ACOMPARISON OF ACCESS AND DIRECT ENTRY STUDENT SUCCESS IN ELECTRICAL ENGINEERING TECHNOLOGY
Bibliographic record
Abstract
This paper provides an overview of the Introduction to Electrical Engineering Technology program, analyzes its success, and provides observations for administrators considering a similar initiative.A barrier to access for Science Technology Engineering and Math-based careers is the requirement for high school math and physics courses. To address this shortcoming, an Electrical Engineering Technology (EET) program at a major Manitoba college implemented a one term Introduction to Electrical Engineering Technology (Introduction to EET) program to upgrade student mathematical abilities, reduce the first-year workload, and help them identify a career that interest and motivate them.Student success is evaluated by comparing the retention and relative performance of the Introduction to EET students who entered the regular EET program with the performance of direct entry students in the same classes. The grades were analyzed using an ANOVA approach to determine if the two groups were statistically different. In the first cohort, statistical differences were observed in three courses with the direct entry students performing lower. In the second cohort, the Introduction to EET students performed poorer in mathematics than their direct entry peers. As students continued through the program, performance differences became harder to detect.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.016 | 0.003 |
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".