Learning pattern classification using moodle logs and the visualization of browsing processes by time-series cross-section

Abstract

In recent years, distance learning using learning management and e-book systems has been actively conducted in higher education institutions and various other organizations. It is possible to collect and analyze learning logs even in classes with many learners, including clickstreams and quiz scores in detail for each individual. This research proposes using Moodle's learning logs to classify learning patterns and outliers in order to identify struggling learners. The proposed method uses the descriptive statistics between the learner's teaching material clickstream and the final test score accumulated in Moodle, and students can be classified into four learning patterns. The frequency of each learning pattern was correlated with the appearance of outliers in the final test score and the teaching material clickstream. Most learners moved through four learning patterns during the weekly lessons, however, some learners scoring at the top and bottom of the weekly quiz scores repeated the same learning patterns. There was a tendency to correspond to an outlier due to the repetition of the same learning pattern. The time-series learning analytics of the teaching material clickstream revealed that learners with low final test scores and abnormal values tended to fall under a learning pattern with a smaller teaching material clickstream and a smaller access outside class hours.