MultiScaleAnalyzer for Spatiotemporal Learning Data Analysis: A Case Study of Eye-Tracking and Mouse Movement

Abstract

With the development of high-performance computers, cloud storage, and advanced sensors, people’s ability to gather complex learning data has greatly improved. However, analyzing these data remains a significant challenge. Especially for spatiotemporal learning data such as eye-tracking and mouse movement, understanding and analyzing these data to identify the learning insights behind them is a difficult task. We propose a visualization platform called “MultiScaleAnalyzer”, which employs hierarchical structure to illustrate spatiotemporal learning data in multiple views. From high-level overviews to detailed analyses, “MultiScaleAnalyzer” provides varying resolutions of data tailored to educators’ need. To demonstrate the platform’s effectiveness, we applied “MultiScaleAnalyzer” to a mathematical word problem-solving dataset, showcasing how the visualization platform facilitates the exploration of student problem-solving patterns and strategies.