  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.
