Fine grained analysis of students’ online discussion posts

Abstract

Collaborative discussions should engage all students, not just a few who dominate (“leaders”) while others participate as “followers” (Zhu, 2006). Cunningham (1991) noted that collaborating learners bring, discuss and debate multiple perspectives to develop their own position while acknowledging others' views. Higher levels of knowledge construction emerged when posts stimulated frequent reply by multiple participants (Aviv, Erlich, Ravid, & Geva, 2003) and were strongly content- and task-oriented (Rovai, 2007). So, to help students more actively and productively engage in knowledge-constructing discussions, an instructor needs to detect students' posts that do not stimulate replies, identify content those posts introduce, and guide students to revise posts to encourage peers' responses. However, such monitoring would be very time- and energy-consuming, especially in large-enrolment courses (Hura, 2010). To set a stage for developing a classifier to automate these tasks, we proposed 10 rhetorical moves characteristic of the interactive mode of Chi and Wylie's ICAP framework (2014) and categorized fine-grained content in discussion posts using these moves. We then identified attributes of posts that triggered a greater number of responses. Rhetorical moves of “asking questions,” “requesting justification,” “building-on,” “giving a reason” and “making a claim” triggered more peer responses. Posts with moves of “disagreeing,” “comparing” and “making claims” predicted students' achievement on a test and an argumentative writing task. We propose analytics for learners and instructors about forming and revising posts to promote constructive discussions and subsequent achievements.