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Anticipating students\u2019 academic performance can function as an early alert system for those facing potential failure, enabling educational institutions to implement interventions proactively. This study proposes leveraging features extracted from a convolutional neural network (CNN) in conjunction with machine learning models to enhance predictive accuracy. This approach obviates the need for manual feature extraction and yields superior outcomes compared to using machine learning and deep learning models independently. Initially, nine machine learning models are applied to both the original and convoluted features. The top-performing individual models are then combined into an ensemble model. This research work makes an ensemble of support vector machine (SVM) and random forest (RF) for academic performance prediction. The efficacy of the proposed method is validated against existing models, demonstrating its superior performance. With an accuracy of 97.88%, and precision, recall, and F1 scores of 98%, the proposed approach attains outstanding results in forecasting student academic success. This study contributes to the burgeoning field of predictive analytics in education by showcasing the effectiveness of leveraging multimedia data from learning management systems with convoluted features and ensemble modeling techniques.<\/jats:p>","DOI":"10.1145\/3687268","type":"journal-article","created":{"date-parts":[[2024,8,10]],"date-time":"2024-08-10T07:10:49Z","timestamp":1723273849000},"page":"1-16","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":14,"title":["Student Academic Success Prediction Using Learning Management Multimedia Data With Convoluted Features and Ensemble Model"],"prefix":"10.1145","volume":"17","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0443-4645","authenticated-orcid":false,"given":"Abdullah","family":"Al-Ameri","sequence":"first","affiliation":[{"name":"College of Education, University of Hafr Al Batin","place":["Hafr Al Batin, Saudi Arabia"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3687-8208","authenticated-orcid":false,"given":"Waleed","family":"Al-Shammari","sequence":"additional","affiliation":[{"name":"Department of Education in Hail, Kingdom of Saudi Arabia Ministry of Education","place":["Riyadh, Saudi Arabia"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0571-1074","authenticated-orcid":false,"given":"Aniello","family":"Castiglione","sequence":"additional","affiliation":[{"name":"Department of Management & Innovation Systems, University of Salerno","place":["Fisciano, Italy"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2517-2867","authenticated-orcid":false,"given":"Michele","family":"Nappi","sequence":"additional","affiliation":[{"name":"Computer Science, University of Salerno","place":["Fisciano, Italy"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5517-2198","authenticated-orcid":false,"given":"Chiara","family":"Pero","sequence":"additional","affiliation":[{"name":"Department of Management & Innovation Systems, University of Salerno","place":["Fisciano, Italy"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6015-9326","authenticated-orcid":false,"given":"Muhammad","family":"Umer","sequence":"additional","affiliation":[{"name":"Computer Science, The Islamia University of Bahawalpur Pakistan","place":["Bahawalpur, Pakistan"]}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2025,9,23]]},"reference":[{"key":"e_1_3_1_2_2","doi-asserted-by":"publisher","DOI":"10.1177\/07356331211056178"},{"issue":"1","key":"e_1_3_1_3_2","first-page":"161","article-title":"Exploring cross-cultural and gender differences in predicting academic performance using machine learning","volume":"12","author":"Ali S.","year":"2023","unstructured":"S. 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