1. I NTEGRATING M ICROETHICS AND M ACROETHICS IN G RADUATE S CIENCE AND E NGINEERING E DUCATION Joseph Herkert Karin Ellison Heather Canary and Jameson Wetmore

2. 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

3. 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

4. Coordination Workshop Feb 2009 Consultants presented background of grad education in science and engineering ethicsConsultants presented background of grad education in science and engineering ethics ASU team members presented four modelsASU team members presented four models DiscussionsDiscussions Issues and outcomes Issues and outcomes Pedagogy Pedagogy Assessment models Assessment models

5. Four Educational Models Stand-alone courseStand-alone course Technical course with embedded ethics contentTechnical course with embedded ethics content Online/Classroom hybridOnline/Classroom hybrid Lab group engagementLab group engagement

6. Fundamentals of Biological Design Micro- and macroethical content included in a required technical course for scientists and engineersMicro- and macroethical content included in a required technical course for scientists and engineers Ethics introduced in context with other professional knowledge and skillsEthics introduced in context with other professional knowledge and skills Model takes advantage of learning opportunities as they ariseModel takes advantage of learning opportunities as they arise

7. Introduction to RCR in the Life Science Classroom/Online HybridClassroom/Online Hybrid One-credit courseOne-credit course Required for some life science graduate studentsRequired for some life science graduate students Taught every other semesterTaught every other semester Students prepare using online materialsStudents prepare using online materials CITI Program RCR modules CITI Program RCR modules ASU, “The Humane Care and Use of Laboratory Animals” ASU, “The Humane Care and Use of Laboratory Animals” NIH, "Protecting Human Research Participants." NIH, "Protecting Human Research Participants." Classroom sessions focus on case analysis and discussionClassroom sessions focus on case analysis and discussion

8. Science Policy for Scientists and Engineers Stand-alone courseStand-alone course One-creditOne-credit Meets CHM 501 requirementMeets CHM 501 requirement Taught every semesterTaught every semester Topic and focus change each semesterTopic 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) wordCalled “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 interestStudents choose half of the readings to ensure coverage of timely topics of interest

9. 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?

10. Assessment Fall 2009 - Spring 2011Fall 2009 - Spring 2011 – 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 researchStudy-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 – Engineering and Science Issues Test (ESIT), Borenstein, Kirkman & Swann, 2005 – Moral Judgment Test (MJT), Lind, 2002 Student-instructor communication (post test only)Student-instructor communication (post test only)

11. Knowledge of Relevant Standards Summed Scale; Possible Range = 0 – 16Summed Scale; Possible Range = 0 – 16 Increase from pretest to posttest, experimental groups:Increase from pretest to posttest, experimental groups: t(54) = 5.02, p <.001 t(54) = 5.02, p <.001 M A = 11.89, SD = 2.15; M B = 13.31, SD = 04 M A = 11.89, SD = 2.15; M B = 13.31, SD = 04 Significant group differences on posttest:Significant group differences on posttest: F(3,78) = 11.03, p <.001 F(3,78) = 11.03, p <.001 All three experimental groups significantly higher than Control group All three experimental groups significantly higher than Control group Hybrid group significantly higher than Stand Alone and Embedded groups Hybrid group significantly higher than Stand Alone and Embedded groups

12. Sensitivity to Ethical Issues Mean of 14 Items on 1 – 5 ScaleMean of 14 Items on 1 – 5 Scale Increase from pretest to posttest, experimental groups:Increase from pretest to posttest, experimental groups: t(53) = 3.03, p <.01 t(53) = 3.03, p <.01 M A = 3.37, SD =.38; M B = 3.54, SD =.43 M A = 3.37, SD =.38; M B = 3.54, SD =.43 Significant group differences on posttestSignificant group differences on posttest F(3,78) = 3.99, p =.01 F(3,78) = 3.99, p =.01 Control group significantly lower than all experimental groups Control group significantly lower than all experimental groups No significant differences between experimental groups No significant differences between experimental groups

13. Engineering & Science Issues Test (ESIT) (Borenstein et al., 2009) Two outcome scoresTwo outcome scores P-Score = percentage of postconventional reasoning P-Score = percentage of postconventional reasoning N2-Score = uses P-Score & accounts for absence of preconventional thinking N2-Score = uses P-Score & accounts for absence of preconventional thinking No overall significant gains in P-ScoreNo overall significant gains in P-Score Increase from pretest to posttest, experimental groups:Increase from pretest to posttest, experimental groups: T(53) = 2.54, p <.05 T(53) = 2.54, p <.05 M A = 8.22, SD = 3.92; M B = 9.25, SD = 4.37 M A = 8.22, SD = 3.92; M B = 9.25, SD = 4.37

14. ESIT, Continued Significant group differences in N2-ScoreSignificant group differences in N2-Score F(3,77) = 5.36, p <.01 F(3,77) = 5.36, p <.01 All experimental groups significantly higher than Control group All experimental groups significantly higher than Control group

15. Student-Instructor Communication Instructor Argumentativeness (1-50) – productive & positive engagement in content argumentsInstructor Argumentativeness (1-50) – productive & positive engagement in content arguments Instructor Verbal Aggressiveness (1-50) – counter- productive & negative verbal attacksInstructor Verbal Aggressiveness (1-50) – counter- productive & negative verbal attacks Out-of-Class Communication (1-45) – interactions with instructor outside of classroom contextOut-of-Class Communication (1-45) – interactions with instructor outside of classroom context Supportive Classroom Climate (1-40) – instructor fosters safe environment for learning & discussionSupportive Classroom Climate (1-40) – instructor fosters safe environment for learning & discussion Defensive Classroom Climate (1-45) – instructor fosters negative & defensive environmentDefensive 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 societyOpen-ended questions: most memorable discussion; effective & ineffective teaching methods; value & relevance of discussions; perceived role in society

16. Communication Analyses All experimental groups team-taught with 2 or 3 instructorsAll experimental groups team-taught with 2 or 3 instructors Aggregate instructor communication means compared across groups, only 1 group difference: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) 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-of- Class Communication (r=-.35, p=.01).Ethical Sensitivity (posttest) significant correlations with Defensive Classroom Climate (r=-.37, p=.01) and Out-of- Class Communication (r=-.35, p=.01). Supportive Classroom Climate & Argumentativeness (r=.42, p=.01); Supportiveness & Out of Class Communication (r=.57, 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)Defensive Classroom Climate & Verbal Aggressiveness (r=.55, p=.01)

17. Assessment Conclusions Students in ALL experimental groups showed gains; gains significantly higher than control group gainsStudents in ALL experimental groups showed gains; gains significantly higher than control group gains All instructional models improve students’ ethical knowledge, sensitivity, and reasoningAll 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 measuresStudy-specific measures of knowledge of standards and ethical sensitivity tap changes and correlate with existing measures ESIT more appropriate for this population than MJTESIT more appropriate for this population than MJT Study-specific measure of moral reasoning might not be robust; ESIT seems more effective to measure changesStudy-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 climateInstructor-student communication related to student ethical sensitivity and to student perceptions of classroom climate

18. 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

19. Acknowledgements National Science FoundationNational Science Foundation Biological Design Ph.D. ProgramBiological Design Ph.D. Program Center for Biology and SocietyCenter for Biology and Society Center for Nanotechnology and SocietyCenter for Nanotechnology and Society Consortium for Science, Policy & OutcomesConsortium for Science, Policy & Outcomes Lincoln Center for Applied EthicsLincoln Center for Applied Ethics