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Online learning platforms provide learning materials and answers to students'academic questions by experts, peers, or systems. This paper exploresquestion-type identification as a step in content understanding for an onlinelearning platform. The aim of the question-type identifier is to categorizequestion types based on their structure and complexity, using the questiontext, subject, and structural features. We have defined twelve question-typeclasses, including Multiple-Choice Question (MCQ), essay, and others. We havecompiled an internal dataset of students' questions and used a combination ofweak-supervision techniques and manual annotation. We then trained a BERT-basedensemble model on this dataset and evaluated this model on a separatehuman-labeled test set. Our experiments yielded an F1-score of 0.94 for MCQbinary classification and promising results for 12-class multilabelclassification. We deployed the model in our online learning platform as acrucial enabler for content understanding to enhance the student learningexperience.

Question-type Identification for Academic Questions in Online Learning Platform