{"id":11607,"date":"2026-09-06T15:24:54","date_gmt":"2026-09-06T07:24:54","guid":{"rendered":"\/jase\/?post_type=tkuisotope&p=11607"},"modified":"2026-09-06T16:53:16","modified_gmt":"2026-09-06T08:53:16","slug":"jase-202612-35-016","status":"publish","type":"tkuisotope","link":"\/jase\/?tkuisotope=jase-202612-35-016","title":{"rendered":"Leveraging Big Data Analytics to Map National-Level Student Engagement Patterns and Predict Success for Policy-Level Labour Course Planning"},"content":{"rendered":"\n
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<\/i> 2026<\/a><\/p>\n<\/div>\n\n\n\n

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<\/i> Volume 35<\/a><\/p>\n<\/div>\n\n\n\n

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Xiaozan Zhu1<\/sup>, Haiting Zhang1<\/sup><\/i><\/a>, and Min Cai2<\/sup><\/p>\n\n\n\n

1<\/sup>School of Marxism, Weifang Engineering Vacational College, Weifang 262500, Shandong, China<\/p>\n\n\n\n

2<\/sup>School of Continuing Education, Weifang Engineering Vacational College, Weifang 262500, Shandong, China<\/p>\n<\/div>\n\n\n\n

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Received: June 03, 2026
Accepted: July 29, 2026
Publication Date: September 06, 2026<\/p>\n<\/div>\n<\/div>\n\n\n\n

\"\u4e0a\u50b3\u5716\u7247\"\n\n\n

RF architecture for feature selection<\/p>\n<\/div>\n<\/div>\n\n\n\n

<\/i> Copyright <\/strong>The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0)<\/a>, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.<\/p>\n\n\n\n

Download Citation: BibTeX <\/a>| http:\/\/dx.doi.org\/10.6180\/jase.202612_35.016<\/a> <\/p>\n\n\n\n

Download PDF<\/a><\/p>\n\n\n\n

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The rapid growth of educational data has enabled big data analytics to improve engagement analysis and student success prediction. However, existing approaches primarily focus on prediction accuracy while overlooking engagement patterns and recommendation mechanisms. This research proposes an integrated big data framework to analyse engagement patterns, predict national-level student success and support policy-level labour-course planning. The framework uses OECD Education Global Positioning System (GPS) data and includes data preprocessing, feature extraction, K-means clustering, and an Adaptive Grey Wolf Optimizer (GWO)-based Hybrid Random Forest-Deep Neural Network (RF-DNN) model for feature selection, prediction, and hyperparameter optimization. A rules-based recommender system generates mathematics-focused labour-course recommendations based on predicted success and engagement patterns. Experimental results achieved R\u00b2 = 0.982, MSE = 0.0005, RMSE = 0.0225, and MAE = 0.0135, demonstrating high prediction accuracy and low error. The proposed framework provides a scalable solution for engagement analysis, student success prediction, and policy-level course recommendations.<\/p>\n\n\n\n

Keywords: Big Data Analytics, Student Engagement, Random Forest, Deep Neural Network, Adaptive Grey Wolf Optimizer, Course Recommendation<\/em><\/p>\n\n\n\n

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