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Record W1968883966 · doi:10.4018/jdm.2008070102

Using Iconic Graphics in Entity-Relationship Diagrams

2008· article· en· W1968883966 on OpenAlex

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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Database Management · 2008
Typearticle
Languageen
FieldPsychology
TopicVisual and Cognitive Learning Processes
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsGraphicsComputer scienceCognitionCognitive loadDomain (mathematical analysis)Domain theoryCognitive psychologyHuman–computer interactionCognitive scienceNatural language processingPsychologyComputer graphics (images)Mathematics

Abstract

fetched live from OpenAlex

This study reports on an experiment examining the impact of iconic graphics on participants’ understanding of domains represented by entity relationship diagrams. Cognitive load theory and the cognitive theory of multimedia learning are used to hypothesize that iconic graphics reduce the cognitive load of model viewers, leading to more complete mental models and consequently improved understanding. Results, as measured by transfer (problem solving) tasks, confirm the main hypothesis. Additionally, iconic images were found to be less effective in improving domain understanding with English-as-a-second-language (ESL) participants. ESL results are shown to be consistent with predictions based on the cognitive load theory.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.099
Threshold uncertainty score0.344

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.183
GPT teacher head0.401
Teacher spread0.218 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it