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Record W3096978787 · doi:10.5430/ijhe.v9n9p71

Development of Critical Thinking in Doctoral Students in Education

2020· article· en· W3096978787 on OpenAlexvenueno aff
Luis Alberto Núñez Lira, Yolanda Felícitas Soria Pérez, JESUS DANIEL COLLANQUE PINTO, Oriana Rivera‐Lozada

Bibliographic record

VenueInternational Journal of Higher Education · 2020
Typearticle
Languageen
FieldEnvironmental Science
TopicWater Resource Management and Quality
Canadian institutionsnot available
Fundersnot available
KeywordsCritical thinkingCornerstoneMathematics educationConstruct (python library)Reliability (semiconductor)PsychologyTest (biology)PopulationPedagogySociologyComputer science

Abstract

fetched live from OpenAlex

Critical thinking in university studies is the cornerstone for the development of research processes at the doctoral level; it becomes the vector of this action, whose processes in the management of learning will require that the competencies understood are developed by teachers and students, for the achievement of the goals proposed by the actors involved. This is how the research had the purpose of measuring the critical thinking of university doctorate students whose methodology was quantitative, with a population of 150 students, which allowed, in the first place, to establish the reliability and the analysis of the construct of the instrument used (Watson-Glaser test) and whose results showed a reliability of 0.77, KMO of 0.757 with a bilateral significance of 0.000. Likewise, of the five dimensions or factors of the instrument, five have a positive impact on moderate levels (Nagelkerke's pseudo-R square of 0.574) excluding inference. The descriptive analysis established that 11.3% present critical thinking at the advanced level.

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)0.014
metaresearch head score (Gemma)0.039
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.014
Threshold uncertainty score0.076

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0140.039
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.001
Science and technology studies0.0030.006
Scholarly communication0.0070.003
Open science0.0010.004
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0040.001

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 head0.036
GPT teacher head0.369
Teacher spread0.333 · 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 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

Citations2
Published2020
Admission routes1
Has abstractyes

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