PEER TUTORING IN A PROJECT-BASED COURSE PART 2: THE SECOND YEAR OF IMPLEMENTATION - IMPROVEMENTS AND BENEFITS
Bibliographic record
Abstract
In Fall 2017, a peer-tutoring program (PTP) was implemented in a first-year multidisciplinary design course of the mechanical, electrical and electromechanical engineering programs at Université du Québec à Rimouski. Fourth-year students with relevant design experience acted as tutors for teams of first-year students. The intent was to reduce the pressure on our professional staff, while maintaining the quality of supervision. PTP assessment revealed that all stakeholders appreciated the experience. At low cost, it allowed more weekly hours of supervision and freed up time for professional staff to perform tasks requiring their expertise. Although the impact of the PTP on the quality of learning was not directly measured, the student level of satisfaction was very high. However, the teaching team concluded from PTP assessment results that tutors could have more impact on teamwork and team spirit if they were better prepared. Four main areas for improvement of the PTP have been identified. Modifications for improvement of the PTP were developed as follows. Tutor training was modified to include content on teamwork, group dynamics and conflict resolution. To enhance communication between tutors and teaching team, tutors were provided with a new week-by-week guide outlining project planning and giving tutors instructions for structuring team discussions. Tutors participated in a professional co-development session in the fifth week of the project, an activity that allowed tutors to learn from each other and improve their practice. Lastly, the assessment questionnaire was improved to collect more significant data on student learning and teamwork. The new assessment results reveal that the changes made to the PTP have had a very positive influence on teamwork and group dynamics. Results also indicate that the improved PTP has a positive impact on achievement of course objectives. In conclusion, peer tutoring is evidently a very good strategy for supporting first-year students in the development of their design skills.
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 | 0.016 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".