Machine Learning algorithm in educational data

Abstract

Educational Data Mining (EDM) is one of the concern areas of data mining used for gathering, analyzing, and presenting information. The purpose of this paper is to analyze online learners' activities to extract hidden information using clustering and classification techniques. The data were collected from learners enrolled in a MOOC course called C programming offered by Kathmandu University of Nepal. For clustering, K-means algorithm was used for grouping of the student with similar characteristic to understand the learners' behavior and for classification, Support Vector Machine (SVM) classifier was implemented to develop predictive model that predicts the students' performance labeled with a class such as low, medium and high. The extracted knowledge can be used by the academic institution to improve teaching and learning processes and improve learner's performance which consequently helps in academic achievement. This research helps in early identification of weak students such that timely decision making can be done to improve learner's performance and reduce online learner's dropout rates.