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Expert View

A Novel Paradigm for Engineering Education: Virtual Internships With Individualized Mentoring and Assessment of Engineering Thinking

[+] Author and Article Information
Naomi C. Chesler

University of Wisconsin—Madison,
2146 Engineering Centers Building,
1550 Engineering Drive,
Madison, WI 53706
e-mail: chesler@engr.wisc.edu

A. R. Ruis

University of Wisconsin—Madison,
489 Educational Sciences Building,
1025 West Johnson Street,
Madison, WI 53706
e-mail: arruis@wisc.edu

Wesley Collier

University of Wisconsin—Madison,
499 Educational Sciences Building,
1025 West Johnson Street,
Madison, WI 53706
e-mail: wcollier@wisc.edu

Zachari Swiecki

University of Wisconsin—Madison,
499 Educational Sciences Building,
1025 West Johnson Street,
Madison, WI 53706
e-mail: swiecki@wisc.edu

Golnaz Arastoopour

University of Wisconsin—Madison,
487 Educational Sciences Building,
1025 West Johnson Street,
Madison, WI 53706
e-mail: arastoopour@wisc.edu

David Williamson Shaffer

University of Wisconsin—Madison,
499 Educational Sciences Building,
1025 West Johnson Street,
Madison, WI 53706
e-mail: dws@education.wisc.edu

Because ENA models the co-occurence of codes, the entries on the diagonal of the matrix are assigned a value of zero regardless of the presence of absence of the codes corresponding to the cells, since cells on the diagonal would represent codes co-occurring with themselves.

The cumulative adjacency matrices are symmetric, because Ciju = Cjiu for all i and j.

Spherical normalization is accomplished by dividing each vector Cu by its length. This is the equivalent of the cosine norm frequently used in natural language processing and automated content analysis.

Technically speaking, ENA places the nodes so as to minimize the distance between Pu and the centroid of the network corresponding to Cu as represented in ENA space. The optimization typically results in a good fit of the model.

Manuscript received August 15, 2014; final manuscript received November 22, 2014; published online January 26, 2015. Editor: Victor H. Barocas.

J Biomech Eng 137(2), 024701 (Feb 01, 2015) (8 pages) Paper No: BIO-14-1399; doi: 10.1115/1.4029235 History: Received August 15, 2014; Revised November 22, 2014; Online January 26, 2015

Engineering virtual internships are a novel paradigm for providing authentic engineering experiences in the first-year curriculum. They are both individualized and accommodate large numbers of students. As we describe in this report, this approach can (a) enable students to solve complex engineering problems in a mentored, collaborative environment; (b) allow educators to assess engineering thinking; and (c) provide an introductory experience that students enjoy and find valuable. Furthermore, engineering virtual internships have been shown to increase students'—and especially women's—interest in and motivation to pursue engineering degrees. When implemented in first-year engineering curricula more broadly, the potential impact of engineering virtual internships on the size and diversity of the engineering workforce could be dramatic.

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References

Figures

Grahic Jump Location
Fig. 1

In RescuShell, students use a simulated design tool to create prototypes of their exoskeletons. Students make decisions concerning the material, control sensors, power source, actuation, and range of motion of the robotic legs.

Grahic Jump Location
Fig. 2

ENA scatterplot showing two groups of students who used the virtual internship Nephrotex. One group had no prior exposure to an engineering virtual internship (first simulation). The other group used RescuShell prior to using Nephrotex (second simulation). The points are individual students; the squares are the means for the two groups; the boxes are the 95% confidence intervals. The numbers in parentheses indicate the percentage of variance in the data accounted for by that dimension.

Grahic Jump Location
Fig. 3

Mean epistemic networks of the two groups of students described in Fig. 2, thresholded to reveal the most prominent connections. Students with no prior experience of an engineering virtual internship (left) made connections primarily among basic skills and knowledge and collaboration. Students with prior experience of an engineering virtual internship (right) made more connections to epistemological elements of engineering and to knowledge of the client. S = skills, K = knowledge, I = Identity, V = Values, and E = epistemology.

Grahic Jump Location
Fig. 4

ENA scatterplot showing two groups of students who used the engineering virtual internship RescuShell. At the end of the virtual internship, students assign their teammates virtual bonuses based on the perceived quality of their engineering design contributions. The points are students who received either low (bottom quartile) or high (top quartile) bonuses from their peers; the squares are the means for the two groups; the boxes are the 95% confidence intervals. The numbers in parentheses indicate the percentage of variance in the data accounted for by that dimension.

Grahic Jump Location
Fig. 5

Mean epistemic networks of the two groups described in Fig. 4, thresholded to reveal the most prominent connections. Students who received lower bonuses (left) made connections primarily to design and data issues and to teamwork and communication. Students who received higher bonuses (right) also made connections to the context of the client and ethics.

Grahic Jump Location
Fig. 6

Percentage of students who reported a positive, negative, or neutral/mixed experience after participating in a first or a second engineering virtual internship.

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