ENGINEERS AS LIFE-LONG LEARNERS: PEDAGOGICAL TOOLS FOR ENGINEERING GRADUATE ATTRIBUTE DEVELOPMENT
Bibliographic record
Abstract
We integrate three pedagogical constructs to design a novel toolbox to support engineering graduate attribute development and measurement of life-long learning. These constructs are (i) Bloom’s Taxonomy of Educational Objectives in the Cognitive Domain, (ii) Curriculum mapping to skill levels of Introduction, Reinforcement, Mastery and Assessment (IRMA), and (iii) High Impact Practices (HIPs). We use the toolbox in a single, but in-depth, experiential education research study in our department’s Biomedical and Electrical Engineering program. We formally introduce these pedagogies to a cohort of eighteen final year students. The students then use the toolbox throughout the term to identify and address their own educational needs such that they develop into independent, competent life-long learners. We test the effectiveness of the toolbox using mixed methods research. Bloom’s Taxonomy levels scored by the instructor and the students had a correlation coefficient of 0.36. IRMA mapping was tested using binomial hypothesis testing. The test accepted the alternative hypothesis H1: Student mapping of course learning outcomes to a skill level was informed by the instructor’s pedagogy and followed the same distribution as the instructor's mapping, i.e., the mapping was not random and p≠0.5. Results showed H1: {I (47%), R (84%), M (63%)}. In addition, experiential impressions of life-long learning on the students were gathered through qualitatively written feedback.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".