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Record W2743008653 · doi:10.1177/0735633117722329

Play, Learn, Connect: Older Adults' Experience With a Multiplayer, Educational, Digital Bingo Game

2017· article· en· W2743008653 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 Educational Computing Research · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicTechnology Use by Older Adults
Canadian institutionsUniversité TÉLUQSimon Fraser University
FundersSocial Sciences and Humanities Research Council of CanadaSimon Fraser University
KeywordsSocial connectednessQualitative propertyPsychologyEducational gameData collectionQualitative researchMultimethodologyMultimediaApplied psychologyComputer scienceMathematics educationSocial psychology

Abstract

fetched live from OpenAlex

This mixed-methods study examined the social gameplay and learning experience of 50 adults aged 60 years or more during 4 weeks of playing a multiplayer, educational digital Bingo game with embedded learning content about nutrition and health. The first phase consisted of 4 weeks of gameplay with quantitative data collection using pretests and posttests; the second phase used postgame interviews of selected players to collect qualitative data. The results of this study showed significant improvement in players' scores for knowledge, social connectedness, and attitudes toward digital games from the pretest to the posttest. The interview data confirmed these increases and provided insights on the importance of learning, social connectedness, coplaying, and general enjoyment from playing a digital educational game. The results of this study were also consistent with earlier research studies on older adults' needs, experiences, and preferences for digital gameplay.

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)0.002
metaresearch head score (Gemma)0.013
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.123
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.013
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0030.002
Scholarly communication0.0010.001
Open science0.0020.000
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 head0.058
GPT teacher head0.433
Teacher spread0.374 · 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