Award Date
August 2023
Degree Type
Dissertation
Degree Name
Doctor of Philosophy (PhD)
Department
Teaching and Learning
First Committee Member
P.G. Schrader
Second Committee Member
Michael McCreery
Third Committee Member
Randall Boone
Fourth Committee Member
Jonathan Hilpert
Number of Pages
249
Abstract
Modern societies exhibit an entanglement between the real- and digital-worlds in their daily routines. Learning has been studied in real and virtual settings, but our understanding of research methods in physical spaces eclipses what is known about best practices related to the definition of variables, measurement, data extraction, and the interpretation of findings in online learning environments (OLE). When viewed as non-traditional OLE, video games and the information and communication technologies that undergird them (e.g., Discord, Reddit, etc.) hold promise in learning research for their ability to evince theories, as platforms for the observation of behavior, and digital petri dishes for data collection and study replication. However, it is often difficult to effectively capture the complexity OLE, like video games, when employing traditional research methods and designs. Relative to other contexts, there are limited examples of how to identify, collect, and analyze rich data from OLE like games. Accordingly, this research built an evidence model using a systems approach for the assessment of learning in novel online contexts (i.e., video games and social media). Educational Data Mining (EDM) and Learning Analytics (LA) techniques were utilized. Data science methods prevalent in video game communities were employed to bolster EDM/LA methods regarding data extraction, transformation, and load processes. A Complex Systems Approach served as a guiding framework to consider the dynamic, emergent, and complex characteristics of learning in interactive environments.
Keywords
Data Science; Educational Data Mining; Learning Analytics; Mixed Methods; Social Media
Disciplines
Education | Educational Psychology
File Format
File Size
2010 KB
Degree Grantor
University of Nevada, Las Vegas
Language
English
Repository Citation
Carroll, Mark, "Toward Data Science for Research in Novel Online Learning Environments: A Mixed Method, Complex Systems Approach" (2023). UNLV Theses, Dissertations, Professional Papers, and Capstones. 4819.
http://dx.doi.org/10.34917/36948169
Rights
IN COPYRIGHT. For more information about this rights statement, please visit http://rightsstatements.org/vocab/InC/1.0/