Cognitive Skills Development Among Undergraduate Engineering Students
Bibliographic record
Abstract
This research paper addresses assessment of numeracy and literacy among engineering students, which are core to problem solving and critical thinking, but challenging to consistently measure.The Essential Adult Skills Initiative (EASI) was a research project involving 20 Canadian postsecondary institutions, designed to measure the literacy, numeracy, and problem-solving skills of incoming and graduating college and university students the Education and Skills Online Assessment (ESO).At one participating institution, the ESO was administered over a two-year window in a cross-sectional approach to 112 first year and 65 fourth year engineering students.Statistically significant improvements were observed from first to fourth year in numeracy (W = 2634 , p < 0.05), and in literacy (W = 2743, p > 0.05).Of the fourth year participants, 38% received scores associated with trouble consistently performing critical written analysis, and 49% received scores associated with trouble consistently performing critical numerical analysis.Time spent on test was found to be correlated to final score (r = 0.35, p < 0.001).These results raise questions concerning the baseline skill level of some graduating engineering undergraduates, and when combined with prior literature also question adequacy of low-stakes standardized tests for measuring complex cognitive skills.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".