STUDENT AND FACULTY EXPERIENCE WITH BLENDED LEARNING IN A FIRST-YEAR CHEMISTRY FOR ENGINEERS COURSE
Bibliographic record
Abstract
Abstract CHE 102, Chemistry for Engineers, is an introductory chemistry course taken by most engineering students at the University of Waterloo during their first term of study. In an effort to create time for more valuable instructor-student interaction and to allow students to explore course content at their own pace, some lecture sections piloted a blended learning model for approximately half the Fall 2016 term. Data from surveys administered throughout the term were combined with course grade data in an effort to compare and contrast student experience, satisfaction and performance between a blended learning and traditional lecture model of instruction. While the results from the Fall 2016 study are inconclusive due to challenges with survey administration and implementing the blended learning model, lessons were learned with respect to the readiness of the students for self-directed learning and the integration of the online and in-class components. We plan on continuing this study during the Fall 2017 term.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".