TrackThinkDashboard: Understanding Student Self-Regulated Learning in Programming Study

TL;DR

This paper introduces TrackThinkDashboard, a visualization tool that unifies students' web browsing and programming activities to illuminate self-regulated learning (SRL) in programming tasks. By collecting logs from TrackThinkTS and C2Room and fusing them into time-ordered sequences, the authors visualize problem-solving workflows with flowcharts and resource-use via pie charts. In a study with 33 Japanese university students, they identify five SRL patterns and show distinct differences between lecture-attending and non-attending participants in both strategy and success rates, demonstrating the tool's potential to support data-driven teacher interventions and student metacognitive development. The work contributes a practical framework for monitoring SRL in programming education and lays groundwork for real-time deployments, richer data sources, and broader evaluations of SRL skill development.

Abstract

In programming education, fostering self-regulated learning (SRL) skills is essential for both students and teachers. This paper introduces TrackThinkDashboard, an application designed to visualize the learning workflow by integrating web browsing and programming logs into one unified view. The system aims to (1) help students monitor and reflect on their problem-solving processes, identify knowledge gaps, and cultivate effective SRL strategies; and (2) enable teachers to identify at-risk learners more effectively and provide targeted, data-driven guidance. We conducted a study with 33 participants (32 male, 1 female) from Japanese universities, including individuals with and without prior programming experience, to explore differences in web browsing and coding patterns. The dashboards revealed multiple learning approaches, such as trial-and-error and trial-and-search methods, and highlighted how domain knowledge influenced the overall activity flow. We discuss how this visualization tool can be used continuously or in one-off experiments, consider associated privacy implications, and explore opportunities for expanding data sources to gain richer behavioral insights.

Figures (9)

• Figure 1: Overall workflow of TrackThinkDashboard.
• Figure 2: Application workflow. Visualization of flowchart and pie-chart.
• Figure 3: Experimental settings. Collect logs while solving scheme questions.
• Figure 4: Try-and-error student: Student receive an error response after compiling, and students try to compile before going back to the web search.
• Figure 5: Try-and-search student: Student receive an error response after compiling, and students return to the web browsing to find a solution before the following compilation.