  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.
