COMPARISON OF STUDENT PERFORMANCE WHEN SOLVING A THERMODYNAMIC CYCLE AFTER IMPLEMENTING AN ALTERNATIVE TEACHING METHOD
Bibliographic record
Abstract
Abstract –First-year engineering students at the University of Manitoba take a thermodynamics course. The summer instructor taught the course differently from the fall and winter terms. He combined tutorials with lectures to introduce active learning to the course, implemented an online problem library and increased the number of term tests.
 Students in the summer 2016 term were given a similar thermodynamic cycle question in their final exam as students in the winter 2016 term. Student performance inthe cycle question was compared in order to evaluate the effectiveness of the new teaching method.
 Both groups had similar question averages with generally similar question score histograms. However, winter students scored an average of 10% below their GPA, while summer students scored an average of 1% below their GPA. The difference between these averages is statistically significant (97.5% confidence). These results suggest that the new teaching approach leads to better student performance when solving thermodynamic cycles.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".