Understanding the massive open online course (MOOC) student experience: An examination of attitudes, motivations, and barriers
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During the widespread development of open access online course materials in the last two decades, advances have been made in understanding the impact of instructional design on quantitative outcomes. Much less is known about the experiences of learners that affect their engagement with the course content. Through a case study employing text analysis of interview transcripts, we revealed the authentic voices of participants and gained a deeper understanding of motivations for and barriers to course engagements experienced by students participating in Massive Open Online Courses (MOOCs). We sought to understand why learners take the courses, specifically Introduction to Chemistry or Data Analysis and Statistical Inference, and to identify factors both inside and outside of the course setting that impacted engagement and learning. Thirty-six participants in the courses were interviewed, and these students varied in age, experience with the subject matter, and worldwide geographical location. Most of the interviewee statements were neutral in attitude; sentiment analysis of the interview transcripts revealed that 80 percent of the statements that were either extremely positive or negative were found to be positive rather than negative, and this is important because an overall positive climate is known to correlate with higher academic achievement in traditional education settings. When demographic data was added to the sentiment analysis, students who have already earned bachelor's degrees were found to be more positive about the courses than students with either more or less formal education, and this was a highly statistically significant result. In general, students from America were more critical than students from Africa and Asia, and the sentiments of female participants' comments were generally less positive than those of male participants. An examination of student statements related to motivations revealed that knowledge, work, convenience, and personal interest were the most frequently coded nodes (more generally referred to as “codes”). On the other hand, lack of time was the most prevalently coded barrier for students. Other barriers and challenges cited by the interviewed learners included previous bad classroom experiences with the subject matter, inadequate background, and lack of resources such as money, infrastructure, and internet access. These results are enriched by illustrative quotes from interview transcripts and compared and contrasted with previous findings reported in the literature, and thus this study enhances the field by providing the voices of the learners.
Published Version (Please cite this version)10.1016/j.compedu.2017.03.003
Publication InfoCanelas, Dorian; Cetinkaya-Rundel, M; Lee, CH; Li, K; Shapiro, HB; & Wyman Roth, NE (2017). Understanding the massive open online course (MOOC) student experience: An examination of attitudes, motivations, and barriers. Computers and Education, 110. pp. 35-50. 10.1016/j.compedu.2017.03.003. Retrieved from http://hdl.handle.net/10161/15672.
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Associate Professor of the Practice of Chemistry
Prof. Canelas has been active in implementation of student-centered pedagogies and developing programs to increase undergraduate retention in science tracks. Research interests include chemical education research and the scholarship of teaching and learning as well as macromolecules for industrial and biological applications, such as microelectronics, coatings, membranes, gene therapy delivery, and blood compatibility.
Associate Professor of the Practice of Statistical Science
I am the Director of Undergraduate Studies and an Associate Professor of the Practice in the Department of Statistical Science at Duke University. I received my Ph.D. in Statistics from the University of California, Los Angeles, and a B.S. in Actuarial Science from New York University’s Stern School of Business. My work focuses on innovation in statistics pedagogy, with an emphasis on student-centered learning, computation, reproducible research, and open-source education.
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