Work in progress: A Markov chain method for modeling student behaviors

Corey T. Schimpf, Molly H. Goldstein, Robin Adams, Jie Chao, Senay Purzer, Charles Xie

Research output: Contribution to journalConference article

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.

Original languageEnglish (US)
JournalASEE Annual Conference and Exposition, Conference Proceedings
Volume2018-June
StatePublished - Jun 23 2018
Event125th ASEE Annual Conference and Exposition - Salt Lake City, United States
Duration: Jun 23 2018Dec 27 2018

Keywords

  • Engineering design
  • Large learner data
  • Markov chains
  • Research methods

ASJC Scopus subject areas

  • Engineering(all)

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