Drag-and-drop versus point-and-click mouse interaction styles for children
Bibliographic record
Abstract
This research investigates children's use of two common mouse interaction styles, drag-and-drop and point-and-click, to determine whether the choice of interaction style impacts children's performance in interactive learning environments. The interaction styles were experimentally compared to determine if either method was superior to the other in terms of speed, error rate, or user preference, for children. The two interaction styles were also compared based on children's achievement and motivation, within a commercial software environment. Experiment I used an interactive learning environment as children played two versions of an educational puzzle-solving game, each version utilizing a different mouse interaction style; experiment II used a mouse-controlled software environment modeled after the educational game. The results were similar to previous results reported for adults: the point-and-click interaction style was faster; fewer errors were committed using it; and it was preferred over the drag-and-drop interaction style. Within the context of the puzzle-solving game, the children solved significantly fewer puzzles, and they were less motivated using the version that utilized a drag-and-drop interaction style as compared to the version that utilized a point-and-click interaction style. These results were also explored through the use of state-transition diagrams and GOMS models, both of which supported the experimental data gathered.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".