Article: Towards group psychological state prediction with elastic computing resource allocation in large-scale open courses Journal: International Journal of Information and Communication Technology (IJICT) 2026 Vol.27 No.33 pp.28 - 48 Abstract: Massive open online courses have difficulty dynamically allocating computational resources and maintaining a good learner experience. Existing methods, using system metrics, do not consider the impact of collective psychological states on demand. This paper puts forward a group psychological state-oriented elastic resource allocation framework. We analyse multimodal behavioural data to build a predictive model of learner states such as engagement or confusion. The prediction dynamically guides cloud resource scaling through a state-aware algorithm. Extensive experiments on the public massive open online courses dataset prove the effectiveness of our approach. The accuracy of recognising the psychological state reaches 89.3%, and it can save about 30% of resources compared with traditional methods. This study shows how mixing psychological knowledge enables better resource management, making them more useful and quicker to react for big online learning places on internet. Inderscience Publishers - linking academia, business and industry through research

Title: Towards group psychological state prediction with elastic computing resource allocation in large-scale open courses

Authors: Ya Zhou

Addresses: Hunan Vocational College of Electronic Science and Technology, Changsha 410203, China

Abstract: Massive open online courses have difficulty dynamically allocating computational resources and maintaining a good learner experience. Existing methods, using system metrics, do not consider the impact of collective psychological states on demand. This paper puts forward a group psychological state-oriented elastic resource allocation framework. We analyse multimodal behavioural data to build a predictive model of learner states such as engagement or confusion. The prediction dynamically guides cloud resource scaling through a state-aware algorithm. Extensive experiments on the public massive open online courses dataset prove the effectiveness of our approach. The accuracy of recognising the psychological state reaches 89.3%, and it can save about 30% of resources compared with traditional methods. This study shows how mixing psychological knowledge enables better resource management, making them more useful and quicker to react for big online learning places on internet.

Keywords: massive open online courses; MOOCs; collective psychological state; elastic resource allocation; machine learning; resource optimisation.

DOI: 10.1504/IJICT.2026.152919

International Journal of Information and Communication Technology, 2026 Vol.27 No.33, pp.28 - 48

Received: 18 Dec 2025
Accepted: 20 Jan 2026
Published online: 14 Apr 2026 *

Title: Towards group psychological state prediction with elastic computing resource allocation in large-scale open courses

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