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Record W1576369930

An Empirical Study of Optimizing Cognitive Load in Multimedia Integrated English Teaching

2014· article· en· W1576369930 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.

venuePublished in a venue whose home country is Canada.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueStudies in literature and language · 2014
Typearticle
Languageen
FieldPsychology
TopicVisual and Cognitive Learning Processes
Canadian institutionsnot available
Fundersnot available
KeywordsCognitive loadComputer scienceCognitionReading comprehensionMultimediaReading (process)Empirical researchVocabularyComprehensionControl (management)Mathematics educationCognitive strategyCollege EnglishVocabulary learningCognitive psychologyPsychologyArtificial intelligenceLinguisticsStatistics
DOInot available

Abstract

fetched live from OpenAlex

Cognitive load is one of the important factors influencing complex learning. The article introduces relevant research in optimizing cognitive load in multimedia learning at abroad and in China. Results of the empirical study of the instructional design in the multimedia Integrated English show that the means of all the scores in the tests and the number of the students who pass the TEM-4 in the experimental group are higher than those in the control group, among which significant differences can be found in Cloze, Vocabulary and Structure Reading Comprehension, Paraphrasing and Total Score between the experimental group and the controlled group whereas no significant differences exist in their average score in Translation and Writing. The study indicates that optimizing cognitive load in the multimedia learning facilitates improving English learning efficiency.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.061
Threshold uncertainty score0.518

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 integrity0.0000.001
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 head0.026
GPT teacher head0.420
Teacher spread0.394 · 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