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Published on: June 20, 2020
A longitudinal evaluation of an educational software program: a case study of Urinalysis-Tutor
1Department of Family Medicine, University of Washington School of Medicine, Seattle, 98195, USA. sarakim@u.washington.edu.
Summary
Revising educational software showed minimal overall performance changes, but improved learning in two key concepts. Analyzing student interaction patterns revealed a link between specific feature use and better performance.
Area of Science:
- Medical Education
- Educational Technology
- Human-Computer Interaction
Background:
- Educational software is increasingly used in medical schools.
- Understanding how students interact with educational software is crucial for optimizing learning outcomes.
- Previous versions of educational software may not fully leverage interactive features for concept acquisition.
Purpose of the Study:
- To evaluate the impact of revising an educational software program on medical student learning.
- To analyze student usage patterns of an interactive image comparison feature.
- To correlate software usage with learning performance.
Main Methods:
- Comparative analysis of pre- and post-test scores from medical students using original (1996/1997) and revised (1998) software versions.
- Observational data and program-tracked navigational pathways were collected to analyze student interaction.
- Statistical analysis was performed on performance data and usage patterns.
Main Results:
- Overall student performance showed negligible differences between the original and revised software versions.
- Error analysis indicated significant learning improvements in two out of eleven conceptual areas after software revision.
- Navigational data revealed varied usage patterns for the image comparison feature, with a positive association between performance and anchored viewing mode.
Conclusions:
- While specific design changes' impact on learning was inconclusive, linking usage data with performance offers valuable insights.
- Future research should focus on design factors influencing student interaction patterns and learning outcomes.
- Integrating navigational data analysis into software development can inform future educational technology design.
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