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Work in Progress: A Markov Chain Method for Modeling Student Behaviors

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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

Research Methods

Tagged Division

Educational Research and Methods

Page Count

14

Permanent URL

https://peer.asee.org/31262

Download Count

31

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Paper Authors

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Corey T. Schimpf The Concord Consortium

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Corey Schimpf is a Learning Analytics Scientist with interest in design research, learning analytics, research methods and under-representation in engineering, A major strand of his work focuses on developing and analyzing learning analytics that model students’ cognitive states or strategies through fine-grained computer-logged data from open-ended technology-centered science and engineering projects. His dissertation research explored the use of Minecraft to teach early engineering college students about the design process.

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Molly H. Goldstein Purdue University, West Lafayette (College of Engineering) Orcid 16x16 orcid.org/0000-0002-2382-4745

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Molly Goldstein is a Ph.D. student in the School of Engineering Education at Purdue University, West Lafayette with a research focus on characterizing behaviors in student designers. She previously worked as an environmental engineer specializing in air quality influencing her focus in engineering design with environmental concerns. She earned her B.S. in General Engineering (Systems Engineering & Design) and M.S. in Systems and Entrepreneurial Engineering from the University of Illinois in Urbana-Champaign.

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Robin Adams Purdue University, West Lafayette (College of Engineering)

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Robin S. Adams is an Associate Professor in the School of Engineering Education at Purdue University and holds a PhD in Education, an MS in Materials Science and Engineering, and a BS in Mechanical Engineering. She researches cross-disciplinarity ways of thinking, acting and being; design learning; and engineering education transformation.

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Jie Chao The Concord Consortium

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Jie Chao is a learning scientist with extensive research experience in technology-enhanced learning environments and STEM education. She completed her doctoral and postdoctoral training in Instructional Technology and STEM Education at the University of Virginia. Her past research experiences ranged from fine-grained qualitative mental process analysis to large-scale quantitative and longitudinal investigations. She is currently focusing on learning analytics research in open-ended domains such as engineering design and authentic scientific inquiry. With insights in learning sciences and a strong, computationally oriented mindset, she hopes to utilize learning analytics to investigate important questions with unprecedented granularity and generate actionable knowledge for the design of technology and curriculum.

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Senay Purzer Purdue University, West Lafayette (College of Engineering) Orcid 16x16 orcid.org/0000-0003-0784-6079

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Ṣenay Purzer is an Associate Professor in the School of Engineering Education. She is the recipient of a 2012 NSF CAREER award, which examines how engineering students approach innovation. She serves on the editorial boards of Science Education and the Journal of Pre-College Engineering Education (JPEER). She received a B.S.E with distinction in Engineering in 2009 and a B.S. degree in Physics Education in 1999. Her M.A. and Ph.D. degrees are in Science Education from Arizona State University earned in 2002 and 2008, respectively.

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Charles Xie The Concord Consortium

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Abstract

Students from a middle school (N=152) and from a high school (N=33) completed the same energy-efficient home design challenges in a simulated environment for engineering design (SEED) supported by rich design tool with construction and analysis capabilities, Energy3D. As students design in Energy3D, a log of all of their design actions are collected. In this work-in-progress a subsample of the five most engaged students from both the middle and high school samples are analyzed to identify similarities and differences in their design sequences through Markov chain models. Sequence learning is important to many fields of study, particularly fields that have a large practice component such as engineering and design. Design sequences represent micro-strategies for developing a design. By aggregating these sequences into a model we aim to characterize and compare their design process. Markov chains aid in modeling these sequences by developing a matrix of transition probabilities between actions. Preliminary results suggest we can identify similarities and differences between the groups and that their design sequences reflect important considerations of the design problem. We conclude that Markov chains hold promise for modeling student practices.

Schimpf, C. T., & Goldstein, M. H., & Adams, R., & Chao, J., & Purzer, S., & Xie, C. (2018, June), Work in Progress: A Markov Chain Method for Modeling Student Behaviors Paper presented at 2018 ASEE Annual Conference & Exposition , Salt Lake City, Utah. https://peer.asee.org/31262

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