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Title: #Confused and beyond: detecting confusion in course forums using students' hashtags
Students' confusion is a barrier for learning, contributing to loss of motivation and to disengagement with course materials. However, detecting students' confusion in large-scale courses is both time and resource intensive. This paper provides a new approach for confusion detection in online forums that is based on harnessing the power of students' self-reported affective states (reported using a set of pre-defined hashtags). It presents a rule for labeling confusion, based on students' hashtags in their posts, that is shown to align with teachers' judgement. We use this labeling rule to inform the design of an automated classifier for confusion detection for the case when there are no self-reported hashtags present in the test set. We demonstrate this approach in a large scale Biology course using the Nota Bene annotation platform. This work lays the foundation to empower teachers with better support tools for detecting and alleviating confusion in online courses.  more » « less
Award ID(s):
1915724
PAR ID:
10193681
Author(s) / Creator(s):
; ; ; ; ; ; ;
Date Published:
Journal Name:
LAK '20: Proceedings of the Tenth International Conference on Learning Analytics & Knowledge
Page Range / eLocation ID:
589 to 594
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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