Observing presenters' use of visual aids to inform the design of classroom presentation software
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Large classrooms have traditionally provided multiple blackboards on which an entire lecture could be visible. In recent decades, classrooms were augmented with a data projector and screen, allowing computer-generated slides to replace hand-written blackboard presentations and overhead transparencies as the medium of choice. Many lecture halls and conference rooms will soon be equipped with multiple projectors that provide large, high-resolution displays of comparable size to an old fashioned array of blackboards. The predominant presentation software, however, is still designed for a single medium-resolution projector. With the ultimate goal of designing rich presentation tools that take full advantage of increased screen resolution and real estate, we conducted an observational study to examine current practice with both traditional whiteboards and blackboards, and computer-generated slides. We identify several categories of observed usage, and highlight differences between traditional media and computer slides. We then present design guidelines for presentation software that capture the advantages of the old and the new and describe a working prototype based on those guidelines that more fully utilizes the capabilities of multiple displays.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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 it