A Longitudinal Study of the Dimensions of Disciplinary Culture to Enhance Innovation and Retention among Engineering Students
Bibliographic record
Abstract
The U.S. engineering educational system has been generally slow in developing pedagogies that successfully promote innovative behaviors. Although numerous sources recognize the growing scope and complexity of challenges that lie ahead in the 21st century, engineering is struggling to balance its goals between the high-risk pursuit of innovation and the traditional problem-solving approach of producing functional, reliable applications. In short, engineering needs more creativity and interdisciplinary fluency, but not at the expense of its discipline-specific problem-solving skills. At the same time, engineering programs continue to struggle with attracting and retaining members of underrepresented populations—whose diversity could greatly contribute to innovation. Interestingly, this lack of diversity is often attributed to cultural traits of the field—often characterized as masculine, individualistic and function-oriented. Notably, students in fields that emphasize functionality (e.g. engineering) rather than creativity (e.g. industrial design) express higher levels of uncertainty avoidance. Together, these cultural dimensions of engineering continue to limit innovative practices, such as interdisciplinary collaboration, design thinking, and diversity of perspectives. The purpose of this study is to investigate patterns of cultural traits in students across disciplines, with the goal of building an actionable theory of engineering culture that can support pedagogies of inclusive and collaborative innovation. Specifically, we are using Hofstede’s theory of dimensions of national culture to understand engineering disciplinary culture. We are using an instrument to evaluate the original four dimensions of national culture (power distance, uncertainty avoidance, individualism, masculinity) to see if the dimensions map to academic disciplines to explain how students develop skills to operate within and across disciplinary boundaries. Furthermore, we are exploring the relationships between these dimensions of disciplinary culture and student recruitment and retention, particularly for underrepresented groups. We are in the third year of a mixed methods study. During year 1, 1043 students from all the disciplines at one institution were surveyed. In year 2, 1199 undergraduate students across 6 institutions were surveyed, and 5 students in electrical and computer engineering were interviewed. During year 3, the survey will go out during the spring semester 2016, and 24 students will be interviewed. Quantitative results describe how undergraduate students in different disciplines understand their culture in terms of Hofstede’s dimensions. However, although we were able to confirm the validity of the instrument with the quantitative data collected, there have not been statistically significant differences between the majors studied, suggesting that the instrument used was to measure the dimensions of culture at the national level rather than at the academic level. In order to continue exploring whether Hofstede’s national cultures map to disciplinary cultures in universities, we are collecting qualitative data (informed by the quantitative results) to obtain in-depth information of how students understand and perceive their disciplines in terms of Hofstede’s dimensions. Results from our research will provide valuable information to understand disciplinary cultures in engineering majors, and contribute to better understanding how to improve engineering culture to make engineers more innovative, to make engineering classrooms more welcoming and inclusive, and to make better decisions regarding curriculum development in engineering.
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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 | 0.000 |
| 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".