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Record W2611117476 · doi:10.1186/s41239-017-0055-0

Situated learning through intergenerational play between older adults and undergraduates

2017· article· en· W2611117476 on OpenAlexaff
Fan Zhang, David Kaufman, Robyn Schell, Glaucia Salgado, Erik Tiong Wee Seah, Julija Jeremic

Bibliographic record

VenueInternational Journal of Educational Technology in Higher Education · 2017
Typearticle
Languageen
FieldPsychology
TopicAging and Gerontology Research
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsSituatedSituated learningHigher educationPsychologySociologyPedagogyMathematics educationComputer scienceArtificial intelligencePolitical science

Abstract

fetched live from OpenAlex

This study is grounded in a social-cultural framework that embeds learning in social activities, mediated by cultural tools and occurring through guided participation in the social practice of a particular community. It uses conversation analysis as a tool to examine the structures of the talk-in-interaction of naturally occurring conversations between 11 pairs of older adult (aged between 65 and 92) and undergraduates (aged between 18 and 25) during a 6-week social practice of intergenerational digital gameplay. The purpose is to demonstrate how older adults adapt to and make sense of collaborative gaming activities through guided participation. The features of minimum gap and overlap, even conversational inputs, and orientation to one another’s turns indicate interactional connection between older adults and younger people. Adjacency pairs in the form of question-answer and self-initiated other-repairs are the situated use of social resources afforded by the intergenerational interaction. It is through these two main means of interaction that younger players offer immediate feedback and explanation to guide older adults to engage in the collaborative play and develop understanding of unfolding concepts and phenomena.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.229
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.048
GPT teacher head0.429
Teacher spread0.381 · 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.

Study designObservational
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

Citations27
Published2017
Admission routes1
Has abstractyes

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