Predicting learner's project performance with dialogue features in online Q&A discussions

Jaebong Yoo, Jihie Kim

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

19 Scopus citations

Abstract

Although many college courses adopt online tools such as Q&A online discussions, there is no easy way to evaluate their impact on learning. In this paper, we investigate a predictive relation between characteristics of discussion contributions and student performance. For the modeling dynamics of conversational dialogue, speech acts (Q&A dialog roles that participants play) and emotional features covered by LIWC (Linguistic Inquiry and Word Count) were used. These dialogue information is used for correlation and regression analyses for predicting the performance of learners (173 student groups). Our current results indicate that the number of answers provided to others, the degree of positive emotion expressions, and how early students exchange information before the deadline correlate with project grades. This finding confirms the argument that in assessing student online activities, we need to capture how they interact, not just what they produce.

Original languageEnglish
Title of host publicationIntelligent Tutoring Systems - 11th International Conference, ITS 2012, Proceedings
Pages570-575
Number of pages6
DOIs
StatePublished - 2012
Event11th International Conference on Intelligent Tutoring Systems, ITS 2012 - Chania, Crete, Greece
Duration: 14 Jun 201218 Jun 2012

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume7315 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference11th International Conference on Intelligent Tutoring Systems, ITS 2012
Country/TerritoryGreece
CityChania, Crete
Period14/06/1218/06/12

Keywords

  • group projects
  • Online discussions
  • speech act classifiers

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