Roles and student identities in online large course forums: Implications for practice
Bibliographic record
Abstract
The use of large online discussion forums within online and distance learning continues to grow. Recent innovations in online learning such as the MOOC (massive open online course) and concomitant growth in the use of online media for the delivery of courses in traditional campus based universities provide both opportunity and challenge for online tutors and learners alike. The recognition of the role that online tutors and student identity plays in the field of retention and progression of distance learners is also well documented in the field of distance learning. Focusing on a course forum linked to a single Level 2 undergraduate module and open to over 1,000 students, this ideographic case study, set in a large distance learning university, uses qualitative methodology to examine the extent to which participation in a large forum can be considered within community of practice (COP) frameworks and contributes to feelings of efficacy, student identity, and motivation. The paper draws on current theory pertaining to online communities and examines this in relation to the extent to which the forum adds to feelings of academic and social integration. The study concludes that although the large forum environment facilitates a certain degree of academic integration and identity there is evidence that it also presents a number of barriers producing negative effects on student motivation and online identity.
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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 | 0.101 | 0.125 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.008 | 0.012 |
| Scholarly communication | 0.014 | 0.017 |
| Open science | 0.004 | 0.011 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".