International Journal of Applied Information Systems (IJAIS)
Predicting academic performance and monitoring the progress of students in a web based learning environment is a critical issue. In this paper, k-means clustering algorithm is implemented to predict student performance at the end of the semester. The results can be used to enhance the understanding of the course instructor to reform the syllabus, thereby increasing the chances of a higher score by lagging students. Higher education institutes offering distance learning courses through web can use this model to identify which area of their course can be improved by data mining technology to achieve higher student marks.