Article: Transforming educational data analytics: developing a reinforcement learning framework for real-time decision-making and resource optimisation Journal: International Journal of Information and Communication Technology (IJICT) 2025 Vol.26 No.9 pp.83 - 106 Abstract: This study presents a reinforcement learning (RL) framework for real-time decision-making and resource optimisation in education. It integrates various data streams, such as learner performance, engagement, and institutional constraints, to enable precise and scalable decisions in diverse educational environments. The framework is assessed using three metrics: optimisation efficiency (OE: +32.1%), real-time decision accuracy (RTDA: +28.4%), and equity distribution index (EDI: +26.7%). A comparison with heuristic-based models shows a cumulative improvement of +29.1% across all metrics. Powered by a deep neural network and optimised using policygradient techniques, the framework focuses on scalability and fairness in resource allocation. Validation with real-world datasets demonstrates its adaptability and robustness. This research lays a solid foundation for AI integration in educational systems, offering a new benchmark for transforming resource allocation and decision-making processes in academia. Inderscience Publishers - linking academia, business and industry through research

Title: Transforming educational data analytics: developing a reinforcement learning framework for real-time decision-making and resource optimisation

Authors: Lihua Tan

Addresses: School of Mathematics and Statistics, Chongqing Jiaotong University, Chongqing, China

Abstract: This study presents a reinforcement learning (RL) framework for real-time decision-making and resource optimisation in education. It integrates various data streams, such as learner performance, engagement, and institutional constraints, to enable precise and scalable decisions in diverse educational environments. The framework is assessed using three metrics: optimisation efficiency (OE: +32.1%), real-time decision accuracy (RTDA: +28.4%), and equity distribution index (EDI: +26.7%). A comparison with heuristic-based models shows a cumulative improvement of +29.1% across all metrics. Powered by a deep neural network and optimised using policygradient techniques, the framework focuses on scalability and fairness in resource allocation. Validation with real-world datasets demonstrates its adaptability and robustness. This research lays a solid foundation for AI integration in educational systems, offering a new benchmark for transforming resource allocation and decision-making processes in academia.

Keywords: data analytics; machine learning; reinforcement learning; decision making; resource optimisation; academic performance.

DOI: 10.1504/IJICT.2025.145826

International Journal of Information and Communication Technology, 2025 Vol.26 No.9, pp.83 - 106

Received: 12 Feb 2025
Accepted: 20 Feb 2025
Published online: 28 Apr 2025 *

Title: Transforming educational data analytics: developing a reinforcement learning framework for real-time decision-making and resource optimisation

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