USING THE CLA+ TO TRACK DEVELOPMENT OF COGNITIVE SKILLS FROM FIRST TO THIRD YEAR: ACCESSIBILITY AND FACTORS FOR SUCCESS
Bibliographic record
Abstract
This paper provides results from the first three years of a 4-year longitudinal assessment project on the development of cognitive skills using the Collegiate Learning Assessment (CLA+). The CLA+ is an online test, designed to measure cognitive skills (CS) (critical thinking, problem solving and written communication, with sub-scores reporting scientific and quantitative reasoning, critical reading and evaluation, and critiquing an argument). Cognitive skills are fundamental elements of engineering programs and central to the practice of engineering. The Canadian Engineering Accreditation Board’s (CEAB) requires programs to assess their student’s graduate attributes and have a system in place to use the assessment for curriculum improvement. CLA+ assessment constructs relate to the CEAB attributes of problem analysis, investigation and communication. The testing was initiated as part of a strategy to track development of cognitive skills and to inform course improvement efforts. The testing was embedded in a range of engineering courses. Student achievement on written communication outcomes appeared to fall over time, and critiquing an argument was identified as an area of weakness, specifically for the first year students. Strategies were subsequently implemented as part of a curriculum improvement initiative. With the goal of maximizing success for all students, we investigated differences in achievement based on sex, language, and parental education, as well as the amount of effort put into the test. There were significant differences in achievement, prompting concerns about motivation and relevance to the engineering discipline. Students without English as a first language demonstrated negative gains over time. Parental education level was the strongest predictor of performance, but the most significant factor impacting on students’ scores was self-reported effort.
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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 | 0.006 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".