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Record W3210462919 · doi:10.5539/hes.v11n4p84

Creative Learning Design in Social Studies to Promote Productive Citizenship of Secondary School Students

2021· article· en· W3210462919 on OpenAlexvenueno aff
Rattikorn Chanchumni, Charin Mangkhang

Bibliographic record

VenueHigher Education Studies · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicEducational Assessment and Pedagogy
Canadian institutionsnot available
Fundersnot available
KeywordsCitizenshipNonprobability samplingMathematics educationSocial studiesPsychologyPedagogyPrincipal (computer security)Action researchDescriptive statisticsSociologyPolitical sciencePopulationComputer science

Abstract

fetched live from OpenAlex

The purposes of this research are: 1) to study the creative learning in social studies to promote productive citizenship of secondary school students; and 2) to design the guidelines for such learning. This research implements the methodology of action research, consisting of 8 samples: 1) 1 school principal and 2 social studies teachers; and 2) 5 learning management experts. The samples are chosen by the purposive sampling method. Research tools consist of 1) unstructured interview form; and 2) appropriateness assessment form for the guidelines of creative learning in social studies to promote productive citizenship of secondary school students. Methods of data analysis consists of content analysis, and descriptive analysis, in addition to calculation of the means and standard deviation. Study results revealed that: 1. Creative learning in social studies to promote productive citizenship of secondary school students consists of the development of 4 minds, including: 1) critical mind; 2) creative mind; 3) productive mind; and 4) responsible mind. And 2. The guidelines of creative learning in social studies to promote productive citizenship of secondary school students consist of 4 subjects and 8 learning management plans. The effectiveness of the guidelines in terms of the learning management was evaluated as excellent.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation 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 score0.127
Threshold uncertainty score0.560

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
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 head0.199
GPT teacher head0.515
Teacher spread0.316 · 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 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

Citations3
Published2021
Admission routes1
Has abstractyes

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