PILOT OF A SERIES OF ONLINE RESOURCES TO HELP STUDENTS TRANSITION TO FIRST YEAR ENGINEERING
Bibliographic record
Abstract
Transitioning from high school to university can be a difficult time for students. A significant element in this transition is related to heightened selfresponsibility and self-regulation for one’s own learning. A series of eight online screencasts (consisting of narrated video with activities and quiz questions) was created and introduced at the University of British Columbia in 2018 as a pilot project. The goal was to help first year engineering students with their academic transition by providing evidence-based principles of effective study strategies and attitudes. Materials were delivered in the academic setting, rather than through traditional orientation and support channels, as a way to elevate this content and to reach as many students as possible. Materials were optional but a small grade incentive was included. Students appear to have found the resources beneficial as roughly half of the class viewed at least half of the screencasts. The opportunity to earn a small course bonus mark was cited as a key incentive, but approximately half of students identified academic and university transition benefits as their primary reasons for viewing. A course survey conducted five months after the final screencast in the series revealed positive student attitudes towards the materials, with approximately 70% of students identifying the materials as helpful or very helpful. In addition, students who had viewed a particular screencast gave significantly more favourable responses in prompts regarding perceptions of effective study practices. Finally, a positive correlation was observed between the number of screencasts viewed and course final exam grade (+0.8% on the final per screencast viewed). Overall, the results of this pilot suggest the use of online screencast materials to aid students in the transition to university is effective.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".