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Record W2605062076 · doi:10.19173/irrodl.v18i2.2860

The Effects of Lecture Diversity on Germane Load

2017· article· en· W2605062076 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

VenueThe International Review of Research in Open and Distributed Learning · 2017
Typearticle
Languageen
FieldPsychology
TopicVisual and Cognitive Learning Processes
Canadian institutionsnot available
Fundersnot available
KeywordsCognitive loadDiversity (politics)GermaneMultimediaCognitionPsychologyComputer scienceMathematics educationChemistrySociology

Abstract

fetched live from OpenAlex

An important aspect of MOOCs is the way students interact with video lectures. Instruction provided through video lectures should focus on ways to increase germane cognitive load, which directly contributes to learning. One approach that may lead to an increase of germane load may be to use video lectures with diverse forms of media, including both auditory and visual aspects of multimedia. This study surveyed a group of students (n = 1602) who participated in MOOC-like courses in South Korea to investigate the relationship between delivery diversity and germane cognitive load. Additionally, the relationships between individual indicators of both audio and visual aspects of multimedia and germane load were examined. Results show a positive relationship between diverse media delivery (auditory, visual, and total media) and germane cognitive load. The implication of these results are important for instructors who wish to promote a better understanding of e-learning materials through the delivery of content by using diverse forms of media.

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)0.003
metaresearch head score (Gemma)0.008
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 score0.661
Threshold uncertainty score0.928

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.001
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 head0.094
GPT teacher head0.498
Teacher spread0.403 · 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