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Leveraging Indigenous Knowledge through Technology to Enhance Creativity and Critical Thinking in Students

2025· article· W7152131995 on OpenAlexaff
Sikya Nodin Mika

Bibliographic record

VenueNusantara Education · 2025
Typearticle
Language
FieldAgricultural and Biological Sciences
TopicIndigenous Knowledge Systems and Agriculture
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsCreativityIndigenousCritical thinkingContext (archaeology)CurriculumQualitative researchDivergent thinkingCritical systems thinkingDesign thinking

Abstract

fetched live from OpenAlex

This study explores the integration of indigenous knowledge with technology to enhance students’ creativity and critical thinking. The main objective is to investigate how digital tools can facilitate the application of local wisdom in educational contexts and how such integration affects cognitive and higher-order thinking skills. Employing a mixed-methods approach, the research combined a quasi-experimental design with pre- and post-tests to quantitatively measure creativity and critical thinking improvements, alongside qualitative interviews to capture students’ experiences, perceptions, and engagement with technology-mediated indigenous knowledge. The study was conducted among students in [specify region or school level if needed], where local cultural practices are rich and varied. Findings reveal that students participating in technology-enhanced indigenous knowledge activities showed a statistically significant increase in creativity and critical thinking compared to control groups. Qualitative analysis further indicates that students developed stronger problem-solving abilities, higher cultural awareness, and greater motivation to apply local knowledge in innovative ways. The integration of technology not only supported interactive and collaborative learning but also provided a meaningful context for connecting academic concepts with students’ cultural backgrounds. This research contributes to the field of educational innovation by offering an empirically validated framework for leveraging indigenous knowledge through technology, emphasizing the importance of contextualization, participation, and transformation in learning. The study also provides practical implications for curriculum developers, educators, and policymakers seeking to design pedagogical strategies that foster higher-order thinking skills while preserving and promoting local wisdom. Ultimately, this approach encourages a balanced development of cognitive, creative, and cultural competencies in students, highlighting the potential of culturally responsive and technology-mediated education in contemporary learning environments.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.482
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.002
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.001
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 head0.012
GPT teacher head0.358
Teacher spread0.346 · 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.

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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