PRE AND POST COURSE STUDENT SELF ASSESSMENT OF CEAB GRADUATE ATTRIBUTES – A TOOL FOR OUTCOMES ASSESSMENT, STUDENT SKILL AND COURSE IMPROVEMENT
Bibliographic record
Abstract
In addition to instructor assessment, capstone and introductory design students self-assess their skill levels based on their perceived attainment of and confidence in their ability to perform categorized skills related to the CEAB Graduate Assessment Attributes pre and post both courses. The assessment levels are no or introductory experience, developing,satisfactory and mastered. The goals of this initiative are to provide data for the CEAB mandated requirement for continuous course improvement, and to gauge student perceptions of their skill development as they progress through the design course sequence. The results from two sets of online surveys for each course have helped identify areas for course development and have helped prioritize course improvements in areas with the largest potential for attribute and skill improvement. Course deliveryeffectiveness was evaluated by comparison with previous cohorts, pre and post course student self-assessment, and student engagement and satisfaction survey data. This report focuses on the results of the pre and post course student self-assessments, including outcomes for cohortscompleting all four surveys, and comparisons between students enrolled in the co-op program, who have an 8-month gap between courses, and traditional engineering program students, who are younger on average and only have a one-month gap between courses
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 | 0.007 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.005 |
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".