Assessment of Two Engineering Courses in Architectural Engineering Program in UAE University Based on the Comparison of the Students Results with the Students and the Instructors Opinions
Bibliographic record
Abstract
The present study examined the level of outcome satisfaction of two main engineering courses taken by students in the Architectural Engineering department (AE) by evaluating the students’ satisfaction result (SR), the attained level of the students’ opinions (SO), and the instructors’ opinions (IO). The AE program in United Arab Emirates University is one of the departments in the College of Engineering accredited by the Engineering Accreditation Commission of ABET which provides assurance that a College or University program meets the quality standards of the profession for which that program prepares graduates. The AE program offers a wide range of engineering courses at different levels from sophomore level to senior level. All the engineering courses are mainly prerequisites to the Capstone Engineering Design Project which builds on the outcomes of all courses to perform detailed design and cost estimates of the selected alternative solutions to a well-defined engineering problem. The two courses considered in this study are Building Electrical Circuits and Building Acoustics and Lighting. New assessment parameters which are the student course outcome satisfaction coefficient (SCOSC) and the mean absolute deviation around a central point (AMD) have been introduced in this paper. These two parameters are calculated based on the comparison of the students’ satisfaction results with both students’ opinions and insructors’ opinions, and compare the mean absolute deviations of the students’ direct results with the students’ opinions and the instructors’ opinions, respectively. Indeed, the course learning outcomes (CLOs) of the SR of some sections for both courses show higher attainment compared with the SO and IO.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".