构建可解释的神经网络，精准识别学生作弊行为。
A constraints-based approach to fully interpretable neural networks for detecting learner behaviors
- 通过约束设计使模型推理过程透明化
- 准确检测出系统作弊行为并匹配专家判断
- 适合教育AI可解释性研究者参考
复杂机器学习模型在教育领域的应用日益广泛，但其可解释性问题引发关注。本文提出一种基于约束的设计方法，构建可解释的神经网络行为检测模型。该模型参数具有明确含义，完整捕捉学习行为知识，且能生成忠实可信、人类可理解的解释。通过施加一系列约束，简化模型推理过程，使其更贴近人类对任务的认知。我们在检测系统作弊行为的任务上训练并评估模型，结果表明模型成功学习到相关行为模式，并与人类专家识别结果高度一致。研究还探讨了该方法的潜在影响，建议采用以人类为基准的可解释性评估方式。
原文摘要 · Abstract (English)
The increasing use of complex machine learning models in education has led to concerns about their interpretability, which in turn has spurred interest in developing explainability techniques that are both faithful to the model's inner workings and intelligible to human end-users. In this paper, we describe a novel approach to creating a neural-network-based behavior detection model that is interpretable by design. Our model is fully interpretable, meaning that the parameters we extract for our explanations have a clear interpretation, fully capture the model's learned knowledge about the learner behavior of interest, and can be used to create explanations that are both faithful and intelligible. We achieve this by implementing a series of constraints to the model that both simplify its inference process and bring it closer to a human conception of the task at hand. We train the model to detect gaming-the-system behavior, evaluate its performance on this task, and compare its learned patterns to those identified by human experts. Our results show that the model is successfully able to learn patterns indicative of gaming-the-system behavior while providing evidence for fully interpretable explanations. We discuss the implications of our approach and suggest ways to evaluate explainability using a human-grounded approach.
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