Attitudes and Academic Performance of Engineering Students in both Prerequisite Courses to Final Year Project and Final Year Project
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".