Effectiveness of blended learning for an energy balance course
Bibliographic record
Abstract
The effectiveness of on-line modules in a fundamental chemical engineering course is examined. An undergraduate second-year course on vapour-liquid equilibrium and energy balances is augmented by six onlinemodules. Each module consists of supplementary lecture material for the students in the form of screencasts and interactive simulations followed by on-line quizzing on the fundamental aspects of the content. The quizzes of three of the six modules count for a small percentage of the final course grade (2% each), whereas the quizzes of the other three are offered only for self-assessment. The primer mode of instruction is still “traditional” face-toface. Access to the on-line resources is monitored andrecorded. The major question that is being examined is whether students value the on-line resources and access them to enhance or clarify their learning, or simply try only the on-line “mandatory”, for grade, components. Correlations between students GPA, achievement in the course, attendance to class and on-line module access and quiz achievement are also investigated. Student qualitative feedback on the effectiveness and value of the on-line material is also collected.Students in general value on-line resources: they let students work at their own pace, on their own schedule, and provide immediate feedback. This work assesses the degree to which such resources provide added value to a course that is phenomenologically outside the corecurriculum (the course is not taught to chemical engineer students) and within a busy study 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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".