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Record W4360846789 · doi:10.5539/jel.v12n2p124

The Development of a Model for Enhancing Research Competencies in the Classroom of Student Teachers

2023· article· en· W4360846789 on OpenAlexvenueno aff
Oranut Srikham, Yannapat Seehamongkon

Bibliographic record

VenueJournal of Education and Learning · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicTechnology-Enhanced Education Studies
Canadian institutionsnot available
Fundersnot available
KeywordsPsychologyMathematics educationExploratory researchTest (biology)Exploratory factor analysisPsychometrics

Abstract

fetched live from OpenAlex

The objectives of the study were to 1) analyze the components and indicators for enhancing classroom research competencies of student teachers, 2) study the needs of developing classroom research competencies of the student teachers, 3) to create a model for enhancing research competencies in the classroom, 4) apply the form of a model for enhancing research competencies in the classroom of student teachers and 5) evaluate the model for enhancing research competencies in the classroom. The sample group consisted of student teachers, mentors, and experts. The instruments used in the research were: 1) a questionnaire on current conditions and desirable conditions for developing research competencies in the classroom, 2) a questionnaire on the components and indicators of research competencies in the classroom of the student teachers, 3) an assessment form for appropriateness and possibility of the model, 4) research knowledge test, 5) assessment form for research practice, 6) research mind test and 7) assessment form for opinions towards the use of the model. Statistics used in data analysis were percentage, mean, standard deviation, and data analysis by Priority Needs Index Modified (PNImodified) and Exploratory Factor Analysis (EFA). The results of the study were as follows: 1) Components and indicators of research competencies in the classroom of student teachers were classified into 3 components and 71 indicators as follows: Component 1: Cognitive, there were 24 indicators, the eigenvalue was 4.69; Component 2: Research practice, there were 30 indicators, the eigenvalue was 45.13; Component 3: research mind, there were 17 indicators, the eigenvalue was 1.91. 2) The results of the assessment of the needs of research competencies in the classroom of the student teachers by Priority Needs Index Modified (PNImodified) revealed that the most needed competencies were cognitive (0.179), followed by research practice skills (0.178) and research mind (0.127), respectively. 3) The model for enhancing research competencies in the classroom of student teachers was conducted with the research practice of student teachers. There were 5 steps to develop according to the established method: self-awareness, motivation, intimation, learning, and evaluation. 4) The results of the experimental model of enhancing research competencies in the classroom of the student teachers revealed that they had a good understanding at 76.30%, and the overall classroom research practice was at a high level (X̅ = 3.99, S.D. = .589), and the overall research mind was at a high level (X̅ = 4.15, S.D. = .863). 5) The evaluation results of the model for enhancing research competencies in the classroom of the student teachers, overall were at a high level (X̅ = 4.28, S.D. = .663).

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)0.005
metaresearch head score (Gemma)0.013
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.006
Threshold uncertainty score0.029

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.013
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0010.001
Scholarly communication0.0040.004
Open science0.0020.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.180
GPT teacher head0.497
Teacher spread0.318 · 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 designTheoretical or conceptual
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

Citations2
Published2023
Admission routes1
Has abstractyes

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