ENHANCEMENT OF STUDENT LEARNING THROUGH SELF-REFLECTION
Bibliographic record
Abstract
Recent accreditation requirements by the Canadian Engineering Accreditation Board (CEAB) have forced engineering educators to focus on the outcomes of their teaching efforts. Faculty members are rapidly gaining expertise in the assessment of the 12 graduate attributes, and it is envisioned that emphasis on outcomes-based assessment will improve both the quality of the overall curriculum and individual course instruction. Nevertheless, the ultimate goal of any educational activity is to foster student learning. It is anticipated that students will gain a better understanding of the graduate attributes being covered in their courses if they are given the opportunity to self-reflect upon their educational experiences and achievements. The portfolio is the tool most often used to achieve this goal of self-reflection. A project has been undertaken in the Department of Biosystems Engineering at the University of Manitoba to assess the impact of self-reflection on student learning. During the fall of 2018, a series of voluntary workshops were organized i) to introduce Biosystems Engineering students to the purpose and art of self-reflection, ii) to describe self-reflection in the context of the Canadian Engineering Accreditation Board graduate attributes, iii) to introduce the e-portfolio tool, iv) to develop the skill of self-reflective writing, and v) to demonstrate the link between e-portfolio development and career success. The purpose of the paper is to describe the workshop series, the focus groups that followed the workshop series, and the theoretical framework within which the work is positioned.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".