Data Mining and Student e-Learning Profiles

Abstract

Data mining techniques have been applied to educational research in various ways. In this paper, I presented the application of sequential data mining algorithms to analyze computer logs to profile learners in terms of their learning tactic use and motivation in a web-based learning environment (gStudy). The data mining algorithms are employed to discover patterns which characterize learners either across session or groups based on their study tactic choice and goal orientation. The use of this method is illustrated through a sequential pattern analysis of gStudy log files generated by university students.