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Analyzing Student Success and Mistakes in Virtual Microscope Structure Search Tasks

Many modern anatomy curricula teach histology using virtual microscopes, where students inspect tissue slices in a computer program (e.g. a web browser). However, the educational data mining (EDM) potential of these virtual microscopes remains under-utilized. In this paper, we use EDM techniques to investigate three research questions on a virtual microscope dataset of N = 1 , 460 students. First, which factors predict the success of students locating structures in a virtual microscope? We answer this question with a generalized item response theory model (with 77% test accuracy and 0 . 82 test AUC in 10-fold cross-validation) and ﬁnd that task diﬃculty is the most predictive parameter, whereas student ability is less predictive, prior success on the same task and exposure to an explanatory slide are moderately predictive, and task duration as well as prior mistakes are not predictive. Second, what are typical locations of student mistakes? And third, what are possible misconceptions explaining these locations? A clustering analysis revealed that student mistakes for a diﬃcult task are mostly located in plausible positions (’near misses’) whereas mistakes in an easy task are more indicative of deeper misconceptions.