Cross-course Process Mining of Student Clickstream Data -- Aggregation and Group Comparison

TL;DR

This paper presents new methods for analyzing student clickstream data from Moodle to enable cross-course process mining, revealing broader engagement patterns and student behaviors across multiple courses.

Contribution

It introduces an automated SQL-based data extraction process, aggregation techniques, and a standardization method for cross-course analysis of Moodle student interaction data.

Findings

Higher-performing students engage more with activities.

Students show more dynamic movement in section-level analysis.

Cross-course aggregation reveals broader usage patterns.

Abstract

This paper introduces novel methods for preparing and analyzing student interaction data extracted from course management systems like Moodle to facilitate process mining, like the creation of graphs that show the process flow. Such graphs can get very complex as Moodle courses can contain hundreds of different activities, which makes it difficult to compare the paths of different student cohorts. Moreover, existing research often confines its focus to individual courses, overlooking potential patterns that may transcend course boundaries. Our research addresses these challenges by implementing an automated dataflow that directly queries data from the Moodle database via SQL, offering the flexibility of filtering on individual courses if needed. In addition to analyzing individual Moodle activities, we explore patterns at an aggregated course section level. Furthermore, we present a method for standardizing section labels across courses, facilitating cross-course analysis to uncover broader usage patterns. Our findings reveal, among other insights, that higher-performing students demonstrate a propensity to engage more frequently with available activities and exhibit more dynamic movement between objects. While these patterns are discernible when analyzing individual course activity-events, they become more pronounced when aggregated to the section level and analyzed across multiple courses.