MétaCan
Menu
Back to cohort
Record W3149621007 · doi:10.29173/iasl8166

Gender Interest Differences with Multimedia Learning Interfaces

2021· article· en· W3149621007 on OpenAlexvenueno aff
Davіd Passіg, Haya Levin

Bibliographic record

VenueIASL Annual Conference Proceedings · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicGender and Technology in Education
Canadian institutionsnot available
Fundersnot available
KeywordsTask (project management)MultimediaClass (philosophy)Computer scienceHuman–computer interactionInterface (matter)Significant differenceInterface designPsychologyArtificial intelligenceEngineeringMathematics

Abstract

fetched live from OpenAlex

In this study we examined whether there are gender differences in leaming interest from different designs of multimedia interfaces. In the study we assumed that design characteristics add to the interest in learning and we developed taxonomy of design of efficient user interfaces both for boys and girls. The research included ninety children from three indergarten class, who were exposed to interactive multimedia stories. The researchsubjects, with the help of a Polymeter (Lampert 1981), answered to questionnaires, which examined their previous experience with a computer, their level of time on task and their level of satisfaction with the various interfaces. The research findings indicate that there is a significant difference between boys and girls in the influence of the design of the learning interfaces on their level of time on task as well as on their level of satisfaction with the different interfaces. Boys on the one hand had a higher level of time on task, and were more familiar with computer games and looked for assistance through navigational buttons. Girls on the other hand tended to ask for help with the game. Girls preferred to include writing into the game and preferred colorful screens full of drawings, which changed slowly. We also found that boys preferred green and blue colon, whilst the girls preferred red and yellow. Generally speaking we found that girls preferred the components of the Mise-en-scene interface, and boys preferred the components of the Montage interface.

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 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: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.216
Threshold uncertainty score0.628

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 communication0.0000.001
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 head0.064
GPT teacher head0.313
Teacher spread0.249 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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

Citations1
Published2021
Admission routes1
Has abstractyes

Explore more

Same venueIASL Annual Conference ProceedingsSame topicGender and Technology in EducationFrench-language works237,207