Bibliographic record
Abstract
This paper examines the contributors to a successful online educational program. In particular, it focuses on an online mentoring program called Tracking Canada's Past (TCP) which was implemented in three high schools in British Columbia, Canada, in 2003. Tracking Canada's Past investigated the application of online mentoring in the high school history curriculum through the use of Knowledge Forum® software–a web-based group workspace in which students could share and discuss their ongoing research with their online mentors and other students. The goal of TCP project was to help students understand the concept of history as a discipline through online mentoring and the use of “primary” sources, in addition to standard textbooks. There were 72 students and 16 online mentors involved in this study, approximately one mentor for each group of 5-11 students. Through a series of pre- and post-program surveys and interviews, data were collected on the students' backgrounds, expectations for specific mentoring functions, affective responses to mentoring, and the mentoring functions they recognized receiving. Volunteer mentors were also asked about the mentoring functions and the type of advice they would offer to their students. Findings from this study indicated that students' judgments of a successful online mentoring program were best predicted by the helpfulness of the questions mentors asked, the usefulness of the reading materials and/or web resources they recommended, the helpfulness of mentors in developing questions or ideas to investigate, the level of trust students placed in their mentors, and the helpfulness of the online workspace where students and mentors shared their ideas. These findings suggest that the most important determinants of a successful online mentoring program are those that online program designers have the ability to refine over time.
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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 | 0.021 | 0.003 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".