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. In this paper, we tackle this issue by implementing explainable AI methods for black-box neural networks. This work focuses on the context of online and blended learning and the use case of student success prediction models. We use a pairwise study design, enabling us to investigate controlled differences between pairs of courses. 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 quantitatively compare the distances between the explanations across courses and methods. 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. 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. All code, extended results, and the interview protocol are provided at https://github.com/epfl-ml4ed/trusting-explainers.

翻译：深度学习模型在近年学习分析领域日益普及；然而，由于缺乏信任与透明性，这类方法在实际场景中仍未被广泛采用。本文通过为黑箱神经网络实现可解释AI方法来解决这一问题，重点关注在线与混合式学习情境下的学生成功预测模型用例。我们采用成对研究设计，能够系统考察课程组之间的受控差异。分析涵盖五组在教育维度上存在显著差异的课程对，并应用两种主流的实例级可解释AI方法（LIME与SHAP）。通过量化比较跨课程与跨方法的解释结果差异，我们进一步对LIME和SHAP的解释效果进行验证：基于26次针对高校教师的半结构化访谈，探讨他们认为哪些特征最影响学生成功、最信任哪些解释方法，以及如何将这些洞察转化为可操作的课程设计决策。结果表明，定量分析显示不同解释器对关键特征存在显著分歧，而质性分析表明专家自身对最可信解释的认知也缺乏统一性。所有代码、扩展结果及访谈协议均可在 https://github.com/epfl-ml4ed/trusting-explainers 获取。