Lesson Engagement and Outlier Extraction Analytics Through Learning Log Classification

Abstract

With the spread of online distance learning and digital teaching materials, research analyzing the learning logs from learning management systems and uncovering knowledge that can be used to improve classes is becoming increasingly important. The interquartile method and the interquartile range were applied to the classification of learning patterns, the creation of cluster heat maps, and to outlier detection to improve the robustness of this research. Comparison of extraction methods also clarified that multiple methods were necessary to extract outliers. The analysis results of learning log classification, cluster heat mapping, and outlier detection can also be used to explain the basis of insufficient student effort when teachers make academic interventions. The experimental data showed that learners in need of academic intervention can be categorized by visualizing class engagement with a cluster heat map and outlier extraction.