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Record W2613342477 · doi:10.18260/1-2--12372

Engineering Faculty Teaching Styles And Attitudes Toward Student Centered And Technology Enabled Teaching Strategies

2020· article· en· W2613342477 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldPsychology
TopicLearning Styles and Cognitive Differences
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsLearning stylesContext (archaeology)Engineering educationModalitiesPerceptionActive learning (machine learning)Computer scienceSession (web analytics)PsychologyMathematics educationEngineeringEngineering managementArtificial intelligenceSociologyWorld Wide Web

Abstract

fetched live from OpenAlex

This paper presents results of a survey assessing learning preferences and teaching strategies of engineering faculty. Of particular interest were questions pertaining to technology implementations and to professional development. The survey pointed to lack of interest in educational activities and low use of innovative instructional methods and instructional technologies, particularly among junior engineering faculty. Results of a recent national faculty survey are reviewed to provide the context for discussion. Professional development of engineering faculty, long an area of concern, becomes more urgent as accumulated applied engineering and teaching experience is being lost through impending retirements. Ironically, with faculty renewal, there is a risk of the dominant culture in engineering departments becoming even less responsive to students' needs. Such concerns have been highlighted before and this study confirms them. I.

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.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.321
Threshold uncertainty score0.692

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.039
GPT teacher head0.339
Teacher spread0.300 · 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