MétaCan
Menu
Back to cohort
Record W2068286162 · doi:10.4018/jwltt.2009010104

Animated Pedagogical Agents

2009· article· en· W2068286162 on OpenAlexaff
R. Heller, Mike Procter

Bibliographic record

VenueInternational Journal of Web-Based Learning and Teaching Technologies · 2009
Typearticle
Languageen
FieldArts and Humanities
TopicMedia Influence and Health
Canadian institutionsAthabasca University
Fundersnot available
KeywordsPersonaEvocationContrast (vision)PerceptionPsychologyImage (mathematics)Social psychologyCognitive psychologyAestheticsArtComputer scienceArtificial intelligenceHuman–computer interactionLiterature

Abstract

fetched live from OpenAlex

The role of Animated Pedagogical Agents (APAs) depends on an understanding of the persona effect as a mechanism for increasing student engagement and motivation. We argue that historical figure applications of APAs may be helpful to identify the parameters that give rise to a persona effect. Given the importance of visual information, an experimental approach was used to examine how different image conditions would affect perception of a historical figure APA interaction. Eighty-eight participants were randomly assigned to one of three conditions; no image, static image, or animated image. Contrary to expectations, the no image condition was associated with significantly higher ratings for 6 of the 12 measures, including 3 measures of social presence. These findings stand in contrast to previous research and suggest that historical figure applications may be unique in their evocation of a persona effect and valuable for understanding the nature of the persona effect.

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.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.020
Threshold uncertainty score0.065

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0020.002
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0200.002

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 head0.072
GPT teacher head0.361
Teacher spread0.290 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreMethods

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations8
Published2009
Admission routes1
Has abstractyes

Explore more

Same venueInternational Journal of Web-Based Learning and Teaching TechnologiesSame topicMedia Influence and HealthFrench-language works237,207