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Diagnostic reasoning is a key component of many professions. To improvestudents' diagnostic reasoning skills, educational psychologists analyse andgive feedback on epistemic activities used by these students while diagnosing,in particular, hypothesis generation, evidence generation, evidence evaluation,and drawing conclusions. However, this manual analysis is highlytime-consuming. We aim to enable the large-scale adoption of diagnosticreasoning analysis and feedback by automating the epistemic activityidentification. We create the first corpus for this task, comprising diagnosticreasoning self-explanations of students from two domains annotated withepistemic activities. Based on insights from the corpus creation and the task'scharacteristics, we discuss three challenges for the automatic identificationof epistemic activities using AI methods: the correct identification ofepistemic activity spans, the reliable distinction of similar epistemicactivities, and the detection of overlapping epistemic activities. We propose aseparate performance metric for each challenge and thus provide an evaluationframework for future research. Indeed, our evaluation of variousstate-of-the-art recurrent neural network architectures reveals that currenttechniques fail to address some of these challenges.

Challenges in the Automatic Analysis of Students' Diagnostic Reasoning