Fine grained analysis of students' online discussion posts. (November 2020)
- Record Type:
- Journal Article
- Title:
- Fine grained analysis of students' online discussion posts. (November 2020)
- Main Title:
- Fine grained analysis of students' online discussion posts
- Authors:
- Raković, Mladen
Marzouk, Zahia
Liaqat, Amna
Winne, Philip H.
Nesbit, John C. - Abstract:
- 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"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. Highlights: Providing both frequent and on topic posts is important to advance knowledge construction in online discussions. We partitioned students' posts into idea units and developed a 10-category coding scheme inspired by the ICAP framework. Asking questions, requesting justification and building-on trigger more peer responses that contribute to the discussion. Disagreeing, comparing and making claims predict students' achievement on argumentative writing task and achievement test. … (more)
- Is Part Of:
- Computers & education. Volume 157(2020)
- Journal:
- Computers & education
- Issue:
- Volume 157(2020)
- Issue Display:
- Volume 157, Issue 2020 (2020)
- Year:
- 2020
- Volume:
- 157
- Issue:
- 2020
- Issue Sort Value:
- 2020-0157-2020-0000
- Page Start:
- Page End:
- Publication Date:
- 2020-11
- Subjects:
- Cooperative/collaborative learning -- Distance education and online learning -- Post-secondary education
Education -- Data processing -- Periodicals
Education -- Periodicals
Computers -- Periodicals
Computer-Assisted Instruction -- Periodicals
Éducation -- Informatique -- Périodiques
Electronic journals
370.285 - Journal URLs:
- http://www.sciencedirect.com/science/journal/03601315 ↗
http://www.elsevier.com/journals ↗ - DOI:
- 10.1016/j.compedu.2020.103982 ↗
- Languages:
- English
- ISSNs:
- 0360-1315
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library DSC - 3394.677000
British Library DSC - BLDSS-3PM
British Library HMNTS - ELD Digital store - Ingest File:
- 13815.xml