Bibliographic record
Abstract
Current graphical keyboard and mouse interfaces are better suited for handling mechanical tasks, like email and text editing, than they are at supporting focused problem solving or complex learning tasks. One reason is that graphical interfaces limit users’ ability to fluidly express content involving different representational systems (e.g., symbols, diagrams) as they think through steps during complex problem solutions. We asked: Can interfaces be designed that actively stimulate students’ ability to “think on paper,” including providing better support for both ideation and convergent problem solving? In this talk, we will summarize new research on the affordances of different types of interface (e.g., pen-based, keyboard-based), and how these basic computer input capabilities function to substantially facilitate or impede people’s ideational fluency. We also will show data on the relation between interface support for communicative fluency (i.e., both linguistic and non-linguistic forms) and ideational fluency. In addition, we’ll discuss the relation between interface support for active marking (i.e., both formal structures like diagrams, and informal ones such as “thinking marks”) and successful problem solving. Finally, we’ll present new data on interfaces that improve support for learning and performance in lower-performing populations, and we will discuss how these new directions in interface media could play a role in improving their education and minimizing the persistent achievement gap between low- versus high-performing groups
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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| 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".