Multi-Clustered Mathematical Model for Student Cognitive Skills Prediction Optimization

Abstract

The outbreak of COVID-19 boosted the rapid increase in E-Learning platforms. It also paves the way for Massive Online Open Courses (MOOCs) to break the record for students' enrollment in online courses. In such circumstances, it is significant to timely identify at-risk students' Cognitive Skills (CS) through an optimized E-Health service. CS is profoundly influenced (negatively and positively) by many human factors, including anxiety and biological age group. Literature has massive findings that correlated CS with anxiety and ageing. However, the earlier studies contributed to CS prediction algorithms are still limited and not up to the mark to efficiently estimate CS under the umbrellas of anxiety and age clusters. The CS prediction system requires an optimization algorithm to mathematical model the influence of age and anxiety clusters. This work predicts students' CS under the influence of anxiety and age clusters, referred to as the Anxiety and Ageing (AA) mathematical model. It solves threefold challenges. First, the study quantizes students' CS, age, and the adverse effects of anxiety. Second, it iteratively computes CS with respect to the anxiety cluster and further revises it under the influence of the age cluster. Third, the study provides a novel data collection method for future researchers by demonstrating assumption-based datasets. The prediction results manifest that the current model achieved excellent precision, recall, and F1 score performance.