{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,25]],"date-time":"2026-02-25T19:07:07Z","timestamp":1772046427893,"version":"3.50.1"},"reference-count":58,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2024,9,11]],"date-time":"2024-09-11T00:00:00Z","timestamp":1726012800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Computers"],"abstract":"<jats:p>Student enrollment is a vital aspect of educational institutions, encompassing active, registered and graduate students. All the same, some students fail to engage with their studies after admission and drop out along the line; this is known as attrition. The student attrition rate is acknowledged as the most complicated and significant problem facing educational systems and is caused by institutional and non-institutional challenges. In this study, the researchers utilized a dataset obtained from the National Open University of Nigeria (NOUN) from 2012 to 2022, which included comprehensive information about students enrolled in various programs at the university who were inactive and had dropped out. The researchers used deep learning techniques, such as the Long Short-Term Memory (LSTM) model and compared their performance with the One-Dimensional Convolutional Neural Network (1DCNN) model. The results of this study revealed that the LSTM model achieved overall accuracy of 57.29% on the training data, while the 1DCNN model exhibited lower accuracy of 49.91% on the training data. The LSTM indicated a superior correct classification rate compared to the 1DCNN model.<\/jats:p>","DOI":"10.3390\/computers13090229","type":"journal-article","created":{"date-parts":[[2024,9,12]],"date-time":"2024-09-12T02:30:23Z","timestamp":1726108223000},"page":"229","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Deep Learning for Predicting Attrition Rate in Open and Distance Learning (ODL) Institutions"],"prefix":"10.3390","volume":"13","author":[{"given":"Juliana Ngozi","family":"Ndunagu","sequence":"first","affiliation":[{"name":"Department of Computer Science and Information Technology, Faculty of Sciences, National Open University of Nigeria, Plot 91, Abuja 900108, Nigeria"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9638-8764","authenticated-orcid":false,"given":"David Opeoluwa","family":"Oyewola","sequence":"additional","affiliation":[{"name":"Department of Mathematics and Statistics, Faculty of Science, Federal University Kashere, PMB 0182, Gombe 760001, Nigeria"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0764-2391","authenticated-orcid":false,"given":"Farida Shehu","family":"Garki","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Information Technology, Faculty of Sciences, National Open University of Nigeria, Plot 91, Abuja 900108, Nigeria"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-5031-4970","authenticated-orcid":false,"given":"Jude Chukwuma","family":"Onyeakazi","sequence":"additional","affiliation":[{"name":"Directorate of General Studies, Federal University of Technology, PMB 1526, Owerri 460114, Nigeria"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-2510-5966","authenticated-orcid":false,"given":"Christiana Uchenna","family":"Ezeanya","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Information Technology, Faculty of Sciences, National Open University of Nigeria, Plot 91, Abuja 900108, Nigeria"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1350-4438","authenticated-orcid":false,"given":"Elochukwu","family":"Ukwandu","sequence":"additional","affiliation":[{"name":"Department of Applied Computing, Cardiff School of Technologies, Cardiff Metropolitan University, 200 Western Avenue, Cardiff CF5 2YB, UK"}]}],"member":"1968","published-online":{"date-parts":[[2024,9,11]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"135550","DOI":"10.1109\/ACCESS.2021.3117117","article-title":"Deep Learning Model to Predict Students Retention Using BLSTM and CRF","volume":"9","author":"Uliyan","year":"2021","journal-title":"IEEE Access"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"29","DOI":"10.47738\/jads.v1i1.9","article-title":"Predicting Dropout on E-Learning Using Machine Learning","volume":"1","author":"Akmal","year":"2020","journal-title":"J. Appl. Data Sci."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Alamri, A., Sun, Z., Cristea, A.I., Senthilnathan, G., Shi, L., and Stewart, C. (2020). Is mooc learning different for dropouts? a visually driven, multi-granularity explanatory ml approach. International Conference on Intelligent Tutoring Systems, Springer.","DOI":"10.1007\/978-3-030-49663-0_42"},{"key":"ref_4","first-page":"29","article-title":"Mitigating Academic Institution Dropout Rates with Predictive Analytics Algorithms","volume":"3","author":"Lainjo","year":"2023","journal-title":"Int. J. Educ. Teach. Soc. Sci."},{"key":"ref_5","unstructured":"L\u00f3pez-Pernas, S., Kleimola, R., V\u00e4is\u00e4nen, S., and Hirsto, L. (2022, January 29\u201330). Early detection of dropout factors in vocational education: A large-scale case study from Finland. Proceedings of the Finnish Learning Analytics and Artificial Intelligence in Education Conference (FLAIEC22), Joensuu, Finland."},{"key":"ref_6","first-page":"387","article-title":"The effectiveness of using deep learning algorithms in predicting students\u2019 achievements","volume":"19","author":"Akour","year":"2020","journal-title":"Indones. J. Electr. Eng. Comput. Sci."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Taye, M.M. (2023). Understanding of Machine Learning with Deep Learning: Architectures, Workflow, Applications and Future Directions. Computers, 12.","DOI":"10.3390\/computers12050091"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"233","DOI":"10.3991\/ijet.v16i07.22123","article-title":"A framework of an intelligent education system for higher education based on deep learning","volume":"16","author":"Zhang","year":"2021","journal-title":"Int. J. Emerg. Technol. Learn. (Online)"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Aggarwal, K., Singh, S.K., Chopra, M., Kumar, S., and Colace, F. (2022). Deep learning in robotics for strengthening industry 4.0.: Opportunities, challenges and future directions. Robotics and AI for Cybersecurity and Critical Infrastructure in Smart Cities, Springer.","DOI":"10.1007\/978-3-030-96737-6_1"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"19","DOI":"10.1007\/s13735-021-00218-1","article-title":"A review on deep learning in medical image analysis","volume":"11","author":"Suganyadevi","year":"2022","journal-title":"Int. J. Multimed. Inf. Retr."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1071","DOI":"10.1007\/s11831-019-09344-w","article-title":"A survey of deep learning and its applications: A new paradigm to machine learning","volume":"27","author":"Dargan","year":"2020","journal-title":"Arch. Comput. Methods Eng."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Pierrakeas, C., Koutsonikos, G., Lipitakis, A.D., Kotsiantis, S., Xenos, M., and Gravvanis, G.A. (2020). The variability of the reasons for student dropout in distance learning and the prediction of dropout-prone students. Machine Learning Paradigms: Advances in Learning Analytics, Springer. The Variability of the Reasons for Student Dropout in Distance Learning and the Prediction of Dropout-Prone Students.","DOI":"10.1007\/978-3-030-13743-4_6"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"357","DOI":"10.1108\/JARHE-05-2019-0110","article-title":"Analysis of the attrition phenomenon through the lens of university dropouts in the United Arab Emirates","volume":"12","author":"Ashour","year":"2020","journal-title":"J. Appl. Res. High. Educ."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Del Bonifro, F., Gabbrielli, M., Lisanti, G., and Zingaro, S.P. (2020, January 6\u201310). Student dropout prediction. Proceedings of the Artificial Intelligence in Education: 21st International Conference, AIED 2020, Ifrane, Morocco. Proceedings, Part I 21.","DOI":"10.1007\/978-3-030-52237-7_11"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"429","DOI":"10.1007\/s12528-019-09241-y","article-title":"Factors affecting student dropout in MOOCs: A cause and effect decision-making model","volume":"32","author":"Aldowah","year":"2020","journal-title":"J. Comput. High. Educ."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Kim, D., and Kim, S. (2018). Sustainable education: Analyzing the determinants of university student dropout by nonlinear panel data models. Sustainability, 10.","DOI":"10.3390\/su10040954"},{"key":"ref_17","first-page":"380","article-title":"Development of prediction model to improve dropout of cyber university","volume":"21","author":"Park","year":"2020","journal-title":"J. Korea Acad.-Ind. Coop. Soc."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Sun, Z., Harit, A., Yu, J., Cristea, A.I., and Shi, L. (2021, January 7\u201311). A Brief Survey of Deep Learning Approaches for Learning Analytics on MOOCs. Proceedings of the Intelligent Tutoring Systems: 17th International Conference, ITS 2021, Virtual Event.","DOI":"10.1007\/978-3-030-80421-3_4"},{"key":"ref_19","first-page":"2670562","article-title":"Using Machine Learning Techniques to Predict Learner Drop-out Rate in Higher Educational Institutions. Edited by Robin Singh Bhadoria","volume":"2022","author":"Dake","year":"2022","journal-title":"Mob. Inf. Syst."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"28","DOI":"10.1186\/s41239-022-00333-x","article-title":"Why do open and distance education students drop out? Views from various stakeholders","volume":"19","year":"2022","journal-title":"Int. J. Educ. Technol. High. Educ."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Queiroga, E.M., Lopes, J.L., Kappel, K., Aguiar, M., Ara\u00fajo, R.M., Munoz, R., Villarroel, R., and Cechinel, C. (2020). A Learning Analytics Approach to Identify Students at Risk of Dropout: A Case Study with a Technical Distance Education Course. Appl. Sci., 10.","DOI":"10.3390\/app10113998"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"53736","DOI":"10.1109\/ACCESS.2023.3280075","article-title":"Analysis of attrition studies within the computer sciences","volume":"11","author":"Obaido","year":"2023","journal-title":"IEEE Access"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Stephenson, C., Miller, A.D., Alvarado, C., Barker, L., Barr, V., Camp, T., Frieze, C., Lewis, C., Mindell, E.C., and Limbird, L. (2018). Retention in Computer Science Undergraduate Programs in the Us: Data Challenges and Promising Interventions, ACM.","DOI":"10.1145\/3406772"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"107271","DOI":"10.1016\/j.compeleceng.2021.107271","article-title":"Deep Analytic Model for Student Dropout Prediction in Massive Open Online Courses","volume":"93","author":"Mubarak","year":"2021","journal-title":"Comput. Electr. Eng."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Ba\u00f1eres, D., Rodr\u00edguez, M.E., Guerrero-Rold\u00e1n, A.E., and Karadeniz, A. (2020). An early warning system to detect at-risk students in online higher education. Appl. Sci., 10.","DOI":"10.3390\/app10134427"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Isidro, C., Carro, R.M., and Ortigosa, A. (2018, January 19\u201321). Dropout detection in MOOCs: An exploratory analysis. Proceedings of the 2018 International Symposium on Computers in Education (SIIE), C\u00e1diz, Spain.","DOI":"10.1109\/SIIE.2018.8586748"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Raga, R.C., and Raga, J.D. (2019, January 2\u20134). Early prediction of student performance in blended learning courses using deep neural networks. Proceedings of the 2019 International Symposium on Educational Technology (ISET), Hradec Kr\u00e1lov\u00e9, Czech Republic.","DOI":"10.1109\/ISET.2019.00018"},{"key":"ref_28","first-page":"55","article-title":"Future of remote learning: The virtual laboratory perspective","volume":"8","author":"Ndunagu","year":"2022","journal-title":"Univ. Ib. J. Sci. Log. ICT Res."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"547","DOI":"10.1177\/0735633118757015","article-title":"Dropout prediction in MOOCs: Using deep learning for personalized intervention","volume":"57","author":"Xing","year":"2019","journal-title":"J. Educ. Comput. Res."},{"key":"ref_30","unstructured":"Cristea, A.I., Alamri, A., Kayama, M., Stewart, C., Alsheri, M., and Shi, L. (2018). Earliest predictor of dropout in moocs: A longitudinal study of futurelearn courses. Information Systems Development: Designing Digitalization, Association for Information Systems."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"K\u0151r\u00f6si, G., and Farkas, R. (2020). Mooc performance prediction by deep learning from raw clickstream data. International Conference on Advances in Computing and Data Sciences, Springer.","DOI":"10.1007\/978-981-15-6634-9_43"},{"key":"ref_32","first-page":"132","article-title":"Factors related to student persistence in open universities: Changes over the years","volume":"20","author":"Li","year":"2019","journal-title":"Int. Rev. Res. Open Distrib. Learn."},{"key":"ref_33","first-page":"4308","article-title":"Causes of Students\u2019 Dropout at Elementary Level in Pakistan","volume":"17","author":"Hassan","year":"2020","journal-title":"Palarch\u2019s J. Archaeol. Egypt\/Egyptol."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"85","DOI":"10.1016\/j.neunet.2014.09.003","article-title":"Deep Learning in Neural Networks: An Overview","volume":"61","author":"Schmidhuber","year":"2015","journal-title":"Neural Netw."},{"key":"ref_35","first-page":"3320","article-title":"How transferable are features in deep neural networks?","volume":"4","author":"Yosinski","year":"2014","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"436","DOI":"10.1038\/nature14539","article-title":"Deep learning","volume":"521","author":"LeCun","year":"2015","journal-title":"Nature"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1561\/2200000006","article-title":"Learning Deep Architectures for AI","volume":"2","author":"Bengio","year":"2009","journal-title":"Found. Trends Mach. Learn."},{"key":"ref_38","first-page":"228","article-title":"Graph Neural Networks for Predicting Student Performance: A Deep Learning Approach for Academic Success Forecasting","volume":"12","author":"Kannan","year":"2024","journal-title":"Int. J. Intell. Syst. Appl. Eng."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"184","DOI":"10.3991\/ijet.v18i17.41449","article-title":"Design of a Machine Learning Model to Predict Student Attrition","volume":"18","author":"Fauszt","year":"2023","journal-title":"Int. J. Emerg. Technol. Learn."},{"key":"ref_40","first-page":"78","article-title":"Prediction of Instructor Performance using Machine and Deep Learning Techniques","volume":"13","author":"Abunasser","year":"2022","journal-title":"(IJACSA) Int. J. Adv. Comput. Sci. Appl."},{"key":"ref_41","first-page":"6526","article-title":"Predicting Employee Attrition and Performance Using Deep Learning","volume":"100","author":"Arqawi","year":"2022","journal-title":"J. Theor. Appl. Inf. Technol."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"974","DOI":"10.1108\/JARHE-02-2021-0073","article-title":"Prediction of student attrition risk using machine learning","volume":"14","year":"2022","journal-title":"J. Appl. Res. High. Educ."},{"key":"ref_43","first-page":"811","article-title":"Deep neural network in prediction of student performance","volume":"8","author":"Lokhande","year":"2022","journal-title":"Gradiva Rev. J."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"140731","DOI":"10.1109\/ACCESS.2021.3119596","article-title":"Prediction of Students\u2019 Academic Performance Based on Courses\u2019 Grades Using Deep Neural Networks","volume":"9","author":"Nabil","year":"2021","journal-title":"IEEE Access"},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Opazo, D., Moreno, S., \u00c1lvarez-Miranda, E., and Pereira, J. (2021). Analysis of first-year university student dropout through machine learning models: A comparison between universities. Mathematics, 9.","DOI":"10.3390\/math9202599"},{"key":"ref_46","first-page":"562","article-title":"Predicting student enrollments and attrition patterns in higher educational institutions using machine learning","volume":"18","author":"Shilbayeh","year":"2021","journal-title":"Int. Arab J. Inf. Technol."},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"He, Y., Chen, R., Li, X., Hao, C., Liu, S., Zhang, G., and Jiang, B. (2020). Online At-Risk Student Identification Using RNN-GRU Joint Neural Networks. Information, 11.","DOI":"10.3390\/info11100474"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"28","DOI":"10.1080\/21568235.2020.1718520","article-title":"Predicting student dropout: A machine learning approach","volume":"10","author":"Kemper","year":"2020","journal-title":"Eur. J. High. Educ."},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Aguilar-Gonzalez, S., and Palafox, L. (2019). Prediction of Student Attrition Using Machine Learning. Advances in Soft Computing, Proceedings of the 18th Mexican International Conference on Artificial Intelligence, MICAI 2019, Xalapa, Mexico, 27 October\u20132 November 2019, Springer International Publishing. Proceedings 18.","DOI":"10.1007\/978-3-030-33749-0_18"},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Oyewola, D.O., Dada, E.G., Omotehinwa, T.O., Emebo, O., and Oluwagbemi, O.O. (2022). Application of Deep Learning Techniques and Bayesian Optimization with Tree Parzen Estimator in the Classification of Supply Chain Pricing Datasets of Health Medications. Appl. Sci., 12.","DOI":"10.3390\/app121910166"},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"e14836","DOI":"10.1016\/j.heliyon.2023.e14836","article-title":"Optimizing Sentiment Analysis of Nigerian 2023 Presidential Election Using Two-Stage Residual Long Short Term Memory","volume":"9","author":"Oyewola","year":"2023","journal-title":"Heliyon"},{"key":"ref_52","unstructured":"Masrour, T., El Hassani, I., and Cherrafi, A. (2021). A Proposal for a Deep Learning Model to Enhance Student Guidance and Reduce Dropout BT\u2014Artificial Intelligence and Industrial Applications, Springer International Publishing."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"1168","DOI":"10.1016\/j.procs.2020.03.049","article-title":"Stock Market Prediction Using LSTM Recurrent Neural Network","volume":"170","author":"Moghar","year":"2020","journal-title":"Procedia Comput. Sci."},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Qazi, E.U., Almorjan, A., and Zia, T. (2022). A One-Dimensional Convolutional Neural Network (1D-CNN) Based Deep Learning System for Network Intrusion Detection. Appl. Sci., 12.","DOI":"10.3390\/app12167986"},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"53","DOI":"10.1016\/j.compind.2018.12.001","article-title":"Intelligent Fault Diagnosis of Rotating Machinery Based on One-Dimensional Convolutional Neural Network","volume":"108","author":"Wu","year":"2019","journal-title":"Comput. Ind."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"70","DOI":"10.1016\/j.enconman.2019.05.007","article-title":"One Dimensional Convolutional Neural Network Architectures for Wind Prediction","volume":"195","author":"Harbola","year":"2019","journal-title":"Energy Convers. Manag."},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"Sharma, Y., and Singh, B.K. (2022). One-Dimensional Convolutional Neural Network and Hybrid Deep-Learning Paradigm for Classification of Specific Language Impzhanaired Children Using Their Speech. Comput. Methods Programs Biomed., 213.","DOI":"10.1016\/j.cmpb.2021.106487"},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"113564","DOI":"10.1016\/j.eswa.2020.113564","article-title":"Classification of Non-Small Cell Lung Cancer Using One-Dimensional Convolutional Neural Network","volume":"159","author":"Moitra","year":"2020","journal-title":"Expert Syst. Appl."}],"container-title":["Computers"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2073-431X\/13\/9\/229\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T15:54:11Z","timestamp":1760111651000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2073-431X\/13\/9\/229"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,9,11]]},"references-count":58,"journal-issue":{"issue":"9","published-online":{"date-parts":[[2024,9]]}},"alternative-id":["computers13090229"],"URL":"https:\/\/doi.org\/10.3390\/computers13090229","relation":{},"ISSN":["2073-431X"],"issn-type":[{"value":"2073-431X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,9,11]]}}}