Mentoring in the Open: A Strategy for Supporting Human Development in the Knowledge Society
Bibliographic record
Abstract
On-line mentoring, or between K-12 students and adult volunteers has proven a valuable way to enable ambitious classroom inquiry. However, past research has shown that simply placing mentoring opportunities at the disposal of all students is not enough to make telementoring effective on an equitable basis. When mentoring relationships are conducted via private media like e-mail, the students who most need to see models of successful mentoring are, in fact, the least likely to encounter them. In the worst case this leads to a rich get richer dynamic, in which only students with previous experience of supportive learning partnerships are able to draw benefit from them. In design experiments conducted in Toronto-area high schools, we orchestrated telementoring relationships in a Knowledge Forum™ database — an asynchronous, electronic group workspace. In this new model of telementoring, students could (and did) peek into the telementoring dialogues of their peers. This opportunistic model-seeking, as we call it, allowed students to develop more sophisticated ideas about the kinds of advice and guidance they wanted from their mentors, defeating the rich get richer dynamic. Our findings suggest that mentoring in the open may serve as a powerful component strategy for building equitable and sustainable on-line learning communities for participants of diverse age and expertise.
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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 | 0.008 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.008 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".