#Confused and beyond: Detecting confusion in course forums using students' hashtags

Shay A. Geller, Nicholas Hoernle, Kobi Gal, Avi Segal, Amy X. Zhang, David Karger, Marc T. Facciotti, Michele Igo

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

4 Scopus citations

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.

Original languageEnglish
Title of host publicationLAK 2020 Conference Proceedings - Celebrating 10 years of LAK
Subtitle of host publicationShaping the Future of the Field - 10th International Conference on Learning Analytics and Knowledge
PublisherAssociation for Computing Machinery
Pages589-594
Number of pages6
ISBN (Electronic)9781450377126
DOIs
StatePublished - 23 Mar 2020
Event10th International Conference on Learning Analytics and Knowledge: Shaping the Future of the Field, LAK 2020 - Frankfurt, Germany
Duration: 23 Mar 202027 Mar 2020

Publication series

NameACM International Conference Proceeding Series

Conference

Conference10th International Conference on Learning Analytics and Knowledge: Shaping the Future of the Field, LAK 2020
Country/TerritoryGermany
CityFrankfurt
Period23/03/2027/03/20

Keywords

  • Confusion Detection
  • Emojis
  • Hashtags
  • Online Discussion Forum
  • Self-Reported Affect
  • Text Classification

ASJC Scopus subject areas

  • Software
  • Human-Computer Interaction
  • Computer Vision and Pattern Recognition
  • Computer Networks and Communications

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