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 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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".