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Beyond the Means – Visualizing Learner Activity and Outcomes for Online Instructors

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2019 ASEE Annual Conference & Exposition


Tampa, Florida

Publication Date

June 15, 2019

Start Date

June 15, 2019

End Date

October 19, 2019

Conference Session

Technical Session 7: Online and Distributed Learning

Tagged Division

Computers in Education

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Paper Authors


Taylor V. Williams Purdue University-Main Campus, West Lafayette (College of Engineering) Orcid 16x16

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Taylor Williams is a Ph.D. student in Purdue's school of engineering education. He is currently on an academic leave from his role as an instructor of engineering at Harding University. While at Harding he taught undergraduate biomedical, computer, and first-year engineering. Taylor also spent time working in industry as a systems engineer. Taylor received his master's in biomedical engineering from Tufts University and his bachelor's in computer engineering and mathematics from Harding University. His primary research interest is in how to use machine learning in fully online and hybrid educational environments to understand students and improve their learning.

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Kerrie A. Douglas Purdue University-Main Campus, West Lafayette (College of Engineering) Orcid 16x16

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Dr. Douglas is an Assistant Professor in the Purdue School of Engineering Education. Her research is focused on improving methods of assessment in large learning environments to foster high-quality learning opportunities. Additionally, she studies techniques to validate findings from machine-generated educational data.

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Peter Bermel Purdue University-Main Campus, West Lafayette (College of Engineering) Orcid 16x16

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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 5500 times, for an h-index value of 28, 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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Hillary E. Merzdorf Purdue University-Main Campus, West Lafayette (College of Engineering)

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College of Engineering

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Massive open online courses (MOOCs) provide an opportunity for motivated individuals to expand their education in ways previously unimaginable. However, for a variety of reasons, most MOOC learners stop engaging after interacting with only a fraction of the course, potentially for reasons related to the course itself. While it is well known that traditional educational metrics must be contextualized for the MOOC environment, there is little to guide instructors, instructional staff, or others regarding exactly how to make sense of the outcomes. Moreover, since different types of courses may attract different student populations with different behavior patterns, comparing learner behavior seen in specialized or advanced MOOCs with that seen in MOOCs generally may be unwarranted.

The resulting research question is, "In what ways do different types of learners interact differently with advanced STEM MOOCs?" In this study, we examined multiple advanced STEM MOOCs offered by nanoHUB on the edX platform. We first identified users’ behavioral patterns for each course through clustering how much learners interacted with the course. In each course, we found the proportion of students who belong to each cluster, the amount of each type of course material each cluster interacted with, and how recently students in each cluster engaged with the course. So far, across the courses, we see consistent patterns of engagement for each cluster as well as a consistent distribution of students within each cluster. Using these patterns, we have developed descriptions for five types of advanced STEM MOOC users.

Based on our findings, the next goal will be to create benchmarks for advanced STEM MOOCs by incorporating more courses into our analysis. Comparing course usage with similar courses could help us determine at what points learners’ behavior is typical and where it deviates from what is expected—perhaps indicating that the instructor should intervene before more learners depart. With these benchmarks, instructors running new courses or interventions will have baseline data to which they can compare their own students’ behavior.

Williams, T. V., & Douglas, K. A., & Bermel, P., & Merzdorf, H. E. (2019, June), Beyond the Means – Visualizing Learner Activity and Outcomes for Online Instructors Paper presented at 2019 ASEE Annual Conference & Exposition , Tampa, Florida. 10.18260/1-2--32152

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