We explore the use of deep reinforcement learning to audit an automatic short
answer grading (ASAG) model. Automatic grading may decrease the time burden of
rating open-ended items for educators, but a lack of robust evaluation methods
for these models can result in uncertainty of their quality. Current
state-of-the-art ASAG models are configured to match human ratings from a
training set, and researchers typically assess their quality with accuracy
metrics that signify agreement between model and human scores. In this paper,
we show that a high level of agreement to human ratings does not give
sufficient evidence that an ASAG model is infallible. We train a reinforcement
learning agent to revise student responses with the objective of achieving a
high rating from an automatic grading model in the least number of revisions.
By analyzing the agent's revised responses that achieve a high grade from the
ASAG model but would not be considered a high scoring responses according to a
scoring rubric, we discover ways in which the automated grader can be
exploited, exposing shortcomings in the grading model.

探索使用深度强化学习来审计自动短答案评分（ASAG）模型，以揭示其质量不可靠的问题。通过培训一个强化学习代理来修改学生的回答，以获得自动评分模型的高分，并发现自动评分模型的潜在缺陷。

深度强化学习下的自动评分模型审计

Auditing an Automatic Grading Model with deep Reinforcement Learning

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.

使用自动短答案评分（ASAG）模型可以减轻评分的时间负担，同时鼓励教育者经常在课程中引入开放性问题。然而，目前最先进的 ASAG 模型是大型神经网络（NN），常被描述为 “黑匣子”，对于生成的输出哪些特征是重要的没有解释。为了创建一个强大且可解释的 ASAG 模型，我们尝试使用一种称为神经可添加模型（NAM）的模型类型，将 NN 的性能与可解释性模型的性能结合起来。我们使用学习科学中的知识整合（KI）框架指导特征工程，创建反映学生回答中是否包含某些思想的输入。我们假设指示预定义思想的包含（或排除）作为特征将足以使 NAM 具有良好的预测能力和可解释性，因为这可以指导使用 KI 评分标准的人工评分人员。我们使用相同的特征比较 NAM 和另一个可解释模型逻辑回归的性能，以及不需要特征工程的不可解释的神经模型 DeBERTa。