Assessing Life Long Learning Utilizing Coop Work Term Report
Bibliographic record
Abstract
CEAB introduced graduate attributes (GA) as a tool to measure the performance of an engineering institute in delivering its engineering programs. The 12th attribute is Life Long Learning (LL), which is defined as the student’s ability to identify and address their own educational needs. A student’s reaching out to technical references, away from an academic setting, is identified as a measuring tool for LL. As a pilot study, the technical references evaluated were extracted from a sample of 12 artifacts - 4th year work-term technical reports submitted as a component of co-operative education (co-op).To measure LL, a categorical metric to assess quality of cited sources was used to assess student competence in selecting credible technical information. All students included at least one technical reference in the design/analysis section (Technical Reference, TR); with most students using a mix of TR quality. Only 1/3 of students had average TR quality scores that met or exceeded the benchmark of 3.0. There may be a relationship between the type of work sector experienced and quality of references used.The pilot study suggests that using a quality metric for technical references within student documents has potential to assess lifelong learning at both the individual and cohort level. Results reinforce the need to educate and reiterate to engineering students the importance of credibility of the source of information over convenience.
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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 | 0.002 |
| 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".