Bibliographic record
Abstract
In 2021, during the global pandemic that necessitated lockdowns and primarily online delivery of education, the University of Saskatchewan introduced its new Re-Engineered first-year program. Engineering students often self-report as more introverted than the general population, raising concerns that their preference to spend time alone, coupled with pandemic restrictions, could lead to feelings of isolation and undermine the successful implementation of the new program. To mitigate potential isolation, the Re-Engineered program introduced “Study Squads” which were an impactful, low-investment addition to the program that received far less attention than other major pedagogical innovations like the implementation of competency-based-assessment and the elimination of final exams. The program’s use of block registration allows for easy creation of Squads of approximately 12 students with a common schedule. Students are introduced to their Squad members and exchange contact information early in the first term during an Introduction to Engineering course session on group dynamics. Throughout the academic year, students complete certain course requirements as a Study Squad. Students are also given optional opportunities to interact and compete as a Squad. Examples of these activities will be discussed in the paper. Re-Engineered has now been offered four times since the fall of 2021. Surveys conducted at the end of each academic year reveal that students have generally positive perceptions of their Study Squads. Many students have reported that the inclusion of Study Squads has facilitated meeting new people and enhanced both their learning and their enjoyment of first-year engineering. This implementation of Study Squads has had a very high return on investment and can be easily implemented in other engineering programs. The authors hope that this work contributes to fostering a more collegial environment for students.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.031 | 0.007 |
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".