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Characterizing MOOC Learners from Survey Data Using Modeling and n-TARP Clustering

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Conference

2018 ASEE Annual Conference & Exposition

Location

Salt Lake City, Utah

Publication Date

June 23, 2018

Start Date

June 23, 2018

End Date

July 27, 2018

Conference Session

COED: Online and Blended Learning Part 1

Tagged Division

Computers in Education

Tagged Topic

Diversity

Page Count

13

Permanent URL

https://peer.asee.org/30186

Download Count

16

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Paper Authors

biography

Taylor V. Williams Purdue University, West Lafayette Orcid 16x16 orcid.org/0000-0001-5816-4022

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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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biography

Kerrie A. Douglas Purdue University, West Lafayette

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Dr. Douglas is an Assistant Professor in the Purdue School of Engineering Education. Her research is focused on methods of assessment for large-scale learning environments.

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Tarun Yellamraju Purdue University, West Lafayette

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Tarun Yellamraju is currently a PhD student in the school of Electrical and Computer Engineering at Purdue University. He received his Bachelor of Technology with Honors degree in Electrical Engineering from the Indian Institute of Technology Bombay. His current research interests include High Dimensional Data Analysis and Machine Learning.

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Mireille Boutin Purdue University, West Lafayette Orcid 16x16 orcid.org/0000-0002-0837-6577

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Mireille (Mimi) Boutin is an Associate Professor in Purdue’s School of Electrical and Computer Engineering with a courtesy appointment in the Department of Mathematics. Her past research accomplishments include the development of light-weight methods for language translation on mobile phones, food analysis tools for the treatment of the inherited metabolic disease phenylketonuria, and improved document processing methods for the printing industry. Her current areas of research include signal processing, big data, and various applied mathematics problems motivated by engineering applications. In particular, she is interested in high-dimensional machine learning problems that stem from applications, including data analysis issues related to STEM education research. She created "Project Rhea,” a student-driven online learning project at www.projectrhea.org. She is a three-time recipient of Purdue’s Seed for Success Award. She is also a recipient of the Eta Kappa Nu Outstanding Faculty Award, the Eta Kappa Nu Outstanding Teaching Award and the Wilfred “Duke” Hesselberth Award for Teaching Excellence.

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Abstract

MOOCs (Massive Open Online Courses) attract a diverse and large set of learners, with largely unknown learning needs and expectations. Researchers have begun to explore reasons learners enroll in MOOCs and have found that learners enroll for variety of reasons and have differing levels of prerequisite knowledge. A new approach to understanding the complex grouping of learners is needed. Commonly used clustering algorithms for large datasets do not take into account all of the dimensions a given learner has. In this paper, we present a computationally fast method to identify significant categories of learners based on their responses to pre-course surveys. The purpose of this paper is to test a recently developed modeling and clustering technique, 1-Dimensional Random Projection (RP1D) with survey data from MOOCs. The RP1D method was developed for high-dimensional data and has been used to find patterns in machine generated learner data. However, the approach has not been tested with survey data. The modeling technique proposed allows one to combine different survey questions and deal with missing responses in a robust fashion through use of a rubric. In order to understand the feasibility and appropriateness of this approach to creating learner groups, we ask, “To what degree does the modeling and RP1D clustering technique result in interpretable groups of MOOC learners?” We apply the technique on pre-course survey data acquired in four MOOCs of the following topics, thermodynamics, math puzzles, forensic science, and mindfulness. The survey asked learners questions concerning their goals for the course and their intended participation in Likert-style statements. Applying the modeling and RP1D method resulted in distinguishable groups of learners in each course according to multiple randomly generated criteria. Depending on the course, 2-6% of the random criteria successfully separated the learners into two distinct groups. For example, in the forensic science course, we found 31 criteria (of 1000) that identified significant groupings of learners. Among the best separated of these learner groups was a 57%/43% split based mainly on differences in the learners’ extrinsic motivation dimensions. A different criterion from that same course found a grouping based mainly on their lifestyle dimensions (a 70%/30% split). Additionally, these criteria persist between the STEM and non‑STEM courses. That is, we found learners grouped into similar clusters regardless of course topic. The ability to separate learner types into distinct categories within and across courses is an important step in furthering the goal of enabling MOOC designers to better design online open educational systems to serve their diverse set of learners. The RP1D technique and implications will be further discussed in the paper.

Williams, T. V., & Douglas, K. A., & Yellamraju, T., & Boutin, M. (2018, June), Characterizing MOOC Learners from Survey Data Using Modeling and n-TARP Clustering Paper presented at 2018 ASEE Annual Conference & Exposition , Salt Lake City, Utah. https://peer.asee.org/30186

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