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Record W3116910834 · doi:10.5539/ies.v14n1p86

Pre-Service Primary Teachers’ Opinions on Team-Games-Tournaments

2020· article· en· W3116910834 on OpenAlexvenueno aff
Aşkın Baydar

Bibliographic record

VenueInternational Education Studies · 2020
Typearticle
Languageen
FieldPsychology
TopicEducational Games and Gamification
Canadian institutionsnot available
Fundersnot available
KeywordsPsychologyQualitative researchMathematics educationAction researchTeamworkService (business)Medical educationCooperative learningGroup dynamicPedagogyTeaching methodSociologySocial psychologyManagementMedicine

Abstract

fetched live from OpenAlex

The aim of this study is to determine pre-service primary teachers’ opinions regarding the implementation of teams-games-tournaments (TGT). For this purpose, the action research method, which is one of the qualitative research methods, was employed. The study group in the research consisted of 30 students who attended Artvin Çoruh University College of Education Elementary Education Department Primary School Teaching Program. Qualitative data was collected by interview forms with open-ended questions. With the answers the participants gave to the interview questions, they shared their opinions about advantages and disadvantages of the method and their suggestions about using the method in their future classes. Participants found positive interdependence and learning in heterogeneous groups, which are among the common features of cooperative learning, useful and necessary. Participants also stated the features of causing noise, making classroom management more difficult, sharing responsibility and rewards unfairly, and difficulty of implementing the method in crowded classes as disadvantages of the method.

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)0.004
metaresearch head score (Gemma)0.009
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.007
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0020.001
Scholarly communication0.0020.001
Open science0.0000.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0040.001

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 head0.102
GPT teacher head0.435
Teacher spread0.333 · 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 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

Citations8
Published2020
Admission routes1
Has abstractyes

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