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dc.contributor.authorHellmann, Jonathon Daviden_US
dc.date.accessioned2015-07-11T08:00:48Z
dc.date.available2015-07-11T08:00:48Z
dc.date.issued2015-07-10en_US
dc.identifier.othervt_gsexam:6022en_US
dc.identifier.urihttp://hdl.handle.net/10919/54544
dc.description.abstractBlock-based programming languages were originally designed for educational purposes. Due to their low requirements for a user's programming capability, such languages have great potential to serve both introductory programmers in educational settings as well as domain experts as a data processing tool. However, the current design of block-based languages fails to address critical factors for these two audiences: 1) domain experts do not have the ability to perform crucial steps: import data sources, perform efficient data processing, and visualize results; 2) the focus of online assignments towards introductory programmers on entertainment (e.g. games, animation) fails to convince students that computer science is important, relevant, and related to their day-to-day experiences. In this thesis, we present the design and implementation of DataSnap, which is a block-based programming language extended from Snap!. Our work focuses on enhancing the state of the art in block-based programming languages for our two target audiences: domain experts and introductory programmers. Specifically, in this thesis we: 1) provide easy-to-use interfaces for big data import, processing, and visualization methods for domain experts; 2) integrate relevant social media, geographic, and business-related data sets into online educational platforms for introductory programmers and enable teachers to develop their own real-time and big-data access blocks; and 3) present DataSnap in the Open edX online courseware platform along with customized problem definition and a dynamic analysis grading system. Stemming from our research contributions, our work encourages the further development and utilization of block-based languages towards a broader audience range.en_US
dc.format.mediumETDen_US
dc.publisherVirginia Techen_US
dc.rightsThis Item is protected by copyright and/or related rights. Some uses of this Item may be deemed fair and permitted by law even without permission from the rights holder(s), or the rights holder(s) may have licensed the work for use under certain conditions. For other uses you need to obtain permission from the rights holder(s).en_US
dc.subjectcomputer scienceen_US
dc.subjectblock-based programmingen_US
dc.titleDataSnap: Enabling Domain Experts and Introductory Programmers to Process Big Data in a Block-Based Programming Languageen_US
dc.typeThesisen_US
dc.contributor.departmentComputer Scienceen_US
dc.description.degreeMSen_US
thesis.degree.nameMSen_US
thesis.degree.levelmastersen_US
thesis.degree.grantorVirginia Polytechnic Institute and State Universityen_US
thesis.degree.disciplineComputer Science and Applicationsen_US
dc.contributor.committeechairTilevich, Elien_US
dc.contributor.committeememberKafura, Dennis Gen_US
dc.contributor.committeememberShaffer, Clifford Aen_US


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