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Record W2908568743 · doi:10.24908/pceea.v0i0.12923

Engineering Preparatory Programs: Students’ Academic Motivation

2018· article· en· W2908568743 on OpenAlexvenueno aff
Aharon Gero, Gershon Abraham

Bibliographic record

VenueProceedings of the Canadian Engineering Education Association (CEEA) · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicCareer Development and Diversity
Canadian institutionsnot available
Fundersnot available
KeywordsAutonomyMathematics educationEngineering educationValue (mathematics)PsychologyEngineeringMathematicsEngineering managementPolitical scienceStatistics

Abstract

fetched live from OpenAlex

Engineering preparatory programs (EPPs) offer candidates who have not attained adequate achievements during their secondary education an opportunity to improve their chances to be accepted to undergraduate engineering programs. In light of the importance of academic motivation in EPPs, this study characterized the motivational factors for studying engineering in students attending an EPP in Israel. Forty students took part in the study which utilized quantitative tools. The findings indicate that the students were primarily motivated by interest in studying engineering (intrinsic motivation) and by recognition of their inherent value (identified regulation). However, a comparison of the Relative Autonomy Index to the one of excelling 12th grade students majoring in engineering reveals a significant difference between the two groups. This gap, in favor of the high school students, is characterized by a medium effect size.

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)0.001
metaresearch head score (Gemma)0.004
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.998
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0020.000
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.015
GPT teacher head0.252
Teacher spread0.237 · 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

Citations1
Published2018
Admission routes1
Has abstractyes

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