Cognitive Science of PowerPoint Part II: The Power of Attention
Bibliographic record
Abstract
The most common pedagogical visual tool used in engineering classrooms are slides, such as those generated by Google Slides, Keynote, Prezi or PowerPoint. Unfortunately, when viewed through the lens of current models describing human information processing, many slides are poorly designed. That is, they either contain too much, poorly organized, or distracting information. Given the complexity of engineering content, it is essential that slides be used to help the student focus on key elements to increase learning, rather than simply act as a data dump or cue card. The paper will first provide an overview of human attention processes and how these impact working memory and learning. Then, the paper will provide an overview of selected theories from cognitive psychology, including top-down vs. bottom-up processing, focused vs. divided attention, and salience models (e.g. perception, gaze, and motion). Finally, using an authentic example of an engineering classroom slide, this paper will demonstrate how the practical application of these cognitive theories of attention can increase focus on (and thus retention) of the relevant content. This paper aims to be a “why-to” as well as a “how-to” guide for improving visual aids, specifically slides, in the engineering classroom. Note, this paper builds on our previous paper that focused on the cognitive load with respect to slide design.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.008 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".