A Multiple Intelligent-Enabled Cognitive Agent Interaction Architecture for Enhancing Student Performance

G. S. Spoorthi; Maragal Venkatamuni Vijay Kumar; Gowdra Shivanandappa Mamatha

doi:10.48084/etasr.12664

Authors

G. S. Spoorthi Department of Computer Science and Engineering, Dr. Ambedkar Institute of Technology, Affiliated to Visvesvaraya Technological University, Belagavi, India | Department of Machine Learning, B.M.S. College of Engineering, P.O. Box No.: 1908, Bull Temple Road, Bengaluru – 560019, Karnataka, India
Maragal Venkatamuni Vijay Kumar Department of Information Science and Engineering, Dr. Ambedkar Institute of Technology, Affiliated to Visvesvaraya Technological University, Belagavi, India
Gowdra Shivanandappa Mamatha Department of Information Science and Engineering, RV College of Engineering, Affiliated to Visvesvaraya Technological University, Belagavi, India

Volume: 15 | Issue: 6 | Pages: 29049-29055 | December 2025 | https://doi.org/10.48084/etasr.12664

Received: 10 June 2025 | Revised: 5 August 2025 and 29 August 2025 | Accepted: 16 September 2025 | Online: 8 December 2025

Corresponding author: G. S. Spoorthi

Abstract

Designing an effective Learning Management System (LMS) improves the overall teaching and learning performance of students. However, assessing student behavior on a virtual learning platform is difficult, as different students exhibit different contextual behaviors. Multiple Intelligence (MI) combined with Cognitive Architecture (CA) can be adopted to learn these multiple behaviors exhibited by students and improve the overall student experience. MICA-based models exhibit high training time and lower accuracy, thus necessitating the need to improve the learning layer with enhanced agent interaction and rewarding mechanisms leveraging a Deep Reinforcement Learning (DRL) model. An experiment was conducted on the Education Process Mining (EPM) dataset, and the results show that the proposed MICA DRL (MICA-DRL) model exhibited enhanced performance, achieving 99.71% accuracy, which is higher compared to current student performance analysis methods.

Keywords:

behavior, cognitive architecture, deep reinforcement learning, multiple intelligence, student performance

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