Content analysis schemes to analyze transcripts of online asynchronous discussion groups: A review
Computers & Education - Methodological issue in researching CSCL
Procrastination, participation, and performance in online learning environments
Computers & Education
Predicting student knowledge level from domain-independent function and content words
ITS'10 Proceedings of the 10th international conference on Intelligent Tutoring Systems - Volume Part II
Exploring asynchronous and synchronous tool use in online courses
Computers & Education
Capturing programming content in online discussions
Proceedings of the seventh international conference on Knowledge capture
Predicting students' final performance from participation in on-line discussion forums
Computers & Education
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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.