Work in Progress: Low Enrollment in Civil Engineering Departments: Exploring High Technology as a Potential Solution
Bibliographic record
Abstract
Abstract There is a recurring issue of low enrollments across many civil engineering departments in postsecondary institutions. While there have been moments where enrollments begin to increase, civil engineering departments find themselves facing low enrollments at around 60% over the last five years across the Middle East and the United States. There are many reasons that could be attributed to this decline, such as low entry-level salaries, over-saturation of civil engineering graduates in the job market in certain regions, and a lack of construction projects due to the impending or current recession. Low enrollment also has an effect on the availability of civil engineers, especially in times of high demand, such as the passing of the recent US legislature on rebuilding infrastructure. However, this recurring problem alludes to an intrinsic issue of the curriculum, as researchers have discovered. The societal shift to the usage of high technology such as machine learning (ML) and artificial intelligence (AI), demands individuals who are proficient at utilizing it. However, in many existing civil engineering curricula, students are not taught programming skills that would aid in using high technology and if introduced at an early level, these skills are not utilized in future coursework. This paper aims to conduct a survey on the civil engineering curriculums of the top 100 American and Middle Eastern universities based on the QS World Ranking system. Initial analysis of the survey results indicates that the majority of universities have not considered new methods of data analytics such as ML or AI in their civil engineering coursework. Based on the results of the survey, the authors will provide suggestions on how to adapt high technology concepts to civil engineering coursework, while abiding by ABET/ASCE accreditation requirements. The findings of this paper will indicate where postsecondary universities offering civil engineering can easily adapt their curriculums to address the current low enrollment crisis, which in turn, supports future civil engineers for the world of high technology.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
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 teacher head, 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".