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2017 ASEE Annual Conference & Exposition

Board # 32 : NSF PRIME Project: Contextualized Evaluation of Advanced STEM MOOCs

Presented at NSF Grantees Poster Session

Contextualized Evaluation of Advanced STEM MOOCs

While it is well-known that massive open online courses, MOOCs, attract large numbers of enrollees but few of whom complete, less is known about how to evaluate MOOCs and what evaluation metrics are appropriate in open online educational environments. The openness of the MOOC environment presents major challenges to understanding how or what type of learning is occurring and whether the pedagogies used are particularly effective. Furthermore, with heterogeneous learners in the thousands, little is known about MOOC users and what they hope to gain, and whether they actually achieve their learning goals. The primary research goal of this NSF-funded project is to provide theoretical foundations for evaluation in online learning environments and increase the capacity for science, engineering, technology, and mathematics (STEM) MOOC evaluation and research. This project focuses on the following research questions: (1) What constructs contribute to learners’ behavior in advanced STEM MOOCs? (2) What information is needed to inform stakeholders decision-making regarding the value of offering advanced STEM MOOCs? How do stakeholder needs drive the design of MOOCs? and (3)What is a context sensitive, generalizable framework of evaluation for advanced STEM MOOCs? We are undertaking 3 lines of research to address these research questions. In our poster, we will present results from each of the three lines of research from this project: stakeholder interviews, surveys of learners and data analytics to inform a typology of MOOC users.

Authors
  1. Dr. Kerrie A Douglas Orcid 16x16http://orcid.org/0000-0002-2693-5272 Purdue University, West Lafayette (College of Engineering) [biography]

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

  2. Prof. Heidi A. Diefes-Dux Orcid 16x16http://orcid.org/0000-0003-3635-1825 Purdue University, West Lafayette (College of Engineering) [biography]

    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.

  3. Prof. Peter Bermel Orcid 16x16http://orcid.org/0000-0001-7140-0667 Purdue University [biography]

    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 4400 times, for an h-index value of 24, 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

  4. Dr. Krishna Madhavan Purdue University, West Lafayette (College of Engineering) [biography]

    Dr. Krishna Madhavan is an Associate Professor in the School of Engineering Education. In 2008 he was awarded an NSF CAREER award for learner-centric, adaptive cyber-tools and cyber-environments using learning analytics. He leads a major NSF-funded project called Deep Insights Anytime, Anywhere (http://www.dia2.org) to characterize the impact of NSF and other federal investments in the area of STEM education. He also serves as co-PI for the Network for Computational Nanotechnology (nanoHUB.org) that serves hundreds of thousands of researchers and learners worldwide. Dr. Madhavan served as a Visiting Researcher at Microsoft Research (Redmond) focusing on big data analytics using large-scale cloud environments and search engines. His work on big data and learning analytics is also supported by industry partners such as The Boeing Company. He interacts regularly with many startups and large industrial partners on big data and visual analytics problems.

  5. Nathan M. Hicks Orcid 16x16http://orcid.org/0000-0003-2512-8484 Purdue University, West Lafayette (College of Engineering) [biography]

    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 was formerly a high school mathematics and science teacher.

  6. Mr. Taylor V. Williams Orcid 16x16http://orcid.org/0000-0001-5816-4022 Purdue University, West Lafayette (College of Engineering) [biography]

    Taylor Williams currently has dual roles: an instructor of biomedical and computer engineering at Harding University and a Ph.D. candidate in Purdue's school of engineering education. At Harding, he teaches undergraduate courses in biomedical, computer, and first-year engineering. He particularly enjoys incorporating service-learning into his courses. 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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