https://doi.org/10.1109/TLT.2020.2988253
Yildiz, M., & Börekci, C. (2020). Predicting Academic Achievement with Machine Learning Algorithms. Journal of Educational Technology and Online Learning, 3 (3). https://doi.org/10.31681/jetol.773206
Yousafzai, B. K., Hayat, M., & Afzal, S. (2020). Application of machine learning and data mining in predicting the performance of intermediate and secondary education level student. Education and Information Technologies, 25 (6), 4677–4697. https://doi.org/10.1007/s10639-020-10189-1
Zabriskie, C., Yang, J., Devore, S., & Stewart, J. (2019). Using machine learning to predict physics course outcomes. Physical Review Physics Education Research, 15 (2), 20120. https://doi.org/10.1103/PhysRevPhysEducRes.15.020120
Zeineddine, H., Braendle, U., & Farah, A. (2021). Enhancing prediction of student success: Automated machine learning approach. Computers and Electrical Engineering, 89 (11), 106903. https://doi.org/10.1016/j.compeleceng.2020.106903
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Derechos de autor 2023 Javier Gamboa-Cruzado, Cinthya Y. Alvarez-Cuellar, Shirley Martinez-Medina, Josue Edison Turpo Chaparro, Aníbal Sifuentes Damián, María Rodríguez Kong
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