Motivating Introductory Computing Students with Pedagogical Datasets

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dc.contributor.authorBart, Austin Coryen
dc.contributor.committeechairTilevich, Elien
dc.contributor.committeechairShaffer, Clifford A.en
dc.contributor.committeememberJones, Brett D.en
dc.contributor.committeememberKafura, Dennis G.en
dc.contributor.committeememberConrad, Phillip T.en
dc.contributor.departmentComputer Scienceen
dc.date.accessioned2017-05-04T08:01:04Zen
dc.date.available2017-05-04T08:01:04Zen
dc.date.issued2017-05-03en
dc.description.abstractComputing 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. RealTimeWeb 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.en
dc.description.degreePh. D.en
dc.format.mediumETDen
dc.identifier.othervt_gsexam:10199en
dc.identifier.urihttp://hdl.handle.net/10919/77585en
dc.publisherVirginia Techen
dc.rightsIn Copyrighten
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/en
dc.subjectMotivationen
dc.subjectIntroductory Computingen
dc.subjectComputational Thinkingen
dc.subjectEngagementen
dc.subjectCORGISen
dc.subjectDatasetsen
dc.subjectData Scienceen
dc.subjectDataen
dc.subjectPedagogical Datasetsen
dc.subjectComputer Science Educationen
dc.subjectEngagementen
dc.titleMotivating Introductory Computing Students with Pedagogical Datasetsen
dc.typeDissertationen
thesis.degree.disciplineComputer Science and Applicationsen
thesis.degree.grantorVirginia Polytechnic Institute and State Universityen
thesis.degree.leveldoctoralen
thesis.degree.namePh. D.en

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