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Record W4413783427 · doi:10.70716/jess.v2i1.191

The Role of Critical Thinking Skills in Enhancing Problem-Solving Abilities among High School Students

2025· article· en· W4413783427 on OpenAlexaff
Yuli Kusuma Dewi, Aisha Khalid, Abdilah Abqari Agam

Bibliographic record

VenueJournal of Education and Social Science · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicEducation and Critical Thinking Development
Canadian institutionsEducation and Early Childhood Development
Fundersnot available
KeywordsCritical thinkingMathematics educationPsychologyPedagogy

Abstract

fetched live from OpenAlex

This study investigates the role of critical thinking skills in enhancing problem-solving abilities among high school students. As education increasingly emphasizes the development of higher-order cognitive processes, understanding the interplay between critical thinking and problem-solving becomes essential. Employing a mixed-methods approach, data were collected through standardized critical thinking assessments, problem-solving tasks, and in-depth interviews with students and educators across multiple high schools. The findings reveal a significant positive correlation between students' critical thinking proficiency and their effectiveness in addressing complex, real-world problems. Moreover, instructional strategies that explicitly foster critical thinking—such as inquiry-based learning, Socratic questioning, and reflective discussions—were found to significantly enhance students' problem-solving performance. These results suggest that integrating critical thinking skill development into the high school curriculum is vital for preparing students to navigate academic challenges and broader societal issues. Implications for pedagogical practices and future research directions are discussed.

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)0.002
metaresearch head score (Gemma)0.014
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 score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.014
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)0.0010.000

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 head0.006
GPT teacher head0.359
Teacher spread0.352 · 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

Citations3
Published2025
Admission routes1
Has abstractyes

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