Clustering of Learners based on Readiness to Online Modality using K-Means Algorithm

Abstract

Clustering is one of the important techniques in data mining. It is an unsupervised task of grouping similar data. It has been applied in various fields with high degree of success. This study aimed to determine the learner segments based on readiness to online learning modality using K-means algorithm. A dataset was collected, tabulated and pre-processed. Further, the values were scaled and transformed using t-distributed Stochastic Neighbor Embedding. Using elbow method and determining the silhouette score, the best K value was determined. Then clustering was conducted using the selected number of clusters. Results revealed three groups of learners; Moderate-signal mobile users, Low-signal mobile users, and mixed group of Low/moderate-signal mobile/broadband users. Students from the different clusters are more suited for flexible learning as opposed to online learning. Varied learning modalities can be catered for students from the different learner segments. Formulation and adoption of new policies are needed to offset the effect of the pandemic towards the students.