The Paradox of the Assisted User: Guidance Leads to more Shallow Behavior
Bibliographic record
Abstract
This paper investigates the influence of interface styles on problem solving performance. It is often assumed that performance on problem solving tasks improves when users are assisted by externalizing task-related information on the interface. Although externalization requires less recall and relieves working memory, it does not instigate planning, understanding and knowledge acquisition. Without this assistance, task-information must be internalized, stored in the user's memory, leading to more planning and thinking and perhaps to better performance and knowledge. Another variable that can influence behavior is "Need for Cognition" (NFC), the tendency to engage in effortful cognitive tasks. We investigated the effects of interface style and cognitive style on performance using a conference planning application. Interface style influenced behavior and performance, but NFC did not. The internalization interface led to more planful behavior and smarter solutions. When planning and learning are the aim, designers should thus beware of giving a user (too) much assistance. Understanding how people react to interface information can be crucial in designing effective software, especially important in the areas of education and learning.
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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 | 0.052 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".