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- 2018 ASEE Annual Conference & Exposition
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Karen H. Jin, University of New Hampshire; Michael Jonas, University of New Hampshire; Christopher David LeBlanc, University of New Hampshire; Theodore Sean Tavares, University of New Hampshire
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Diversity
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Computing and Information Technology
Paper ID #23065Modernizing Capstone Project: External and Internal ApproachesProf. Karen H. Jin, University of New Hampshire Karen H. Jin has been an Assistant Professor of Computer Science in Computing Technology program at UNH Manchester since Spring 2016. She previously taught as a lecturer for over ten years in University of Windsor and Dalhousie University. Her interest in computer science education research focuses on devel- oping new empirically supported theories and practices in teaching programming, software engineering and project-based learning with industrial relevance. She received her Ph.D. and M.Sc. in
- Conference Session
- Computing & Information Technology: Curriculum and Assessment
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- 2018 ASEE Annual Conference & Exposition
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Masoud Naghedolfeizi, Fort Valley State University; Xiangyan Zeng, Fort Valley State University; Chunhua Dong, Fort Valley State University
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. Furthermore, even students who do relatively well in these classes tend tolose their technical capabilities to tackle fairly large and complicated problems fromconcept to solution and then implementation using sound computer programmingpractices due to lack of sufficient practice in their upper division courses. This has beenobserved in the CS capstone course that requires students to design relatively largeprograms for various course projects. As a result, this could hinder their progress towardgraduation as well as future success as software developers, computer programmers,and/or other information technology and computing professionals. Thus, it is critical toimprove the programming skills of computer science students across curriculumparticularly
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- 2018 ASEE Annual Conference & Exposition
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Emil H. Salib, James Madison University; John David Lester
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at the undergrad-uate curriculum level is slow and elementary [9], [10]. Few hands-on, lab-based teaching materialsexist in this area both for the undergraduate faculty members and the students. Seeing the value ofSDN through our recent study (Senior Capstone Project), we believe it is a great opportunity anda critical mission to identify and enhance the right tools and platforms that enable educators andstudents to teach, learn, and stay up-to-date on SDN. We also believe that it’s imperative to demon-strate how these tools may be effectively utilized and applied through the development and deliveryof fully tested hands-on labs and exercises to our undergraduate inter-networking classes.The paper is organized as follows. In section 2, we
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- Computing & Information Technology: Curriculum and Assessment
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- 2018 ASEE Annual Conference & Exposition
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Predrag T. Tosic, University of Idaho; Julie Beeston, University of Idaho
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Computing and Information Technology
incoming freshmen excited about data analytics and the many career opportunities awaiting once those students complete the program.The curriculum committee made sure that there was, in each year of the program, that is, at eachof the four levels, from the 100-level non-technical popular intro to data science, to the 400-level“capstone (senior design) project”, at least one novel, unique to the data analytics programcourse. The senior capstone project was designed to address serious practical data-intensiveproblem in a given application domain or industry, and to involve both academic facultymentors, and mentors (and, whenever possible, sponsors) from the industry. Just like withexisting Computer Science or Electrical Engineering senior design
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Mohammad Rafiq Muqri, DeVry University, Pomona; Nikole Harper; Hasan Muqri; Brian Keith Wesr Sr.
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, 2018Leveraging the power of Matlab, SPSS, EXCEL and Minitab for Statisticalanalysis and inferenceAbstractFor many undergraduate and graduate engineering technology students, data collectionand data analysis—including methodology, statistical analysis, and data preparation—is the most daunting and frustrating aspect of working on capstone senior projects andmaster’s theses. This paper provides an introduction to a number of statisticalconsiderations, specifically statistical hypotheses, statistical methods, appropriateanalytic techniques, and sample size justifications. Statistical analysis of data utilizingstatistical software packages, including MATLAB, SPSS, Minitab, EXCEL, and R,will be shown for scientific applications, quality assurance, corporate