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  1. null (Ed.)
    Engineering instructors often use physical manipulatives such as foam beams, rolling cylinders, and large representations of axis systems to demonstrate mechanics concepts and help students visualize systems. Additional benefits are possible when manipulatives are in the hands of individual students or small teams of students who can explore concepts at their own pace and focus on their specific points of confusion. Online learning modalities require new strategies to promote spatial visualization and kinesthetic learning. Potential solutions include creating videos of the activities, using CAD models to demonstrate the principles, programming computer simulations, and providing hands-on manipulatives to students for at-home use. This Work-in-Progress paper discusses our experiences with this last strategy in statics courses two western community colleges and a western four-year university where we supplied students with their own hands-on kits. We have previously reported on the successful implementation of a hands-on statics kit consisting of 3D printed components and standard hardware. The kit was originally designed for use by teams of students during class to engage with topics such as vectors, moments, and rigid body equilibrium. With the onset of the COVID-19 pandemic and shift to online instruction, the first author developed a scaled down version of the kit for at-home use by individual students and modified the associated activity worksheets accordingly. For the community college courses, local students picked up their models at the campus bookstore. We also shipped some of the kits to students who were unable to come to campus, including some in other countries. Due to problems with printing and availability of materials, only 18 kits were available for the class of 34 students at the university implementation. Due to this circumstance, students were placed in teams and asked to work together virtually, one student showing the kit to the other student as they worked through the worksheet prompts. One community college instructor took this approach as well for a limited number of international students who did not receive their kits in a timely manner due to shipping problems. Two instructors assigned the hands-on kits as asynchronous learning activities in their respective online courses, with limited guidance on their use. The third used the kits primarily in synchronous online class meetings. We found that students’ reaction to the models varied by pilot site and presume that implementation differences contributed to this variation. In all cases, student feedback was less positive than it has been for face-to-face courses that used the models from which the take home kit was adapted. Our main conclusion is that implementation matters. Doing hands-on learning in an online course requires some fundamental rethinking about how the learning is structured and scaffolded. 
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  2. Abstract Background

    Noncognitive and affective (NCA) factors (e.g., belonging, engineering identity, motivation, mindset, personality, etc.) are important to undergraduate student success. However, few studies have considered how these factors coexist and act in concert.

    Purpose/Hypothesis

    We hypothesize that students cluster into several distinct collections of NCA factors and that identifying and considering the factors together may inform student support programs and engineering education.

    Design/Method

    We measured 28 NCA factors using a survey instrument with strong validity evidence. We gathered responses from 2339 engineering undergraduates at 17 U.S. institutions and used Gaussian mixture modeling (GMM) to group respondents into clusters.

    Results

    We found four distinct profiles of students in our data and a set of unclustered students with the NCA factor patterns varying substantially by cluster. Correlations of cluster membership to self‐reported incoming academic performance measures were not strong, suggesting that students' NCA factors rather than traditionally used cognitive measures may better distinguish among students in engineering programs.

    Conclusions

    GMM is a powerful technique for person‐centered clustering of high‐dimensional datasets. The four distinct clusters of students discovered in this research illustrate the diversity of engineering students' NCA profiles. The NCA factor patterns within the clusters provide new insights on how these factors may function together and provide opportunities to intervene on multiple factors simultaneously, potentially resulting in more comprehensive and effective interventions. This research leads to future work on both student success modeling and student affairs–academic partnerships to understand and promote holistic student success.

     
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