Clustering Examples in Web-based Tutoring Systems based on Relevance of Concepts

Web-based online tutoring systems (WOTS) have become extremely important and relevant in today's world, especially with COVID-19 requiring schools, colleges and universities to offer alternate forms of delivery. Many studies have indicated that students find worked-out examples very useful, when they are performing a task or studying for final exams. WOTS certainly have the capability to host hundreds of such examples in their repositories, but presenting students with such repositories may cause cognitive overload on students and may force them to bear the responsibility of searching for the most relevant examples, when in need. This paper proposes an algorithm called CER (Clustering Examples based on Relevance) that organizes a collection of worked-out examples into coherent and relevant clusters - relevant to the learning concepts covered by them. When generating clusters, CER acknowledges not only the local relevance of a concept (using parameters such as mode) within a cluster but also its global relevance. The proposed algorithm CER is validated using Dunn's index as the internal validity index - a score of 0.81 was achieved for CER. The external validity of CER was measured by comparing its results to a benchmark dataset that had properties of data that were common to the domain of CER.