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Record W4415295089 · doi:10.5430/jct.v14n4p42

A Science and Technology Society Model to Enhance Critical Thinking Social Studies Students in Indonesia

2025· article· W4415295089 on OpenAlexvenueno aff
Septian Aji Permana, Nurulasyikin Binti Hasan

Bibliographic record

VenueJournal of Curriculum and Teaching · 2025
Typearticle
Language
FieldSocial Sciences
TopicEducation and Critical Thinking Development
Canadian institutionsnot available
Fundersnot available
KeywordsCritical thinkingAction researchCompleteness (order theory)Class (philosophy)Social studiesAction (physics)

Abstract

fetched live from OpenAlex

The background of the problem in this study is the low critical thinking of students in class VIII B Og 10th Public Junior High School Yogyakarta, Indonesia. The aim is to improve student learning outcomes using the Science Technology Society learning model. This type of research is classroom action research conducted in two cycles. Each cycle consists of four stages: action planning, action, observation, and reflection. The results of this study indicate an increase in the value of student learning outcomes with a minimum completeness criterion of 70. As seen from students before the action, out of 34 students in class VIII B, only 11 students reached minimum completeness criteria, with a 32% percentage. Then, the action in cycle I to 23 students reached the minimum completeness criteria with a percentage of 67.65%. In cycle II, learning outcomes have increased, namely reaching minimum completeness criteria 27 students with a percentage of 79.41%. The conclusion is that the use of the Science Technology Society learning model can improve students' critical thinking in social studies subjects.

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)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.002
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.022
GPT teacher head0.433
Teacher spread0.410 · 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 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
Published2025
Admission routes1
Has abstractyes

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