Tutor Messaging and Its Effectiveness in Encouraging Student Participation on Computer Conferences
Bibliographic record
Abstract
Following student requests for more frequent tutor intervention in academic computer conferences, research was conducted to discover whether different patterns of tutor intervention did in fact result in greater student activity, measured both by the number of student contributions and by instances of dialogue between tutor and student. The conclusion reached was that certain tutor behaviour could encourage participation where it might otherwise not take place, but that it was not a necessary precursor to student activity in all circumstances. En reponse aux demandes des etudiants souhaitant une intervention plus frequente des tuteurs aux teleconferences informatisees, une recherche a ete effectuee afin de determiner si divers modeles d'interventions des tuteurs encourageaient effectivement la participation etudiante. Le niveau de participation a ete evalue en fonction du nombre de contributions des etudiants et d'echanges entre tuteur et etudiants. Les conclusions ont revele que certains comportements de la part des tuteurs pouvaient en effet encourager la participation des etudiants, qui autrement ne prendraient pas part au dialogue, mais ce changement de comportement de la part du tuteur n'etait toutefois pas l'unique facteur.
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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 | 0.007 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".