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Record W4319767784 · doi:10.5430/ijhe.v12n1p45

Attitudes and Academic Performance of Engineering Students in both Prerequisite Courses to Final Year Project and Final Year Project

2023· article· en· W4319767784 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.

venuePublished in a venue whose home country is Canada.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueInternational Journal of Higher Education · 2023
Typearticle
Languageen
FieldEngineering
TopicEngineering Education and Curriculum Development
Canadian institutionsnot available
Fundersnot available
KeywordsContext (archaeology)Engineering educationRelation (database)PsychologyEngineeringEngineering managementMedical educationMathematics educationComputer scienceMedicineGeography

Abstract

fetched live from OpenAlex

The aim of this study was to examine the attitudes of engineering students and their academic performance towards both prerequisite courses for and the final year project (FYP), given the need to increase our understanding of attitudes and performance in the context of engineering students, currently underexplored. Questionnaire surveys of 714 eligible students enrolled in the FYP across six engineering programs were conducted. The results show that students enrolled in Industrial, Mechanical and Civil engineering programs, have a negative attitude towards the FYP and its prerequisites, while students enrolled in Electrical, Electronic and Industrial Design and Technology programs have a positive attitude. A statistically strong positive correlation between project prerequisites and engineering FYP was found, confirmed by factor analysis. Majority of students struggle with project progress as compared to other stages of the FYP, due to inadequacy in fundamentals such as design. This study contributes to an understanding of existing knowledge by providing empirical evidence of not only challenges faced by engineering students (as opposed to other disciplines that have been widely covered) but also remedies to improve students’ academic performance. The findings also have implications on engineering education, in relation to informing policy decisions on engineering program structure.

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 score0.034
Threshold uncertainty score0.356

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.025
GPT teacher head0.349
Teacher spread0.324 · 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