EFFECTIVENESS OF VARIOUS SUPPLEMENTAL TEACHING APPROACHES IN EDUCATION OF ENGINEERING MATHEMATICS
Bibliographic record
Abstract
This paper provides information on how two secondyearengineering mathematics courses are delivered atUBC Okanagan. The goal is to provide more diversifiedteaching and learning strategies that suit students withdifferent learning styles. The list of approaches used inclass includes: traditional lectures in classroom;tutorial session and assignments; typed lecture andtutorial notes, Q&A through Piazza.com; video tutorialsthrough YouTube; Matlab/Maple simulations; andLecture videos on YouTube. Students can resort tovarious resources for answers. In order to explore theeffectiveness of these approaches, a survey wasconducted to collect students' opinions on strength,weakness and improvements of those various assistiveapproaches. The paper will give a brief introduction ofhow these supplemental teaching approaches areintegrated into the courses, and will discuss theeffectiveness of these approaches based on the surveyresults
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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 | 0.001 |
| 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 | 0.000 | 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".