How People Learn Engineering: Two Examples and A Call to Action

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How People Learn EngineeringTwo Examples and A
Call to Action

• Cynthia J. Atman
• Director, Center for Engineering Learning and
  Teaching
• Associate Professor, Industrial Engineering
• University of Washington
• Richard Felder
• Hoechst Celanese Professor Emeritus of Chemical
  Engineering
• North Carolina State University
• Jennifer E. Turns
• Assistant Professor, Technical Communication
  Department
• University of Washington

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Acknowledgments

• Grants National Science Foundation (RED-9358516,
  NSF-9714459, EEC-9872498), GE Fund, Ford Motor
  Company Fund, Lockheed Martin, SUCCEED Coalition
  (EEC-9727411), Westinghouse Foundation, Xerox
  Corporation, University of Washington.
• Data analysis Robin Adams (University of
  Washington), Mary Sacre, Heather Nachtmann and
  Justin Chimka (University of Pittsburgh), and
  teams of undergraduate and graduate students from
  the University of Washington, the University of
  Pittsburgh, and North Carolina State University.

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Thinking About Engineering Student Learning

• How do engineering students gather information?
• How do engineering students solve engineering
  design problems?
• Do some teaching methods work better than others?
  Do they work better for all students or only
  certain types of learners?
• Do engineering graduates define problems broadly?
• Do engineering graduates integrate the concepts
  they are learning linking them to each other?

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Why are these important questions?

• We are surrounded by engineered artifacts that
  are getting more complex to design and maintain
• Engineering graduates need the skills to help
  them succeed in this environment immediately upon
  graduation and throughout their careers
• Engineering educators need to design educational
  experiences that develop these skills

We need better information about how
engineering students learn!
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Can the Engineering Education Community Answer
These Questions?
Rene Magritte, c. 1956
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Two Slices

• Slice One Design Processes
• Slice Two Teaching and Learning
• A Call to Action Need More Research

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Slice One Design Processes

• How do engineering students gather information?
• How do engineering students solve engineering
  design problems?

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Playground Problem Statement

• You live in a mid-size city. A local resident
  has recently donated a corner lot for a
  playground. Since you are an engineer who lives
  in the neighborhood, you have been asked by the
  city to design a playground.
• You estimate that most of the children who will
  use the playground will range from 1 to 10 years
  of age...(other specifications)...Your design
  should use materials that are available at any
  hardware or lumber store. The playground must be
  ready for use in 2 months.
• Please take a moment and start a list of the
  information you would need to solve this problem.

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Experimental Design

• 26 entering students, 24 graduating students
• Graduating students from CE, IE and ME
• Solved Playground Problem talking out loud
• Asked experiment administrator for information as
  solved problem
• Took from 2 to 3 hours

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Categories of Information
Material costs Material specifications Neighborhoo
d demographics Neighborhood opinions Safety Superv
ision concerns Technical references Utilities
Availability of materials Body dimensions Budget H
andicapped accessibility Information about the
area Labor availability and costs Legal
liability Maintenance concerns
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Results Information Gathering
Bursic, Karen M. and Cynthia J. Atman,
Information Gathering A Critical Step for
Quality in the Design Process, Quality
Management Journal, vol. 4, no. 4, pp. 60-75,
1997.
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Design Process Activities(Derived from analysis
of 7 engineering texts)

• Identification of a Need
• Problem Definition
• Information Gathering
• Generation of Ideas
• Modeling
• Feasibility of analysis
• Evaluation
• Decision
• Communication
• Implementation

Problem Scoping Developing Alternative
Solutions Project Realization
Areas where recent reports say students need to
improve.
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Verbal Protocol Analysis

• In our studies on design
• Subjects solve design problem while talking aloud
• Transcribe protocol
• Segment transcript into codable chunks of
  subject statements (reliability check)
• Code transcript (reliability check)
• Analyze to answer specific research questions

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Results Entering vs. Graduating Students

• Graduating students
• had higher quality designs (whew!!)
• gathered more information, covering more
  categories
• made more transitions among design steps
• spent more time iterating and iterate more
  effectively (Adams, 2001)
• progressed farther in the design process

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Design Process Timelines
Successful Graduating Student (High Quality Score
0.63)
Canonical Entering Student (Low Quality Score
0.37)
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Design Process Timelines
Entering University Students Graduating
University Students
Atman, Cynthia J., Justin R. Chimka, Karen M.
Bursic, and H. L. Nachtmann, A Comparison of
Freshman and Senior Engineering Design
Processes, Design Studies, vol. 20, no. 2, pp.
131-152, March 1999.
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Slice Two Teaching and Learning

• Do some teaching methods work better than
  others? Do they work better for all students or
  only certain types of learners?

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A Longitudinal Study of Engineering Student
Performance and Retention

• Teach five CHE courses in successive semesters
  using
• inductive, active, cooperative learning
• problems that address a variety of disciplines
  and thinking skills
• lab plant visits, exposure to practicing
  engineers
• carefully designed tests, criterion-referenced
  grading

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• Collect
• demographic data, precollege credentials, MBTI
  LASSI profiles, first-year grades
• academic performance and retention data from
  first CHE course through graduation
• responses to survey questions about attitudes to
  courses, confidence levels, career goals
• Compare academic and affective outcomes for
  experimental group and traditionally-taught
  comparison group

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Groups Studied

• Experimental Group
• N 123
• 29 female, 71 male
• 6 Afr.-Amer., 5 Asian-Amer., 84 white, 5
  other
• Taught with method outlined in 5 courses

• Comparison Group
• N 190
• 36 female, 64 male
• 7 Afr.-Amer., 10 Asian-Amer., 81 white, 2
  other
• Taught traditionally in all courses

No between-group demographic differences
statistically significant at .05 level.
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Grade Distributions inIntroductory Course
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Percent Giving Highest Rating to Quality of
Preparation by Prerequisite Courses
plt.001
p.16
No sequence courses among prerequisites
Capstone design course
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Seniors Ratings of the Classroom Environment
plt.001
plt.001
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Seniors Ratings of CHE Instructional Quality
plt.001
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5-Year Graduation and Attrition Rates
plt.01
plt.01
Grad graduated in chemical engineering
Left switched curricula or dropped out of
school
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Other Results

• Gender differences
• Rural/urban differences
• MBTI type effects
• Self-assessments of problem-solving abilities
  (basic creative), self-confidence levels,
  post-graduation plans career goals

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A Call to Action

• We need more slices!!!

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A Platform Exists to Build FromResearch Results

• What do we know from research in other fields?
• Expert/novice differences
• Situated cognition
• Conceptual change
• Problem-based learning
• ..

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A Platform Exists to Build From Leveraging
Results

• How do we leverage these results?
• Provide insights
• Provide vocabulary
• Help us to select areas likely to succeed
• ..
• -gt BUT, cannot replace research in engineering
  learning

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Moving into Engineering EducationResearch
Questions

• What is the nature of engineering expertise?
• How do engineers integrate knowledge and apply it
  to solve engineering problems?
• How do we adapt teaching innovations known to be
  successful in other domains to engineering?
• How do we motivate students?
• ..

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Moving into Engineering Education Multiple
Approaches

• Example Study Designs
• Responses to teaching methods
• Responses by sub-populations (coop vs. non-coop,
  ...)
• Detailed descriptions of behaviors,
  understandings, skills, attitudes by
• Case studies
• Observation or interview
• Verbal protocol analysis
• Meta-analyses
• Statistical analyses of factors contributing to
  performance

• Issues
• Qualitative and quantitative
• Presence of control/ comparison groups
• Balancing size of populations with depth of
  analysis
• One shot vs. longitudinal studies
• Triangulation

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