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Record W1976728220 · doi:10.5430/ijhe.v2n3p139

Critical Thinking in College Freshmen: The Impact of Secondary and Higher Education

2013· article· en· W1976728220 on OpenAlexvenueno aff
Marie Evens, An Verburgh, Jan Elen

Bibliographic record

VenueInternational Journal of Higher Education · 2013
Typearticle
Languageen
FieldSocial Sciences
TopicEducation and Critical Thinking Development
Canadian institutionsnot available
Fundersnot available
KeywordsBachelorCritical thinkingHigher educationMathematics educationPsychologyTask (project management)MultitudePedagogyBachelor degreeTest (biology)EngineeringPolitical science

Abstract

fetched live from OpenAlex

Critical thinking helps students to confront a multitude of challenges they will face in their carreers and personal lives. It is therefore an important task of higher education to promote students’ critical thinking. However, students do not enter higher education with a blank page. Background characteristics of students are important in developing instruction. The present study investigates the influence of an important background characteristic, namely students’ secondary education, and their current higher education programme on critical thinking in the first year of higher education. The critical thinking of college freshmen was measured by the SCIPIO, a test consisting of both constructed response items and forced choice items. The results indicate that (1) the growth in critical thinking during the first year of higher education is on average small, (2) students with a background in general secondary education have higher entrance performances and show more growth during the first year than students with other educational backgrounds, (3) critical thinking plays a role in the educational choice that students make when they enter higher education, and (4) students in a professional bachelor programme grow more in CT during the first year of higher education than students in an academic bachelor programme.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.646
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.000

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 head0.018
GPT teacher head0.390
Teacher spread0.372 · 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 designTheoretical or conceptual
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

Citations44
Published2013
Admission routes1
Has abstractyes

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