The use of automatic short answer grading (ASAG) models may help alleviate the time burden of grading while encouraging educators to frequently incorporate open-ended items in their curriculum. However, current state-of-the-art ASAG models are large neural networks (NN) often described as "black box", providing no explanation for which characteristics of an input are important for the produced output. This inexplicable nature can be frustrating to teachers and students when trying to interpret, or learn from an automatically-generated grade. To create a powerful yet intelligible ASAG model, we experiment with a type of model called a Neural Additive Model that combines the performance of a NN with the explainability of an additive model. We use a Knowledge Integration (KI) framework from the learning sciences to guide feature engineering to create inputs that reflect whether a student includes certain ideas in their response. We hypothesize that indicating the inclusion (or exclusion) of predefined ideas as features will be sufficient for the NAM to have good predictive power and interpretability, as this may guide a human scorer using a KI rubric. We compare the performance of the NAM with another explainable model, logistic regression, using the same features, and to a non-explainable neural model, DeBERTa, that does not require feature engineering.

翻译：自动简答评分（ASAG）模型的运用可能有助于减轻评分的时间负担，同时鼓励教育工作者在课程中频繁纳入开放式题目。然而，当前最先进的ASAG模型通常是被称为"黑箱"的大型神经网络（NN），无法解释输入中哪些特征对输出结果至关重要。这种不可解释性在师生试图解读或学习自动生成的分数时会造成困扰。为构建强大且可解释的ASAG模型，我们实验了一种名为神经加性模型（Neural Additive Model）的模型类型，该模型结合了神经网络的性能与加性模型的可解释性。我们采用学习科学中的知识整合（KI）框架指导特征工程，创建能反映学生回答中是否包含特定概念的输入特征。我们假设，将预定义概念的包含（或排除）作为特征进行标注，足以使NAM具备良好的预测能力与可解释性，因为这可能引导评分者使用KI评分标准。我们使用相同特征将NAM与另一种可解释模型逻辑回归进行性能对比，并与无需特征工程的不可解释神经模型DeBERTa进行对比。