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Record W2287453265 · doi:10.5430/ijhe.v5n2p23

Incorporating Critical Thinking into an Engineering Undergraduate Learning Environment

2016· article· en· W2287453265 on OpenAlexvenueno aff
Desmond Adair, Martin Jaeger

Bibliographic record

VenueInternational Journal of Higher Education · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicEducation and Critical Thinking Development
Canadian institutionsnot available
Fundersnot available
KeywordsCritical thinkingSet (abstract data type)Critical systems thinkingMathematics educationLogical reasoningAnalytical skillVertical thinkingEngineering ethicsComputer sciencePsychologyConvergent thinkingEngineeringCreative thinkingCreativity

Abstract

fetched live from OpenAlex

Critical thinking extends to all aspects of professional engineering, especially in technical development, and, since the introduction of the ABET 2000 criteria, there has been an increased emphasis in engineering education on the development of critical thinking skills. What is hoped for is that the students obtain critical thinking skills to complement their abundant logical thinking skills. Critical thinking is a developmental skill that cannot be taught simply by traditional methods, i.e., step-by-step instruction followed by repetitive skills, as used for logical thinking skills. The research and development reported here is concerned with outlining how students first can be alerted to what critical thinking is, and how critical thinking can be nurtured through practical experience with appropriate guidance and reinforcement. The module ‘Introduction to Fluid Mechanics’ has been selected to demonstrate how formal critical thinking components may be introduced with the development of principles of logic and an obligatory problem solving procedure. A method of assessment during various phases of critical thinking, i.e., initial design or set-up, testing of method or prototype, and evaluation of the results, of open-ended assignments in engineering for each year of the undergraduate course has been developed.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.508
Threshold uncertainty score0.547

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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)0.0000.000

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 head0.016
GPT teacher head0.345
Teacher spread0.328 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations37
Published2016
Admission routes1
Has abstractyes

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