Multidisciplinary Undergraduate Research Experience In The Internet Of Things: Student Outcomes, Faculty Perceptions, And Lessons Learned

Abstract

A Research Experiences for Undergraduates (REU) Site on the Internet of Things (IoT), funded by the National Science Foundation (NSF), was established at a large public university to engage undergraduate students in a 10-week, immersive research experience. REU students conducted research in fields spanning security, privacy, hardware design, data analytics, healthcare simulations, and social computing. A common survey available to Principal Investigators (PIs) of REU sites in Computer and Information Science and Engineering (CISE) was deployed to the 2016 summer cohort students at this REU IoT site. Results of the student pre- and post-surveys were statistically significant for the research skills and knowledge construct, but not significant for self-efficacy, intentions toward graduate school, attitudes toward the discipline of the assigned REU project, help seeking and coping behaviors, grit, scientific leadership, or scientific identity. A second evaluation was conducted, comparing student and faculty mentor post-survey scores on the self-efficacy construct. The results were not statistically significant, suggesting that students and faculty mentors had similar opinions on the ability of students to perform discrete research processes by the end of the REU. In this paper, we will describe the REU program recruitment strategy, structure, and activities; provide student contributions to the IoT research projects; discuss implications of our evaluation results; and share lessons learned. This paper may be especially interesting to faculty thinking about submitting a NSF REU CISE proposal and newly awarded PIs.

Publication Date

6-24-2017

Publication Title

ASEE Annual Conference and Exposition, Conference Proceedings

Volume

2017-June

Document Type

Article; Proceedings Paper

Personal Identifier

scopus

Socpus ID

85030535365 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/85030535365

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