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Record W2470583257 · doi:10.1111/bjet.12474

Learning and assessment with images: A view of cognitive load through the lens of cerebral blood flow

2016· article· en· W2470583257 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

VenueBritish Journal of Educational Technology · 2016
Typearticle
Languageen
FieldPsychology
TopicVisual and Cognitive Learning Processes
Canadian institutionsWestern University
Fundersnot available
KeywordsCognitive loadTask (project management)CognitionCerebral blood flowPsychologyCognitive psychologyComputer scienceNeuroscienceMedicineCardiology

Abstract

fetched live from OpenAlex

Abstract Understanding the relationship between cognitive processing and learner performance on tasks using digital media has become increasingly important as the transition towards online learning programs increases. Determining the impact of implementation of instructional resources is often limited to performance outcomes and comparisons to the status quo. This study measured changes in cerebral blood velocity (CBV) of the right middle cerebral artery during visual learning tasks using static images. Transcranial Doppler ultrasonography was used to compare the changes in CBV during learning of individuals with high and low spatial ability. Our results show that there is a slight increase from baseline values of CBV in individuals with high spatial ability during the learning task for the present study. In contrast, individuals with low spatial ability experience a decrement from baseline during the learning task. These results suggest spatial ability mitigates cognitive load and potentially has an impact on learner performance on visual learning tasks.

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)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.775
Threshold uncertainty score0.937

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.018
GPT teacher head0.344
Teacher spread0.326 · 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