Journal of Applied Science and Engineering

Published by Tamkang University Press

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Leveraging Big Data Analytics to Map National-Level Student Engagement Patterns and Predict Success for Policy-Level Labour Course Planning

 2026

 Volume 35

Xiaozan Zhu1, Haiting Zhang1, and Min Cai2

1School of Marxism, Weifang Engineering Vacational College, Weifang 262500, Shandong, China

2School of Continuing Education, Weifang Engineering Vacational College, Weifang 262500, Shandong, China

Received: June 03, 2026
Accepted: July 29, 2026
Publication Date: September 06, 2026

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RF architecture for feature selection

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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 R² = 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.

Keywords: Big Data Analytics, Student Engagement, Random Forest, Deep Neural Network, Adaptive Grey Wolf Optimizer, Course Recommendation

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