Explainable Automatic Grading with Neural Additive Models
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.
Code (0)
등록된 구현이 없습니다.
Tasks
Additive modelsautomatic short answer gradingFeature EngineeringMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Enhancing Multi-Domain Automatic Short Answer Grading through an Explainable Neuro-Symbolic Pipeline
Grading short answer questions automatically with interpretable reasoning behind the grading decision is a challenging goal for current transformer approaches. Justification cue detection, in combination with logical rea…
automatic short answer gradingEnsemble ToT of LLMs and Its Application to Automatic Grading System for Supporting Self-Learning
Providing students with detailed and timely grading feedback is essential for self-learning. While existing LLM-based grading systems are promising, most of them rely on one single model, which limits their performance. …
Self-LearningExplainable Artificial Intelligence in Retinal Imaging for the detection of Systemic Diseases
Explainable Artificial Intelligence (AI) in the form of an interpretable and semiautomatic approach to stage grading ocular pathologies such as Diabetic retinopathy, Hypertensive retinopathy, and other retinopathies on t…
DescriptiveExplainable artificial intelligenceOptic Disc DetectionCells are Actors: Social Network Analysis with Classical ML for SOTA Histology Image Classification
Digitization of histology images and the advent of new computational methods, like deep learning, have helped the automatic grading of colorectal adenocarcinoma cancer (CRA). Present automated CRA grading methods, howeve…
Deep Learningimage-classificationImage ClassificationExplainable deepfake and spoofing detection: an attack analysis using SHapley Additive exPlanations
Despite several years of research in deepfake and spoofing detection for automatic speaker verification, little is known about the artefacts that classifiers use to distinguish between bona fide and spoofed utterances. A…
Face SwappingSpeaker Verification