Group Processes Supporting the Development of Progressive Discourse in Online Graduate Courses
Bibliographic record
Abstract
This design-based research study investigates the development of progressive discourse among participants (n=15, n=17, n=20) in three online graduate course contexts. Progressive discourse is a kind of discourse for inquiry in which participants share, question, and revise their ideas to deepen understanding and build knowledge. Although progressive discourse is central to knowledge building pedagogy, it is not known whether it is possible to detect its emergence in the patterns of participation in asynchronous conferencing environments or what kinds of instructional scaffolding are most effective to support its development. This study offers a unique perspective by characterizing episodes of discourse where participants honor the commitments for progressive discourse and by refining designs of peer and software-based scaffolding for progressive discourse.\nResults showed that measures such as note count, replies, and thread sizes can determine some qualities of online discourse but do not shed light on the development of progressive discourse. Thus an in-depth analysis of discourse for groups was developed to trace the interdependent individual contributions to the group discourse. Peer scaffolding that made norms for progressive discourse explicit was introduced to encourage participants to engage in sustained student-centered discourse for inquiry. Findings show that this intervention was most effective at the beginning of a course for newer online learners and newer graduate students, and least effective for students who were practicing K-12 teachers. A significant barrier to fostering progressive discourse is the tendency for teachers to reject these norms and revert to belief-mode thinking and devotional discourse typical of traditional schooling. Additionally, findings suggest that software-based scaffolding (as found in Knowledge Forum’s scaffold support feature) is a promising avenue for future design innovations to encourage progressive discourse.\nAlthough the results of this study are only suggestive, the findings do illustrate ways in which graduate students can uphold the commitments to move beyond expressions of socio- affective connection and opinion to discuss ideas in ways that lead to more useful explanations. The implications for these results for analyzing the quality of online discourse and the designs of instructional scaffolding in online learning environments are discussed.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.011 | 0.041 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".