iSAT: a visual learning analytics tool for instructors.

Area of Science:

• Educational Technology
• Data Visualization
• Learning Analytics

Background:

• Traditional analysis of teaching-learning data often relies on static visualizations like histograms.
• Instructors need dynamic tools to understand student engagement and learning dynamics during activities.
• Peer Instruction (PI) activities generate valuable data but often lack real-time, in-depth analysis.

Purpose of the Study:

• To introduce Interactive Stratified Attribute Tracking (iSAT) as a visual analytics tool for cohort analysis in educational settings.
• To demonstrate how iSAT can visualize student transitions during teaching-learning activities, specifically in Peer Instruction.
• To explore the utility and usability of iSAT for instructors to analyze real-time student response data.

Main Methods:

• Developed and applied Interactive Stratified Attribute Tracking (iSAT), a visual analytics tool.
• Utilized iSAT to analyze clicker response data from a Peer Instruction activity in real-time.
• Identified and interpreted seven distinct student transition patterns: aligned, returns, starburst, slide, attractor, switching, and void.

Main Results:

• iSAT enables real-time visualization of student transitions, offering insights beyond traditional histograms.
• Seven unique transition patterns were identified, providing a nuanced understanding of student engagement and learning.
• Workshop participants reported usefulness and usability of iSAT for analyzing their own teaching data.

Conclusions:

• Interactive Stratified Attribute Tracking (iSAT) empowers instructors with advanced cohort analysis capabilities.
• The tool facilitates real-time understanding of student dynamics, supporting informed pedagogical decisions.
• iSAT enhances the analysis of teaching-learning activities by visualizing complex transition patterns.