ArticleJournal of pathology informatics2015
Exploring viewing behavior data from whole slide images to predict correctness of students' answers during practical exams in oral pathology.
Article in Journal of pathology informatics, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
4 citing papers in PubMed.
- Models and Metrics for Medical Education in Urology: Medical Student Education, Resident Education, and Future Directions.Current urology reports · 2025Review
- Review
- Applications and Challenges of Implementing Artificial Intelligence in Medical Education: Integrative Review.JMIR medical education · 2019Review
- RecutClub.com: An Open Source, Whole Slide Image-based Pathology Education System.Journal of pathology informatics · 2017Article
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The way of viewing whole slide images (WSI) can be tracked and analyzed. In particular, it can be useful to learn how medical students view WSIs during exams and how their viewing behavior is correlated with correctness of the answers they give. We used software-based view path tracking method that enabled gathering data about viewing behavior of multiple simultaneous WSI users. This approach was implemented and applied during two practical exams in oral pathology in 2012 (88 students) and 2013 (91 students), which were based on questions with attached WSIs. Gathered data were visualized and analyzed in multiple ways. As a part of extended analysis, we tried to use machine learning approaches to predict correctness of students' answers based on how they viewed WSIs. We compared the results of analyses for years 2012 and 2013 - done for a single question, for student groups, and for a set of questions. The overall patterns were generally consistent across these 3 years. Moreover, viewing behavior data appeared to have certain potential for predicting answers' correctness and some outcomes of machine learning approaches were in the right direction. However, general prediction results were not satisfactory in terms of precision and recall. Our work confirmed that the view path tracking method is useful for discovering viewing behavior of students analyzing WSIs. It provided multiple useful insights in this area, and general results of our analyses were consistent across two exams. On the other hand, predicting answers' correctness appeared to be a difficult task - students' answers seem to be often unpredictable.
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Registered trials
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