|Published online: June 26, 2014||$US10.00|
|Published online: June 26, 2014||$US5.00|
By utilizing data mining techniques, this study analyzes e-learning habits to trace individual learning paths in order to develop more personalized digital learning environments. Unlike previous studies, the focus is on e-learning materials of theoretical subjects, not on mathematical contents. The ReadIT learning program enables students to access a variety of factual texts, learning tasks, and tests, regardless of time and space. The ReadIT software supports cooperative writing activities between students and their peers or teachers. It also offers the opportunity to monitor students' transitions and performance in real time, which supplies up-to-date information for the teachers about their students' learning. Log data consisting of 47,773 events is gathered from students' interactions with the web-based ReadIT time-use. The amount of transitions and performance scores are the key variables in identifying distinctive behavioral clusters to indicate differences in students' learning strategies. These clusters, in turn, are utilized in tracing relevant learning paths to sieve out the successful and the less successful behavior habits in learners' interactions with the e-learning system. The results show significant differences in e-learning habits between high and low performers and between genders.
|Keywords:||Log Data Analysis, e-Learning Habits, Individual Learning Paths|
Ubiquitous Learning: An International Journal, Volume 6, Issue 2, August 2014, pp.15-26. Published online: June 26, 2014 (Article: Print (Spiral Bound)). Published online: June 26, 2014 (Article: Electronic (PDF File; 458.290KB)).
Director, Research Unit for the Sociology of Education (RUSE), University of Turku, Turku, Finland
Researcher, Research Unit for the Sociology of Education (RUSE), University of Turku, Turku, Finland