Structure and evaluation of flipped chemistry courses: organic & spectroscopy, large and small, first to third year, English and French
Bibliographic record
Abstract
Organic chemistry is a traditionally difficult subject with high failure & withdrawal rates and many areas of conceptual difficulty for students. To promote student learning and success, four undergraduate organic chemistry and spectroscopy courses at the first to third year level (17–420 students) were “flipped” in 2013–2014. In the flipped course, content traditionally delivered in lectures is moved online; class time is dedicated to focused learning activities. The three large courses were taught in English, the small one in French. To structure the courses, each course's intended learning outcomes (ILOs) were analyzed to decide which course components would be delivered online and which would be addressed in class. Short (2–15 min), specific videos were created to replace lectures. Online and in-class learning activities were created in alignment with the ILOs; assessment was also aligned with the ILOs. A learning evaluation was undertaken to determine the impact of the new course structure, using Guskey's evaluation model. Analysis of students' grades, withdrawal rates, and failure rates were made between courses that had a flipped model and courses taught in previous years in a lecture format. The results showed a statistically significant improvement in students' grades and decreased withdrawal and failure rates, although a causal link to the new flipped class format cannot be concluded. Student surveys and course evaluations revealed high student satisfaction; this author also had a very positive experience teaching in the new model. The courses' overall design and evaluation method could readily be adapted to other chemistry, science and other courses, including the use of learning outcomes, the weekly course structure, online learning management system design, and instructional strategies for large and small classes.
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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 | 0.007 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".