The Untapped Learning Potential of CMC in Higher Education
Bibliographic record
Abstract
We argue for programs that support academics to develop an understanding of the relationship between technology and pedagogy. To lay the groundwork, we document how nine instructors (in biology, education, English, general studies, geography, and kinesiology) at two universities integrated a computer conferencing tool into their course design and how their students reported actually using the tool. Among our findings was that most instructors intended students to use computer conferencing for learning of course content and to meet this goal three types of interactions were written into the course design: unidirectional, bidirectional, and co-constructive online interactions. The data was further considered from the perspective of Van Aalst’s framework (2006), which provides a way to build a “communal online learning resource in terms of three notions: collaboration, learning how to learn and idea improvement” (p. 279). Implications are drawn for working with faculty to design online instruction.
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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 | 0.001 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".