Computing courses struggle to retain introductory students, especially as learner demographics have expanded to include more diverse majors, backgrounds, and career interests. Motivational contexts for these courses must extend beyond short-term interest to empower students and connect to learners’ long-term goals, while maintaining a scaffolded experience. To solve ongoing problems such as student retention, methods should be explored that can engage and motivate students.
I propose Data Science as an introductory context that can appeal to a wide range of learners. To test this hypothesis, my work uses two educational theories—the MUSIC Model of Academic Motivation and Situated Learning Theory—to evaluate different components of a student’s learning experience for their contribution to the student’s motivation. I analyze existing contexts that are used in introductory computing courses, such as game design and media computation, and their limitations in regard to educational theories. I also review how Data Science has been used as a context, and its associated affordances and barriers.
Next, I describe two research projects that make it simple to integrate Data Science into introductory classes. The first project, RealTimeWeb, was a prototypical exploration of how real-time web APIs could be scaffolded into introductory projects and problems. Real- TimeWeb evolved into the CORGIS Project, an extensible framework populated by a diverse collection of freely available “Pedagogical Datasets” designed specifically for novices. These datasets are available in easy-to-use libraries for multiple languages, various file formats, and also through accessible web-based tools. While developing these datasets, I identified and systematized a number of design issues, opportunities, and concepts involved in the preparation of Pedagogical Datasets.
With the completed technology, I staged a number of interventions to evaluate Data Science as an introductory context and to better understand the relationship between student motivation and course outcomes. I present findings that show evidence for the potential of a Data Science context to motivate learners. While I found evidence that the course content naturally has a stronger influence on course outcomes, the course context is a valuable component of the course’s learning experience.
This material is based on work supported by the NSF under Grants No. DGE-0822220, DUE-1140318, DUE-1444094, and DUE-1624320.
|Advisor:||Shaffer, Clifford A., Tilevich, Eli|
|Commitee:||Conrad, Phill, Jones, Brett D., Kafura, Dennis|
|School:||Virginia Polytechnic Institute and State University|
|Department:||Computer Science and Applications|
|School Location:||United States -- Virginia|
|Source:||DAI-B 79/01(E), Dissertation Abstracts International|
|Subjects:||Educational technology, Computer science|
|Keywords:||CORGIS, Computational thinking, Computing, Data science, Datasets, Motivation|
Copyright in each Dissertation and Thesis is retained by the author. All Rights Reserved
The supplemental file or files you are about to download were provided to ProQuest by the author as part of a
dissertation or thesis. The supplemental files are provided "AS IS" without warranty. ProQuest is not responsible for the
content, format or impact on the supplemental file(s) on our system. in some cases, the file type may be unknown or
may be a .exe file. We recommend caution as you open such files.
Copyright of the original materials contained in the supplemental file is retained by the author and your access to the
supplemental files is subject to the ProQuest Terms and Conditions of use.
Depending on the size of the file(s) you are downloading, the system may take some time to download them. Please be