Cooperation and Collaboration in Higher Education
Bibliographic record
Abstract
This study illustrates different practices of knowledge co-construction by exploring argumentation processes from its (1) cognitive, (2) affective, and (3) moral dimensions (respect), and by clarifying the meaning of cooperation and collaboration, terms that are commonly used as synonyms. The authors adopted a critical constructivist approach consistent with the cognitive and moral works of Habermas (1987) and Piaget (1977, 1932/2000), and refined a method of online argumentation analysis (Campos, 2004) to better understand knowledge co-construction in the context of electronic conferencing in university courses. Their data analysis focused on the form as well as on the content of online argumentation. Results concerning the cognitive dimension of online discourse confirmed previous studies. However, regarding the affective and moral (respect) dimensions of online discourse, results were less clear. The authors highlight that the technology, the course design, and the instructor’s actions are equally important to successfully achieve set goals in online learning communities.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".