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arXiv · 2211.13727

Question-type Identification for Academic Questions in Online Learning Platform

Azam Rabiee·Alok Goel·Johnson D'Souza·Saurabh Khanwalkar
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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.

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Azam Rabiee, Alok Goel, Johnson D'Souza, Saurabh Khanwalkar. 2022-11-24. Question-type Identification for Academic Questions in Online Learning Platform. https://arxiv.org/abs/2211.13727

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