Transcript 4S 2011 Presentation - Consortium for Science, Policy & Outcomes

INTEGRATING MICROETHICS AND
MACROETHICS IN GRADUATE SCIENCE AND
ENGINEERING EDUCATION
Joseph Herkert
Karin Ellison
Heather Canary
and Jameson Wetmore
Integrating Microethics and Macroethics in Graduate
Science and Engineering Education:
Development and Assessment of Instructional
Models NSF/EESE #0832944
• Develop integrated learning objectives for
graduate students
• Apply learning objectives in four educational
models
• Assess student learning
• Share knowledge and materials
Project Team
Joseph Herkert, ASU, PI
Heather Canary, Utah, Co-PI
Karin Ellison, ASU, Co-PI
Jameson Wetmore, ASU,
Co-PI
JoAnn Williams, ASU
Ira Bennett, ASU
Brad Allenby, ASU
Jonathan Posner, ASU
Joan McGregor, ASU
Dave Guston, ASU
Consultants
Deborah Johnson, Virginia
Rachelle Hollander, NAE
Nick Steneck, Michigan
Advisory Council
Kristen Kulinowski, Rice
Dean Nieusma, RPI
Sarah Pfatteicher,
Wisconsin
Karl Stephan, Texas State
Coordination Workshop
Feb 2009
• Consultants presented
background of grad
education in science
and engineering ethics
• ASU team members
presented four models
• Discussions
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Issues and outcomes
Pedagogy
Assessment models
Four Educational Models
• Stand-alone course
• Technical course with embedded ethics
content
• Online/Classroom hybrid
• Lab group engagement
Fundamentals of Biological Design
• Micro- and macroethical
content included in a
required technical course
for scientists and engineers
• Ethics introduced in context
with other professional
knowledge and skills
• Model takes advantage of
learning opportunities as
they arise
Introduction to RCR in the Life Science
• Classroom/Online Hybrid
• One-credit course
• Required for some life science graduate
students
• Taught every other semester
• Students prepare using online materials
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CITI Program RCR modules
ASU, “The Humane Care and Use of Laboratory
Animals”
NIH, "Protecting Human Research Participants."
• Classroom sessions focus on case analysis and
discussion
Science Policy for Scientists and
Engineers
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Stand-alone course
One-credit
Meets CHM 501 requirement
Taught every semester
Topic and focus change each
semester
• Called “science policy for
scientists and engineers” to
enhance the macroethical
content and avoid student and
advisor biases toward the
E(thics) word
• Students choose half of the
readings to ensure coverage of
timely topics of interest
Lab Group Engagement
Goal: To create a place where expertise from various fields can
be exchanged, discussed, debated, and shared; will create
an environment where both ethicists and scientists learn
more about the ethics of emerging technologies.
Three Research Questions
1. Will this method provide an opportunity to help scientists and
engineers understand the ethical and social implications of
their work?
2. Will this method empower those who shape the direction of
innovation to reflect on the social implications of their work?
3. Can ethicists gain access to information in laboratories about
future technologies that are not readily available in other
places?
Assessment
• Fall 2009 - Spring 2011
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Embedded Model (N = 21)
Stand-Alone Model (N = 14)
Hybrid Model (N = 20)
Lab Model (N = 2; excluded from analysis)
Control Group (N = 26)
• Study-specific outcome measures for: data management,
conflicts of interest, sustainability, military research
- Knowledge of relevant standards
- Ethical sensitivity
- Ethical reasoning
Existing measures of moral reasoning
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Engineering and Science Issues Test (ESIT), Borenstein, Kirkman &
Swann, 2005
Moral Judgment Test (MJT), Lind, 2002
• Student-instructor communication (post test only)
Knowledge of Relevant Standards
• Summed Scale; Possible Range = 0 – 16
• Increase from pretest to posttest, experimental
groups:
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t(54) = 5.02, p < .001
MA = 11.89, SD = 2.15; MB = 13.31, SD = 04
• Significant group differences on posttest:
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F(3,78) = 11.03, p < .001
All three experimental groups significantly higher than
Control group
Hybrid group significantly higher than Stand Alone
and Embedded groups
Sensitivity to Ethical Issues
• Mean of 14 Items on 1 – 5 Scale
• Increase from pretest to posttest, experimental
groups:
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t(53) = 3.03, p < .01
MA = 3.37, SD = .38; MB = 3.54, SD = .43
• Significant group differences on posttest
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F(3,78) = 3.99, p = .01
Control group significantly lower than all experimental
groups
No significant differences between experimental
groups
Engineering & Science Issues Test (ESIT)
(Borenstein et al., 2009)
• Two outcome scores
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P-Score = percentage of postconventional reasoning
N2-Score = uses P-Score & accounts for absence of
preconventional thinking
• No overall significant gains in P-Score
• Increase from pretest to posttest, experimental
groups:
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T(53) = 2.54, p < .05
MA = 8.22, SD = 3.92; MB = 9.25, SD = 4.37
ESIT, Continued
• Significant group differences in N2-Score
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F(3,77) = 5.36, p < .01
All experimental groups significantly higher than Control
group
Student-Instructor Communication
• Instructor Argumentativeness (1-50) – productive &
positive engagement in content arguments
• Instructor Verbal Aggressiveness (1-50) – counterproductive & negative verbal attacks
• Out-of-Class Communication (1-45) – interactions with
instructor outside of classroom context
• Supportive Classroom Climate (1-40) – instructor fosters
safe environment for learning & discussion
• Defensive Classroom Climate (1-45) – instructor fosters
negative & defensive environment
• Open-ended questions: most memorable discussion;
effective & ineffective teaching methods; value &
relevance of discussions; perceived role in society
Communication Analyses
• All experimental groups team-taught with 2 or 3
instructors
• Aggregate instructor communication means compared
across groups, only 1 group difference:
 Instructor Verbal Aggressiveness, Embedded Group
higher (M = 18.33) than Hybrid Group (M = 14.21)
• Ethical Sensitivity (posttest) significant correlations with
Defensive Classroom Climate (r=-.37, p=.01) and Out-ofClass Communication (r=-.35, p=.01).
• Supportive Classroom Climate & Argumentativeness
(r=.42, p=.01); Supportiveness & Out of Class
Communication (r=.57, p=.01)
• Defensive Classroom Climate & Verbal Aggressiveness
(r=.55, p=.01)
Assessment Conclusions
• Students in ALL experimental groups showed gains;
gains significantly higher than control group gains
• All instructional models improve students’ ethical
knowledge, sensitivity, and reasoning
• Study-specific measures of knowledge of standards and
ethical sensitivity tap changes and correlate with existing
measures
• ESIT more appropriate for this population than MJT
• Study-specific measure of moral reasoning might not be
robust; ESIT seems more effective to measure changes
• Instructor-student communication related to student
ethical sensitivity and to student perceptions of
classroom climate
November 10-11, 2011
Tempe, Arizona
This two day meeting will bring together a wide array of
educators to share the programs, materials, and experience
they’ve already developed as well as pioneer new strategies
to help scientists and engineers understand the social and
ethical implications of research.
Sponsored by the ASU Center for Nanotechnology in Society, the
Consortium for Science, Policy & Outcomes, the National
Nanotechnology Infrastructure Network, and ASU NSF/EESE Grants
Acknowledgements
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National Science Foundation
Biological Design Ph.D. Program
Center for Biology and Society
Center for Nanotechnology and Society
Consortium for Science, Policy &
Outcomes
• Lincoln Center for Applied Ethics