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Bridging Cyber and Physical Programming Classes: An Application of Semantic Visual Analytics for Programming Exams

Abstract With the advent of Massive Open Online Courses (MOOCs) educators have the opportunity to collect data from students and use it to derive insightful information about the students. Specifically, for programming based courses the ability to identify the specific areas or topics that need more attention from the students can be of immense help. But the majority of traditional, non-virtual classes lack the ability to uncover such information that can serve as a feedback to the effectiveness of teaching. In majority of the schools paper exams and assignments provide the only form of assessment to measure the success of the students in achieving the course objectives. The overall grade obtained in paper exams and assignments need not present a co... (more)
Created Date 2016
Contributor Pandhalkudi Govindarajan, Sesha Kumar (Author) / Hsiao, I-Han (Advisor) / Nelson, Brian (Committee member) / Walker, Erin (Committee member) / Arizona State University (Publisher)
Subject Educational technology / Educational evaluation / Computer science / Intelligent authoring / Learning Analytics / Orchestration technology / Programming / Semantic Analytics / Visual Analytics
Type Masters Thesis
Extent 63 pages
Language English
Reuse Permissions All Rights Reserved
Note Masters Thesis Computer Science 2016
Collaborating Institutions Graduate College / ASU Library
Additional Formats MODS / OAI Dublin Core / RIS

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Description Dissertation/Thesis