Death to weak PowerPoint: strategies to create effective visual presentations
Bibliographic record
Abstract
THE PROBLEM WITH POWERPOINTThere is nothing more frustrating than sitting through a presentation bombarded by slide after slide of small text, difficult to read graphs, irrelevant clip art images, and poorly designed templates.Often to blame is the use and abuse of PowerPoint 1 (e.g., Tufte, 2003;Bumiller, 2010).Academics typically only endure weak PowerPoint presentations at conferences, while university students may be exposed to them several times a day for an entire semester.Strong PowerPoint presentations enhance student engagement and help students retain information (e.g., Susskind, 2005), while weak PowerPoint slides can lead to distraction, boredom, and impeded learning (Savoy et al., 2009).The authors of this paper became interested in improving their PowerPoint slides after observing several presentations that badly misused PowerPoint, and realizing that they made many of the same mistakes.Our slides used standard, boring templates; were text heavy, and included grainy gif images-embarrassingly, some of which were even animated.For example, Figure 1A contains a slide that was prepared for a lecture in an introductory psychology course.The slide uses a template that makes the text difficult to read, there are several lengthy bullets, and the photos are too small.To make matters worse, the instructor had the slide heavily animated-bullet points flew in, swirled around, and even made sound.Needless to say, students were not impressed.1 Note that in this article we are using the denotation PowerPoint to refer to any presentation software (e.g., Keynote, Impress, etc.).Outstanding PowerPoint slides will not save a weak lecture; however, weak PowerPoint will certainly diminish a strong one.The purpose of this brief article is to provide some basic suggestions and resources for instructors who wish to improve their PowerPoint presentations, and in doing so, create more engaging and informative lectures for students.
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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 | 0.006 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.032 | 0.010 |
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".