Bibliographic record
Abstract
Abstract Online mentoring, or “telementoring,” is increasingly used in schools as a way to link students’ schoolwork with that of adult communities of practice and open their work to a wider responsive audience. Maintaining participants’ interest and engagement is vital in telementoring relationships, but little empirical research has directly addressed the question of what rewards participants (particularly the mentees) expect as a result. What do the participants themselves think of as success? This study developed a methodological approach to assessing mentees’ expectations and how they change. We used a variety of quantitative methods to examine possible influences on 72 adolescents’ perceptions of success in a curriculum-based telementoring program, Tracking Canada’s Past. Findings indicate that among the variables measured for this study, students’ judgments of success were best predicted by the mentors’ helpfulness in asking questions about their ongoing research, recommending reading materials, and working with the students to develop research questions or topics, along with the students’ enthusiasm for the online discussion space in which they worked with their mentors and their trust of their mentors. Further analysis examined how students’ initial desires for particular mentoring functions differed from their mentors’ initial desires to provide them, and the changes that took place in students’ expectations of specific mentoring functions between the beginning and end of the program.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".