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Learners in Advanced Nanotechnology MOOCs: Understanding their Intention and Motivation

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Conference

2016 ASEE Annual Conference & Exposition

Location

New Orleans, Louisiana

Publication Date

June 26, 2016

Start Date

June 26, 2016

End Date

August 28, 2016

ISBN

978-0-692-68565-5

ISSN

2153-5965

Conference Session

Online, Hybrid, and other Virtual Learning Environments

Tagged Division

Computers in Education

Page Count

16

DOI

10.18260/p.25531

Permanent URL

https://peer.asee.org/25531

Download Count

189

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Paper Authors

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Kerrie Anna Douglas Purdue University, West Lafayette

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Dr. Douglas is a Visiting Assistant Professor in the Purdue School of Engineering Education. Her research is focused on methods of assessment and evaluation unique to engineering learning contexts.

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Brittany Paige Mihalec-Adkins Purdue University

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Brittany Mihalec-Adkins is a graduate student in Educational Psychology at Purdue University. Her research interests include academic identity development, educational trends among marginalized groups, and educational interventions. Upon completion of her M.S.Ed in August of 2016, Brittany will begin working toward her PhD in Human Development and Family Studies at Purdue University.

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Nathan M. Hicks Purdue University, West Lafayette Orcid 16x16 orcid.org/0000-0003-2512-8484

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Nathan M. Hicks is a Ph.D. student in Engineering Education at Purdue University. He received his B.S. and M.S. degrees in Materials Science and Engineering at the University of Florida and taught high school math and science for three years.

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Heidi A. Diefes-Dux Purdue University, West Lafayette Orcid 16x16 orcid.org/0000-0003-3635-1825

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Heidi A. Diefes-Dux is a Professor in the School of Engineering Education at Purdue University. She received her B.S. and M.S. in Food Science from Cornell University and her Ph.D. in Food Process Engineering from the Department of Agricultural and Biological Engineering at Purdue University. She is a member of Purdue’s Teaching Academy. Since 1999, she has been a faculty member within the First-Year Engineering Program, teaching and guiding the design of one of the required first-year engineering courses that engages students in open-ended problem solving and design. Her research focuses on the development, implementation, and assessment of modeling and design activities with authentic engineering contexts. She is currently a member of the educational team for the Network for Computational Nanotechnology (NCN).

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Peter Bermel Purdue University Orcid 16x16 orcid.org/0000-0001-7140-0667

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DR. PETER BERMEL is an assistant professor of Electrical and Computer Engineering at Purdue University. His research focuses on improving the performance of photovoltaic, thermophotovoltaic, and nonlinear systems using the principles of nanophotonics. Key enabling techniques for his work include electromagnetic and electronic theory, modeling, simulation, fabrication, and characterization.

Dr. Bermel is widely-published in both scientific peer-reviewed journals and publications geared towards the general public. His work, which has been cited over 3290 times, for an h-index value of 20, includes the following topics:
* Understanding and optimizing the detailed mechanisms of light trapping in thin-film photovoltaics
* Fabricating and characterizing 3D inverse opal photonic crystals made from silicon for photovoltaics, and comparing to theoretical predictions
* Explaining key physical effects influencing selective thermal emitters in order to achieve high performance thermophotovoltaic systems

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Krishna Madhavan Purdue University, West Lafayette

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Dr. Krishna Madhavan is an Associate Professor in the School of Engineering Education at Purdue University. He is Co-PI and Education Director of the NSF-funded Network for Computational Nanotechnology (nanoHUB.org which serves over 330,000 global researchers and learners annually). Dr. Madhavan was the Chair of the IEEE/ACM Supercomputing Education Program 2006. In January 2008, he was awarded the US National Science Foundation (NSF) CAREER award for work on learner-centric, adaptive cyber-tools and cyber-environments. He was one of 49 faculty members selected as the nation’s top engineering educators and researchers by the US National Academy of Engineering to the Frontiers in Engineering Education symposium. Dr. Madhavan leads a major NSF funded effort called Deep Insights Anytime, Anywhere (DIA2) that attempts to characterize the impact of NSF and other federal investments in the area of science, technology, engineering, and mathematics education using interactive knowledge mining and visual analytics for non-experts in data mining. DIA2 is currently deployed inside the NSF and is already starting to affect federal funding policy. Dr. Madhavan also served as Visiting Research Scientist at Microsoft Research, Internet Services Research Group. His research has been published in Nature Nanotechnology, IEEE Transactions on Computer Graphics and Applications, IEEE Transactions on Learning Technologies, and several other top peer-reviewed venues. Dr. Madhavan currently serves as PI or Co-PI on federal and industry funded projects totaling over $20M.

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Abstract

Learners in Advanced Nanotechnology MOOCs: Understanding their Intention and Motivation

Instructional design principles begin with an assessment of the learner. Yet in MOOCs, little is known about learners’ intentions for the course and what their motivation is. In the MOOC environment, learners can enroll in a course with little or no commitment. Indeed, many learners may choose to utilize the course materials more like a textbook; selecting topics of interest, rather than going through the entire sequence. Other learners may desire to fully participate in all aspects. In order to design MOOCs for optimal learning, it is essential that researchers are able to understand what learners want from the experience.

The context of this study is within three-advanced nanotechnology related MOOCs offered through a large MOOC platform provider. By providing cutting-edge research knowledge related to nanotechnology, university faculty have potential to decrease the time between research findings and use by engineers in industry. However, it is unknown whether learners in courses are simply interested in the advanced topics out of curiosity or whether they intend to use the information to further their professional development. Before researchers can study how learning can occur most effectively within the MOOC model, there must first be a level of understanding what learners want from the freely offered content.

From general cognitive view of motivation and learning strategies, we approach understanding motivation and intention of learners in advanced nanotechnology MOOCs. In this research paper, we explore learners in three-advanced nanotechnology MOOCs for the purpose of informing future nanotechnology-related MOOC offerings. This research was guided by the questions, (1) What is the diversity of learners within an advanced nanotechnology MOOC? (2) What are learners’ types of motivation? (3) What are learners’ reasons for enrolling in the course? and (4) What are learners intentions in terms of participation?

To answer these research questions, surveys were embedded as part of the first week’s course material in three courses and combined into a single data set (n = 2507). Questions included two subscales of the Motivational Learning Styles Questionnaire (Pintrich, 1991) as well as other background questions to understand more about learners’ prior knowledge to the course, level of preparation and expectations.

Preliminary Results suggest that on a scale of 1 to 7, learners are significantly more intrinsically (M = 5.76) motivated than externally (M = 3.54) motivated. This suggests that learners are primarily taking the course for their own personal knowledge. Additionally, forty-one percent indicated that they intended to participate in all aspects of the course. Thirty-nine percent said they desired to apply the material learned in the course directly to future projects. The paper will elaborate on results and compare learner types. In addition, the paper will discuss implications for research and learning in engineering MOOC environments.

Douglas, K. A., & Mihalec-Adkins, B. P., & Hicks, N. M., & Diefes-Dux, H. A., & Bermel, P., & Madhavan, K. (2016, June), Learners in Advanced Nanotechnology MOOCs: Understanding their Intention and Motivation Paper presented at 2016 ASEE Annual Conference & Exposition, New Orleans, Louisiana. 10.18260/p.25531

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