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Instructional Technology and Learning Analytics in Online Geographic Information Science (GIS) Education

  • Author(s): Burkhart, Nicholas Jacob
  • Advisor(s): Shin, Michael E
  • et al.
Abstract

As geographic information systems (GIS) technology continues to mature and as university instruction progressively shifts to online learning environments, it is the responsibility of geography educators and the academy more broadly to develop and refine practices for teaching and learning GIS in online environments. Educators of all specializations teaching online today are confronted not only with the challenges and uncertainties endemic to online learning environments, but also with exciting opportunities for implementing data-driven optimization of instructional design and student learning outcomes using educational “big data” -- detailed and automatically captured records of actions taken by students within Web-based learning management system (LMS) environments. This dissertation presents three stand-alone research articles that reside at the nexus connecting GIS education to online education research, and most notably to the emerging field of learning analytics. Each article contains a case study built around educational ``big data'' collected within an online introductory GIS course. The first research article, which is focused upon instructional technology specific to GIS education, assesses the suitability of open source GIS software for supporting introductory online education in GIS. The two remaining research articles relate to computational analysis of data derived from Web-based LMS software. The second research article introduces descriptive and inferential analytics, as well as accompanying and contextualizing data visualizations, that can be derived from the processing and analysis of raw LMS student activity log data. The third research article uses LMS data as a vehicle for both (1) proposing new methodology for approximating student behavior, including procrastination and productivity, and (2) exploring the many relationships that exist between student behavior and student performance in online courses. An additional methodological chapter introduces a new LMS log analysis software tool originally developed to perform the data analysis and visualization for this dissertation; a technical overview of the software's functionality is presented therein to serve as a guide for online educators who wish to begin working with LMS log analysis.

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