Bibliographic record
Abstract
This article discusses the positive outcomes of changing to a flipped classroom instructional approach and how creating a greater sense of influence and control for students in group projects can positively impact multiple measures of student performance and self-reported attitudes. Two sections completed the same readings and videos covering 13 course content modules. Rather than a traditional lecture-based course, most of the class time was used on experiential learning activities. Students worked with real-world clients to create advertising campaigns. One section completed research and pitched their ideas for a social media campaign to a franchise with over 50 restaurants. This section was seen as “low ownership” because students did not know if their ideas would be implemented. The second section created an integrated marketing campaign for an event on campus that drew over 400 attendees. This section is labelled as “high ownership” because the students felt more involved with their project as they imagined, created, and executed the advertising and organization for an event. Overall, most students in both sections preferred the flipped approach to traditional lecture. While the percent of videos watched and grades did not differ between the two sections, students in the high ownership section reported more interest in their project, were more likely to recommend keeping the flipped approach, were more likely to recommend the course to others, found the flipped classroom more interesting than lecture-only classes, and said they learned more using a flipped approach vs. a lecture-only class. These results demonstrate how flipped classrooms and experiential learning exercises with real-world results can improve marketing education.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 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".