Graduation Term
2021
Degree Name
Master of Science (MS)
Department
School of Information Technology: Information Systems
Committee Chair
Xing Fang
Abstract
Under the influence of the COVID-19 pandemic, traditional in-person teaching has undergone significant changes. Online courses become an essential education method. However, online teaching lacks adequate evaluation approaches. That's why exams are still indispensable. However, grading short answer exam questions can be an onerous task. In this work, we propose a novel Automatic Short Answer Grading (ASAG) model based on the Sentence BERT model. On the Short Answer Scoring V2.0 dataset, our proposed model shows improvements on accuracy, Marco F1 score, and Weighted F1 score comparing to the results obtained from the BERT model. In addition, we also compare different task functions and different lengths of answers to further evaluate our model’s performance. A better result is achieved when using the regression task function. At the same time, we find that shorter answers’ result is better than the result obtained from longer answers.
Access Type
Thesis-Open Access
Recommended Citation
Luo, Jinzhu, "Automatic Short Answer Grading Using Deep Learning" (2021). Theses and Dissertations. 1495.
https://ir.library.illinoisstate.edu/etd/1495
DOI
https://doi.org/10.30707/ETD2021.20220215070317697127.999986