Question-type Identification for Academic Questions in Online Learning Platform
This work addresses content understanding for online learning platforms to enhance student experiences, but it is incremental as it applies existing methods to a specific domain.
The paper tackled question-type identification for academic questions in online learning platforms by defining 12 classes and training a BERT-based ensemble model, achieving an F1-score of 0.94 for MCQ binary classification.
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.