@inproceedings{644c99843a2544719f6d6b421db5aa80,
title = "#Confused and beyond: Detecting confusion in course forums using students' hashtags",
abstract = "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.",
keywords = "Confusion Detection, Emojis, Hashtags, Online Discussion Forum, Self-Reported Affect, Text Classification",
author = "Geller, {Shay A.} and Nicholas Hoernle and Kobi Gal and Avi Segal and Zhang, {Amy X.} and David Karger and Facciotti, {Marc T.} and Michele Igo",
note = "Publisher Copyright: {\textcopyright} 2020 Association for Computing Machinery.; 10th International Conference on Learning Analytics and Knowledge: Shaping the Future of the Field, LAK 2020 ; Conference date: 23-03-2020 Through 27-03-2020",
year = "2020",
month = mar,
day = "23",
doi = "10.1145/3375462.3375485",
language = "English",
series = "ACM International Conference Proceeding Series",
publisher = "Association for Computing Machinery",
pages = "589--594",
booktitle = "LAK 2020 Conference Proceedings - Celebrating 10 years of LAK",
}