Achieving PEAK POW: The Effects of Four PowerPoint Techniques on Student Learning and Retention
Bibliographic record
Abstract
Purpose of the Study PowerPoint-based (PP) lectures are a ubiquitous technique used in marketing classes. Despite this popularity, there is little research which demonstrates that existing PP methods optimize student learning and retention. In order to address this gap, this study compares four different PP techniques used by the same instructor for the same materials and develops an active learning PP technique based on the multimedia learning, cognitive load and active learning literatures to optimize student performance. Method/Design and Sample In four Sales classes (n=331), lectures for each class exclusively used one of four PP presentation types throughout the semester: publisher-generated slides, instructor-generated slides, student group-generated slides and student group-generated slides that had been peer-critiqued. Results Results show that the active learning, student-group generated, peer-critiqued slides optimized student final performance on the comprehensive multiple choice and long answer final examination as compared to the other conditions. Value to Marketing Educators This study presents a new active learning PowerPoint technique which enhances student aided and non-aided recall learning and retention. The recommendations are easily implemented and can be used by instructors of all level of experience as well as for all types of classes (face-to-face, online). This method also prepares students to generate effective Powerpoint presentations for the workplace.
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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 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".