Problem-independent text analytics for real-time judgment of CSCL typed-chat dialogues
This experiment trained text classifiers to categorize some of the conversational behaviors that might be indicative of productive online student collaborative exercises. COMPS project exercises have students working together via typed chat, solving problems in small groups. Instructors oversee these conversations. Toward the goal of aiding the instructor in locating which conversations could benefit from intervention, this experiment applies text analytics to recognize when students are using substantive vocabulary, and when they are agreeing or disagreeing with other students. The text classifiers were built from student conversations from two different schools solving problems in two different subject areas, using a vocabulary of only the more common English words.
Glass, Michael; Jagvaral, Yesukhei; Bouman, Nathaniel; Graham, Emily; Kalafatis, Stamatina; Arndt, Lindsey; Desjarlais, Melissa; Kim, Jung Hee; and Bryant, Kelvin, "Problem-independent text analytics for real-time judgment of CSCL typed-chat dialogues" (2018). Computing and Information Sciences Faculty Publications. 6.