An Iterative Transfer Learning Based Ensemble Technique for Automatic Short Answer Grading
This work addresses the need for labeled data in ASAG, offering a solution for educators and researchers in automated assessment, though it is incremental as it builds on existing ensemble and transfer learning methods.
The paper tackles the limitations of supervised automatic short answer grading (ASAG) by introducing an iterative ensemble technique with transfer learning, which outperforms all winning supervised methods on the SCIENTSBANK dataset and shows generalizability across multiple datasets.
Automatic short answer grading (ASAG) techniques are designed to automatically assess short answers to questions in natural language, having a length of a few words to a few sentences. Supervised ASAG techniques have been demonstrated to be effective but suffer from a couple of key practical limitations. They are greatly reliant on instructor provided model answers and need labeled training data in the form of graded student answers for every assessment task. To overcome these, in this paper, we introduce an ASAG technique with two novel features. We propose an iterative technique on an ensemble of (a) a text classifier of student answers and (b) a classifier using numeric features derived from various similarity measures with respect to model answers. Second, we employ canonical correlation analysis based transfer learning on a common feature representation to build the classifier ensemble for questions having no labelled data. The proposed technique handsomely beats all winning supervised entries on the SCIENTSBANK dataset from the Student Response Analysis task of SemEval 2013. Additionally, we demonstrate generalizability and benefits of the proposed technique through evaluation on multiple ASAG datasets from different subject topics and standards.