Engineering or Physical Sciences: How to Choose? An Exploration of How First-Year University Students Choose between Studying the Physical Sciences and Engineering
Bibliographic record
Abstract
Abstract Engineering and the physical sciences share many common foundations in both knowledge and required skills. Yet working scientists and professional engineers may be more inclined to express the differences between their professions and domains of practice, rather than their similarities. The differences between the natural sciences and engineering – in their goals, activities, fields of study, level of materials studied, methodologies, and cultures – have been discussed by scholars in the philosophy and sociology of science as well as by scholars of the practice of engineering and in the emerging field of the philosophy of engineering. Given the actual and perceived disciplinary and career differences between the physical sciences and engineering, how do undergraduate students choose between them for university study? This paper seeks to answer this question by analyzing and examining the results from an ethically-approved online research questionnaire directed at first-year undergraduate university students conducted in September 2022 at a mid-sized comprehensive university. The questionnaire consists of 29 items that ask respondents about the influence of personal, familial, educational, societal/cultural factors on their interest in or decision to study engineering or one of the physical sciences, as well as about their perceptions of engineering versus the physical sciences. Demographic information is also collected from the respondents. The outcomes reported in this paper consist of statistical correlations, as assessed by significance levels, from analysis of the questionnaire data. The theoretical framework for our inquiry is primarily situated within the scientific perspective of critical realism that conceives of knowledge as resulting from the correct understanding of the data of experience, and in which adult students are viewed as operating within horizons of their knowledge and values; within this framework, education can be viewed as constructivist. Within that framework and the context of this study we also explore the dynamics of common models of decision making, including those of rational choice theory, value-focused decision making, and others. Other theoretical influences include Friere's liberation pedagogy, cognitive and affective interactions in attraction to and identification with professional careers, and new literacies for engineering and science. The paper will also discuss how the statistical results from the survey may inform outreach and pedagogy for science and engineering, and thus help to foster greater attraction and retention of undergraduate students in STEM fields, and greater career sustainability and life satisfaction for STEM graduates.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".