SUPPORTING ENGINEERING STUDENTS ON ACADEMIC PROBATION BY IMPROVING THEIR LEARNING SKILLS
Bibliographic record
Abstract
This paper presents the experience of creating a student task force to support undergraduate engineering students on academic probation by strengthening their learning skills. The program described in the paper was designed based on the work of Professor Saundra Yancy McGuire, expert in the field of supporting student learning for over forty years. One of Professor McGuire’s main statement is the need to develop metacognitive skills among our student population [1]. Metacognition as a construct is frequently associated with John Flavell (1979) who defined it as “the ability to: think about one’s own thinking; be consciously aware of oneself as a problem solver; monitor, plan, and control one’s mental processing; and, accurately judge one’s level of learning” [2]. McGill’s ELATE (Enhancing Learning and Teaching in Engineering) initiative designed and implemented a program where students on academic probation meet in small groups on a weekly basis to receive support on the learning skills they struggle the most. Weekly meetings were conducted by engineering Graduate Students Instructors (GSIs) who received an intensive training on metacognition, growth mindset and learning skills. This project having completed the training phase with GSIs and Peer Tutors, we present in this paper survey data that describe challenges and benefits on initiating a project of this nature.
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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 | 0.002 | 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 | 0.005 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".