The development of inclusive leaders is essential for the success of future engineering and our nation. Equipping students with vital leadership-enabling competencies is necessary to develop a workforce that is prepared to act ethically, and responsibly, and tackle unforeseen challenges in the future. Inclusive leaders, or leaders that are self-aware, empathetic, and prioritize diversity, equity, and inclusion in their decision-making, are essential for the forward progress of engineering. A growing body of literature highlights the numerous ways in which students may develop leadership skills outside of the classroom through involvement in out-of-class activities (e.g., internships, clubs, sports, and research experiences). Research Experiences for Undergraduates (REUs) may provide students with a unique opportunity to develop leadership-enabling competencies that will prepare them for leadership in graduate school, the engineering industry, or academia. The goal of this research was to identify how students’ engagement in an engineering education virtual REU site contributed to their development of essential leadership-enabling competencies. The research question guiding this study was ‘What inclusive leadership-enabling competencies and skills did engineering students learn and develop during an engineering education Summer REU program?’ Qualitative data was collected via weekly open-ended surveys from 9 students (7 women, 2 men) participating in an REU over 9 weeks. Participants in this study consisted of students from underrepresented groups in engineering (e.g., Black, Latinx, women, students from low SES backgrounds, or first-generation students), attending large public research universities across the United States. This study implemented mixed methods to understand what leadership competencies were occurring most frequently and how students were learning and developing these competencies. A combination of text mining for frequency (quantitative analysis) and deductive and inductive coding (qualitative analysis) was used to analyze the data. A codebook was developed based on the leadership-coupled professional competencies that engineering industry leaders identified as essential for engineers entering the workforce. Researchers also allowed for other competencies and leadership-enabling skills to emerge from the data. Findings from this work indicate that students were developing a vast amount of inclusive leadership knowledge and skills from participating in the virtual REU site. This paper highlights, through the use of word clouds and text mining software, the many leadership-enabling competencies that participants developed throughout the summer research experience (e.g., learning, communication, adaptability, self-awareness, balance, networking, etc.). Further, students were able to develop digital literacy, increased communication skills, knowledge of career pathways, intrapersonal growth, and interpersonal relations. This work offers a novel contribution to the literature in understanding how students can develop technical engineering and research skills as well as professional and leadership skills in the same space. Findings from this work help to illuminate the benefits of this virtual REU site focused on engineering education research resulting in terms of developing inclusive leadership skills. Implications for future REU programs, students interested in developing leadership skills, engineering graduate programs, academia, and industry employers are outlined.
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Mining Shape Expressions from Positive Examples
Shape expressions (SEs) is a novel specification language that was recently introduced to express behavioral patterns over real-valued signals observed during the execution of cyber-physical systems. An SE is a regular expression composed of arbitrary parameterized shapes, such as lines, exponential curves, and sinusoids as atomic symbols with symbolic constraints on the shape parameters. SEs enable a natural and intuitive specification of complex temporal patterns over possibly noisy data. In this article, we propose a novel method for mining a broad and interesting fragment of SEs from time-series data using a combination of techniques from linear regression, unsupervised clustering, and learning finite automata from positive examples. The learned SE for a given dataset provides an explainable and intuitive model of the observed system behavior. We demonstrate the applicability of our approach on two case studies from different application domains and experimentally evaluate the implemented specification mining procedure.
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- Award ID(s):
- 1837131
- NSF-PAR ID:
- 10199915
- Date Published:
- Journal Name:
- IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
- ISSN:
- 0278-0070
- Page Range / eLocation ID:
- 1 to 1
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1109/TCAD.2020.3012240
