All code, extended results, and the interview protocol are provided at. Our results show that quantitatively, explainers significantly disagree with each other about what is important, and qualitatively, experts themselves do not agree on which explanations are most trustworthy. We then validate the explanations of LIME and SHAP with 26 semi-structured interviews of university-level educators regarding which features they believe contribute most to student success, which explanations they trust most, and how they could transform these insights into actionable course design decisions. We quantitatively compare the distances between the explanations across courses and methods. Our analyses cover five course pairs that differ in one educationally relevant aspect and two popular instance-based explainable AI methods (LIME and SHAP). We use a pairwise study design, enabling us to investigate controlled differences between pairs of courses. This work focuses on the context of online and blended learning and the use case of student success prediction models. In this paper, we tackle this issue by implementing explainable AI methods for black-box neural networks. The two men head a volunteer effort to host a. Deep learning models for learning analytics have become increasingly popular over the last few years however, these approaches are still not widely adopted in real-world settings, likely due to a lack of trust and transparency. Don Williams, left, and Robert Coats stand with Dodge and Max at the Coats family farm in Lauderdale County.
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