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Record W2158316704 · doi:10.19030/tlc.v7i12.953

Animations Are Dynamic, Effective Tools For Science Teaching: If You Just Follow The Rules!

2010· article· en· W2158316704 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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueJournal of College Teaching & Learning (TLC) · 2010
Typearticle
Languageen
FieldPsychology
TopicVisual and Cognitive Learning Processes
Canadian institutionsUniversity of Toronto
FundersUniversity of Toronto
KeywordsComputer scienceAnimationMultimediaGraphicsValue (mathematics)Term (time)Human–computer interactionComputer graphics (images)Machine learning

Abstract

fetched live from OpenAlex

Textbook companies are increasingly including larger numbers of animations as complementary resources for students and teachers. Are all animations useful as teaching tools? The answer is no. Animations can be useful for communicating dynamic events and processes but only when specific rules are followed. The authors review the important components of effective animations and their extensive, original research on the value of animations in learning and long-term memory retention. When the rules are applied, students can learn complex material more easily and retain more of what they have learned in short and long term memory than they can by viewing static textbook figures. Our results also indicate that learning from animations and graphics differs between males and females. Insight gained from student feedback is highlighted with some final comments on future research.

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)0.008
metaresearch head score (Gemma)0.015
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Science and technology studies, Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.689
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0080.015
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0040.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0000.005
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 head0.369
Teacher spread0.343 · 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