Implementing temporally coherent clustering on student activity to predict exam performance & optimizing the learning pathway through markov chain analysis

Abstract

Recognizing a growing need to accommodate students of varied backgrounds and account for individual di erences in learning curves, this paper re ects on our work to implement a Temporally Coherent Clustering approach in order to detect the most optimized pathway for teaching subjects through an MCQ based learning platform. Standard approaches towards extraction of student activity data typically detect similar behavior patterns and use simple statistical analysis in order to make predictions regarding their result. In reality, this causes high noise in the data that is temporally inconsistent and largely inaccurate. We proposed to work with an evolutionary clustering pipeline that can be applied to learning data that we have collected through our Intelligent Teaching System - and aimed at improving cluster stability over a large data set of student behavior. Initially, we have collected and worked on BCS Examination related data, where our results show improved cluster performance of both students and study material, and achieves stability on organic user data in order to be able to detect behavioral patterns and properties of learning environments. As an end result of this whole research, we have incorporated our work into our ITS, which proactively determines student's knowledge level, and automatically determines the best pathway in order to improve their performance. Overall, it deliberately in uences a students capacity improvement in order to passively enable them to answer harder questions by creating an optimized pathway that recognizes the need for individualized learning curves. Overall, we managed to get an accuracy ratio of around 84%, with a silhouette score of 0.53 against an optimized k value of 5 within our clustering algorithm using k-means.