Bibliographic record
Abstract
Abstract Online discourse environments are increasingly popular both in distance education contexts and as adjuncts to face-to-face learning. For many participants such contexts are experienced as positive, community-supported learning opportunities, but this is not the case for everyone. Understanding more about the online and off-line factors that contribute to the online experience is important in order to support equitable online learning. This study has analysed patterns of engagement and disengagement in one particular learning context; that of pre-service, math-anxious elementary candidates enrolled in a two-year pre-service program. Program supports for the self-declared math-anxious participants (n = 20 from a total cohort of 57) included small-group math investigations and participation in an online learning environment. Results show tremendous variability in levels of contribution and that the online context provided most learning support for participants who had had successful social and subject-related experiences in the program. Those with fewer successful face-to-face experiences who espoused an ability-based notion of subject matter, and who felt less able to contribute substantively, participated less online. As well, patterns of participation were established rapidly and were hard to change.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".