Hefkvis: A visual analysis approach for exploring students’ online learning behavior

The analysis and evaluation of students’ online learning behaviors are crucial tasks in online education. Analyzing various behaviors during the learning process and obtaining the corresponding evaluation results can help teachers understand students’ learning conditions, adjust teaching strategies on time, and enhance the quality of teaching. However, the existing methods for evaluating learning behaviors are often based on one specific dimension and have difficulty analyzing student-learning behavior data simultaneously across multiple online platforms, leading to incomplete and inaccurate analysis results. In this paper, we propose a visual analysis pipeline based on a comprehensive scoring model of diverse features of online learning process data that is capable of detecting and analyzing learning behavior anomalies in various learning scenarios. We also developed a visual analysis system that demonstrates the effectiveness of our pipeline in multi-platform and multi-scenario learning behavior analysis. We illustrate the effectiveness and usability of the system through two usage scenarios and in-depth user interviews. • We proposed a method for detecting and analyzing abnormal learning behaviors based on visual analytics. • We introduced an analysis method for multiple learning scenarios based on user interaction, which further enhances the effectiveness of the learning behavior scoring model. • We designed HEFKVis, and demonstrated its effectiveness using two case studies.