WRITTEN ASSIGNMENTS, UNDERGRADUATE LEVELS OF CONFIDENCE AND THE ENGINEERING PORTFOLIO
Bibliographic record
Abstract
Abstract – In this paper, we will summarize some of the results, first, from our course syllabi project that we conducted at the University of Manitoba and, secondly, from our study of second-year students’ levels of confidence in a communication class. In the course syllabi project, we discovered that course outlines in our Engineering school gave little information on the assignments expected of students, so much so that students may have found completing them to be difficult. In the second study, we found that students generally lacked confidence in writing tasks, especially at the beginning of term. These two studies suggest that we need to find a way to guide students in the writing of the assignments that we expect of them if they are to develop the necessary confidence in their ability to write well within a professional context. One way to do that may be the introduction of a portfolio requirement within the Engineering curriculum, A portfolio will serve as a record of students’ ongoing achievements in written assignments throughout their academic programs and, as they compile their portfolios, they can reflect on that achievement and move forward – more communicatively competent and more confident.
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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 | 0.003 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".