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. 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. / Ph. D. / Introductory computing courses struggle to keep students. This has become worse as students with more diverse majors take introductory courses. In prior research, introducing fun and interesting material into courses improved student engagement. This material provides a compelling context for the students, beyond the primary material. But instead of only relying on fun material, courses should also rely on material that is useful. This means connecting to students’ long term career goals and empowering learners. Crucial to this is not making the material too difficult for the diverse audience. To keep more students, we need to explore new methods need of teaching computing.
I propose data science as a computing context that can appeal to a wide range of learners. This work tests this hypothesis using theories of academic motivation and learning theory. The components of a learning experience contribute to students’ motivation. I analyze how the components of other existing contexts can motivate students. These existing contexts include material like game design or media manipulation. I also analyze how good data science is as a context.
Next, I describe two projects that make it simple to use data science in introductory classes. The first project was RealTimeWeb. This system made it easy to use real-time web APIs in introductory problems. RealTimeWeb evolved into the CORGIS Project. This is a diverse collection of free “Pedagogical Datasets” designed for novices. These datasets are suitable for many kinds of introductory computing courses. While developing this collection, I identified many design issues involved in pedagogical datasets. I also made tools that made it easy to manage and update the data.
I used both projects in real introductory computing courses. First, I evaluated the projects’ suitability for students. I also evaluated data science as a learning experience. Finally, I also studied the relationship between student motivation and course outcomes. These outcomes include students interest in learning more computing and their retention rate. I present evidence for the potential of a data science context to motivate learners. But, the primary material has a stronger relationship with course outcomes than the data science context. In other words, students are more interested in continuing computing if they like computing, not if they like data science. Still, the results show that data science is an effective learning experience.
| Identifer | oai:union.ndltd.org:VTETD/oai:vtechworks.lib.vt.edu:10919/77585 |
| Date | 03 May 2017 |
| Creators | Bart, Austin Cory |
| Contributors | Computer Science, Tilevich, Eli, Shaffer, Clifford A., Jones, Brett D., Kafura, Dennis G., Conrad, Phillip T. |
| Publisher | Virginia Tech |
| Source Sets | Virginia Tech Theses and Dissertation |
| Detected Language | English |
| Type | Dissertation |
| Format | ETD, application/pdf |
| Rights | In Copyright, http://rightsstatements.org/vocab/InC/1.0/ |
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