{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,16]],"date-time":"2026-02-16T17:01:18Z","timestamp":1771261278663,"version":"3.50.1"},"reference-count":24,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2025,11,10]],"date-time":"2025-11-10T00:00:00Z","timestamp":1762732800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Higher Education Teaching Reform Research and Practice Project of Henan Province","award":["2024SJGLX0127"],"award-info":[{"award-number":["2024SJGLX0127"]}]},{"name":"Higher Education Teaching Reform Research and Practice Project of Henan Province","award":["2024SJGLX0135"],"award-info":[{"award-number":["2024SJGLX0135"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Algorithms"],"abstract":"<jats:p>Accurate prediction of student graduation status is crucial for higher education institutions to implement timely interventions and improve student success. While existing methods often rely on single data sources or generic model architectures, this paper proposes a novel Multi-Branch Convolutional Neural Network (MBCNN) that systematically integrates multi-dimensional factors influencing student outcomes. The model employs eight dedicated branches to capture both subjective and objective features from four key dimensions: student characteristics, school resources, family environment, and societal factors. Through robust normalization and hierarchical feature fusion, MBCNN effectively learns discriminative representations from these heterogeneous data sources. Evaluated on a real-world dataset from Polytechnic Institute of Portalegre, our approach demonstrates superior performance compared to traditional and up-to-date machine learning methods, achieving improvements of 4.07\u201317.35% in accuracy, 4.60\u201320.19% in weighted precision, 4.07\u201317.35% in weighted recall, and 4.59\u201318.73% in weighted F1-score. The results validate that domain-specific neural architectures, designed to align with the inherent structure of educational data, significantly enhance prediction accuracy and generalization capability.<\/jats:p>","DOI":"10.3390\/a18110711","type":"journal-article","created":{"date-parts":[[2025,11,10]],"date-time":"2025-11-10T15:07:31Z","timestamp":1762787251000},"page":"711","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["A Multi-Branch Convolutional Neural Network for Student Graduation Prediction"],"prefix":"10.3390","volume":"18","author":[{"given":"Zhifeng","family":"Zhang","sequence":"first","affiliation":[{"name":"Software Engineering College, Zhengzhou University of Light Industry, Zhengzhou 450000, China"}]},{"given":"Xiaoyun","family":"Qin","sequence":"additional","affiliation":[{"name":"Department of Material and Chemical Engineering, Zhengzhou University of Light Industry, Zhengzhou 450000, China"}]},{"given":"Junxia","family":"Ma","sequence":"additional","affiliation":[{"name":"Software Engineering College, Zhengzhou University of Light Industry, Zhengzhou 450000, China"}]},{"given":"Yangyang","family":"Chu","sequence":"additional","affiliation":[{"name":"Software Engineering College, Zhengzhou University of Light Industry, Zhengzhou 450000, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3598-5359","authenticated-orcid":false,"given":"Bo","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Henan Institute of Technology, Xinxiang 453003, China"}]}],"member":"1968","published-online":{"date-parts":[[2025,11,10]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Lampropoulos, G., and Sidiropoulos, A. (2024). Impact of Gamification on Students\u2019 Learning Outcomes and Academic Performance: A Longitudinal Study Comparing Online, Traditional, and Gamified Learning. Educ. Sci., 14.","DOI":"10.3390\/educsci14040367"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Lin, Y., Chen, H., Xia, W., Lin, F., Wang, Z., and Liu, Y. (2025). A Comprehensive Survey on Deep Learning Techniques in Educational Data Mining. Data Sci. Eng., 1\u201327.","DOI":"10.1007\/s41019-025-00303-z"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"205","DOI":"10.1108\/AEDS-08-2024-0166","article-title":"Application of machine learning in higher education to predict students\u2019 performance, learning engagement and self-efficacy: A systematic literature review","volume":"14","author":"Chen","year":"2025","journal-title":"Asian Educ. Dev. Stud."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"8611","DOI":"10.1007\/s10639-024-13167-z","article-title":"Technology-based collaborative learning: EFL learners\u2019 social regulation and modifications in their academic emotions and academic performance","volume":"30","author":"Qi","year":"2025","journal-title":"Educ. Inf. Technol."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"4297","DOI":"10.1007\/s10212-024-00862-1","article-title":"The impact of family involvement on students\u2019 social-emotional development: The mediational role of school engagement","volume":"39","year":"2024","journal-title":"Eur. J. Psychol. Educ."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"36","DOI":"10.1007\/s10902-024-00749-4","article-title":"How and When Resilience can Boost Student Academic Performance: A Weekly Diary Study on the Roles of Self-Regulation Behaviors, Grit, and Social Support","volume":"25","author":"Li","year":"2024","journal-title":"J. Happiness Stud."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Realinho, V., Machado, J., Baptista, L., and Martins, M.V. (2022). Predicting Student Dropout and Academic Success. Data, 7.","DOI":"10.3390\/data7110146"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"168","DOI":"10.37934\/araset.45.2.168176","article-title":"Prediction of Student Dropout in Malaysian\u2019s Private Higher Education Institute using Data Mining Application","volume":"45","author":"Roslan","year":"2025","journal-title":"J. Adv. Res. Appl. Sci. Eng. Technol."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"105","DOI":"10.37934\/araset.44.1.105119","article-title":"An Artificial Intelligence Approach to Monitor and Predict Student Academic Performance","volume":"44","author":"Haron","year":"2024","journal-title":"J. Adv. Res. Appl. Sci. Eng. Technol."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1239","DOI":"10.1007\/s40593-024-00429-7","article-title":"An Extended Learning Analytics Framework Integrating Machine Learning and Pedagogical Approaches for Student Performance Prediction and Intervention","volume":"35","author":"Alalawi","year":"2025","journal-title":"Int. J. Artif. Intell. Educ."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"4055","DOI":"10.1007\/s10994-023-06467-x","article-title":"Hybrid approaches to optimization and machine learning methods: A systematic literature review","volume":"113","author":"Azevedo","year":"2024","journal-title":"Mach. Learn."},{"key":"ref_12","first-page":"15793","article-title":"Amachine learning basedmodel for student\u2019s dropout prediction in online training","volume":"29","author":"Zerkouk","year":"2024","journal-title":"Educ. Inf. Technol."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"106583","DOI":"10.1016\/j.econmod.2023.106583","article-title":"Predicting dropout from higher education: Evidence from Italy","volume":"130","author":"Delogu","year":"2024","journal-title":"Econ. Model."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Kok, C.L., Ho, C.K., Chen, L., Koh, Y.Y., and Tian, B. (2024). A Novel Predictive Modeling for Student Attrition Utilizing Machine Learning and Sustainable Big Data Analytics. Appl. Sci., 14.","DOI":"10.20944\/preprints202408.1298.v1"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Hassan, M.A., Muse, A.H., and Nadarajah, S. (2024). Predicting Student Dropout Rates Using Supervised Machine Learning: Insights from the 2022 National Education Accessibility Survey in Somaliland. Appl. Sci., 14.","DOI":"10.3390\/app14177593"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"28","DOI":"10.60084\/jeml.v2i1.191","article-title":"Machine Learning for Early Detection of Dropout Risks and Academic Excellence: A Stacked Classifier Approach","volume":"2","author":"Noviandy","year":"2024","journal-title":"J. Educ. Manag. Learn."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"100205","DOI":"10.1016\/j.caeai.2024.100205","article-title":"Machine learning model (RG-DMML) and ensemble algorithm for prediction of students\u2019 retention and graduation in education","volume":"6","author":"Okoye","year":"2024","journal-title":"Comput. Educ. Artif. Intell."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"100352","DOI":"10.1016\/j.caeai.2024.100352","article-title":"A novel AI-driven model for student dropout risk analysis with explainable AI insights","volume":"8","author":"Mustofa","year":"2025","journal-title":"Comput. Educ. Artif. Intell."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"108301","DOI":"10.1016\/j.chb.2024.108301","article-title":"AI student success predictor: Enhancing personalized learning in campus management systems","volume":"158","author":"Shoaib","year":"2024","journal-title":"Comput. Hum. Behav."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"14365","DOI":"10.1007\/s10639-023-12394-0","article-title":"A novel methodology using RNN + LSTM + ML for predicting student\u2019s academic performance","volume":"29","author":"Kukkar","year":"2024","journal-title":"Educ. Inf. Technol."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"72","DOI":"10.1016\/j.dsm.2024.07.001","article-title":"A model for predicting dropout of higher education students","volume":"8","author":"Rabelo","year":"2025","journal-title":"Data Sci. Manag."},{"key":"ref_22","first-page":"624","article-title":"Early prediction models and crucial factor extraction for first-year undergraduate student dropouts","volume":"17","author":"Chen","year":"2024","journal-title":"J. Appl. Res. High. Educ."},{"key":"ref_23","first-page":"86","article-title":"Bayesian Approach to the Construction of an Individual User Trajectory in The System of Distance Learning","volume":"14","author":"Bosov","year":"2020","journal-title":"Inform. Appl."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Bosov, A.V. (2023). Adaptation of Kohonen\u2019s Self-organizing Map to the Task of Constructing an Individual User Trajectory in an E-learning System. Proceedings of the 6th Computational Methods in Systems and Software (CoMeSySo), Springer.","DOI":"10.1007\/978-3-031-21438-7_44"}],"container-title":["Algorithms"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1999-4893\/18\/11\/711\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,11,12]],"date-time":"2025-11-12T02:38:50Z","timestamp":1762915130000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1999-4893\/18\/11\/711"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,11,10]]},"references-count":24,"journal-issue":{"issue":"11","published-online":{"date-parts":[[2025,11]]}},"alternative-id":["a18110711"],"URL":"https:\/\/doi.org\/10.3390\/a18110711","relation":{},"ISSN":["1999-4893"],"issn-type":[{"value":"1999-4893","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,11,10]]}}}