Mentoring strategies in a project-based learning environment: A focus on self-regulation
Bibliographic record
Abstract
The main purpose of this Action Research investigation was to better understand how post-secondary faculty mentor self-regulatory behaviours in a project-based learning environment (PjBL). The secondary purpose was to understand how the Action Research process supported faculty in their mentoring. Lastly, understanding learner perceptions of being mentored and how the faculty’s mentoring of specific self-regulatory behaviors would align with the expectations of the video game industry, would provide a cross-section of intrigue into the investigation. The research context was the Master of Digital Media Program in Vancouver, Canada. The MDM Program specializes in providing learners, organized in project teams, the opportunity to work on real-world digital media projects. Three faculty mentors and three student teams participated in this study; each team was tasked with co-constructing video-game prototypes for three game companies over a four-month period. Pre-research interviews with established members of the video game industry in Vancouver were conducted in order to determine what qualities and skills they looked for when hiring new recruits. Data from these interviews revealed characteristics of self-regulation, such as self-motivation, ‘ownership’, the ability for recruits to manage their own learning, and self-reliance as being of primary importance. A pilot study was then undertaken to operationalize self-regulation as reflected in the mentoring practices of one MDM faculty member and assess the effectiveness of the planned data collection procedures. The primary investigation consisted of video recording the mentoring sessions of three faculty and three student teams, a total of 18 students. Video recorded mentoring sessions were observed and discussed by the researcher and each faculty member in a one-on-one interview setting. Final faculty and student interviews were conducted. Data from pre-research interviews, the stimulated recall sessions, and final interviews were analyzed and triangulated. Triangulation of learner interviews revealed that mentors supported self-regulatory behaviors using a variety of strategies, which are described in detail. Triangulation of pre-research interviews revealed that mentors were supporting learners in their development of specific characteristics expected of new recruits transitioning into the video game industry.
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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 | 0.011 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 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 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".