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Record W2152159814 · doi:10.20360/g2cc7b

Can Learning Be Fun and Games? The Influence of Everyday Language on Students’ Content Vocabulary Use and Concept Understanding

2010· article· en· W2152159814 on OpenAlexvenueno aff
Geraldine Mongillo

Bibliographic record

VenueLanguage and Literacy · 2010
Typearticle
Languageen
FieldPsychology
TopicEducational Games and Gamification
Canadian institutionsnot available
Fundersnot available
KeywordsVocabularyClass (philosophy)Mathematics educationPsychologyComputer scienceLiteracyUnit (ring theory)PedagogyLinguisticsArtificial intelligence

Abstract

fetched live from OpenAlex

This study examined the use of everyday language employed during game play to discover if this form of classroom discourse helped adolescents learn content area vocabulary and concepts. Data were collected in a grade 7 science class where the students played 5 instructional games during a six-week unit studying Mountain Building that targeted 26 vocabulary words. Data collected included audio recordings, written documents, interviews, formal and informal assessments, and field notes. Results indicated that the participants’ use of scientific language in both written and oral discourse during games was appropriate and comparable to other measures. Further, it was discovered that during games participants used everyday language to build understandings that exceeded token use of the academic vocabulary and concepts through collaborative talk. This study suggests that games provide a non-threatening forum for discussion and the exchange of ideas and implementing games as an alternate instructional strategy allows students to utilize their diverse ways of knowing and speaking.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.581
Threshold uncertainty score0.260

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.035
GPT teacher head0.335
Teacher spread0.300 · 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 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

Citations0
Published2010
Admission routes1
Has abstractyes

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