Question-type Identification for Academic Questions in Online Learning Platform
Abstract
Online learning platforms provide learning materials and answers to students' academic questions by experts, peers, or systems. This paper explores question-type identification as a step in content understanding for an online learning platform. The aim of the question-type identifier is to categorize question types based on their structure and complexity, using the question text, subject, and structural features. We have defined twelve question-type classes, including Multiple-Choice Question (MCQ), essay, and others. We have compiled an internal dataset of students' questions and used a combination of weak-supervision techniques and manual annotation. We then trained a BERT-based ensemble model on this dataset and evaluated this model on a separate human-labeled test set. Our experiments yielded an F1-score of 0.94 for MCQ binary classification and promising results for 12-class multilabel classification. We deployed the model in our online learning platform as a crucial enabler for content understanding to enhance the student learning experience.
Keywords
Cite
@article{arxiv.2211.13727,
title = {Question-type Identification for Academic Questions in Online Learning Platform},
author = {Azam Rabiee and Alok Goel and Johnson D'Souza and Saurabh Khanwalkar},
journal= {arXiv preprint arXiv:2211.13727},
year = {2022}
}
Comments
18 pages, 6 figures, 4th International Conference on Semantic & Natural Language Processing (SNLP 2023)