Using Graphics to Improve Understanding of Conceptual Models
Bibliographic record
Abstract
Making Entity-Relationship diagrams easier to understand for novices has been a topic of previous research. This study provides experimental evidence that suggests using small representative graphics (iconic graphics) to replace standard entity boxes in an ER diagram can have a positive effect on domain understanding for novice users. Cognitive Load Theory and the Cognitive Theory of Multimedia Learning are used to hypothesize that iconic graphics reduce extraneous cognitive load of model viewers leading to more complete mental models and consequently improved understanding. Domain understanding was measured using comprehension and transfer (problem solving) tasks. Results confirm the main hypothesis. In addition, iconic graphics were found to be less effective in improving domain understanding with English as second language (ESL) participants. ESL results are shown to be consistent with predictions based on the Cognitive Load Theory. The importance of this work for systems analysts and designers comes from two considerations. First, the use of iconic graphics seems to reduce the extraneous cognitive load associated with these complex systems. Secondly, the reduction in extraneous load enables users to apply more germane load which relates directly with levels of domain understanding. Thus iconic graphics may provide a simple tool that facilitates better understanding of ER diagrams and the data structure for proposed information systems.
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How this classification was reachedexpand
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 | 0.023 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.010 | 0.005 |
| Science and technology studies | 0.001 | 0.013 |
| Scholarly communication | 0.001 | 0.057 |
| Open science | 0.010 | 0.015 |
| Research integrity | 0.001 | 0.011 |
| Insufficient payload (model declined to judge) | 0.000 | 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; both teacher heads agree on what is shown here.
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".