1 / 13

Heather E. Canary, University of Utah Joseph R. Herkert, Arizona State University

Microethics & Macroethics in Graduate Education for Scientists & Engineers: Developing & Assessing Instructional Models. Heather E. Canary, University of Utah Joseph R. Herkert, Arizona State University Karin Ellison, Arizona State University Jameson M. Wetmore, Arizona State University.

zorion
Download Presentation

Heather E. Canary, University of Utah Joseph R. Herkert, Arizona State University

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript


  1. Microethics & Macroethics in Graduate Education for Scientists & Engineers: Developing & Assessing Instructional Models Heather E. Canary, University of Utah Joseph R. Herkert, Arizona State University Karin Ellison, Arizona State University Jameson M. Wetmore, Arizona State University

  2. Acknowledgements • National Science Foundation: • NSF/EESE #0832944 • ASU Project Team: • Joseph Herkert, PI • Heather Canary, Co-PI (U of Utah) • Karin Ellison, Co-PI • Jameson Wetmore, Co-PI • JoAnn Williams • Ira Bennett • Brad Allenby • Jonathan Posner • Joan McGregor • Dave Guston • 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

  3. Project Overview • Meet the increasing need to integrate instruction of microethical issues with instruction of macroethical issues: • “Microethics” = moral dilemmas & issues confronting individual researchers or practitioners • “Macroethics” = moral dilemmas & issues collectively confronting the scientific enterprise or engineering profession • 5 Project Goals: • Formulate educational outcomes for the integration of micro- and macroethicsin graduate science and engineering education • Develop and pilot different models for teaching micro- and macroethics to graduate students in science and engineering • Assess the comparative effectiveness of the instructional models • Facilitate adoption of the instructional models and assessment methods at other academic institutions • Provide for widespread dissemination of course materials and assessment results in the engineering, science, and ethics education communities.

  4. Instructional Models • Stand-alone course (Science Policy for Scientists and Engineers-1 credit) • Technical course with embedded ethics content (Fundamentals of Biological Design) • Online/Classroom hybrid (Introduction to RCR in the Life Sciences – 1 credit) • Lab group engagement

  5. Participants • Fall 2009 - Spring 2011 (Total N = 81) • Embedded Model (N = 21) • Stand-Alone Model (N = 14) • Hybrid Model (N = 20) • Lab Model (N = 2; excluded from analysis) • Control Group (N = 26) • Student Status: • Undergraduates 5 • Transitional 5 • Masters 20 • PhD 50 • Mean Age = 24.23 • Males = 55; Females = 26

  6. Participants (cont’d.) • Academic Program: • Biodesign 21 • Engineering 30 • Chem/BioChem 9 • Biology 12 • Other 5 • Missing 4 • Previous Ethics Instruction: Yes = 36 • Previous S. R. Instruction: Yes = 22 • First Language: • English 54 • Chinese 10 • Indian Language 8 • Spanish 2 • Korean 2 • Other 5 • Ethnicity/Race: • White 41 • Asian 28 • Hispanic 6 • African American 3 • Other 3

  7. Procedures • Nonequivalent Control-Group Quasi-Experiment • Survey measures of 3 desired learning outcomes: • Increased knowledge of relevant standards • Increased ethical sensitivity • Improved ethical reasoning • Engineering & Sciences Issues Test (ESIT) – short • Study-Specific Measures: • Knowledge of Relevant Standards (T/F/don’t know) • Ethical Sensitivity (1-5 scale) • Student-Instructor Interaction: • Out-of-classroom communication • Classroom climate (supportive/defensive) • Instructor verbal aggressiveness • Instructor verbal assertiveness • Frequency of informal ethics conversations

  8. N2 Scores by Study Group Group 1 = Embedded; Group 2 = Stand-Alone; Group 3 = Hybrid; Group 5 = Control

  9. Outcomes by Study Group Measure Embedded Stand-Alone Hybrid Control Mean Mean Mean Mean ____________________________________________________ Pretest N2-Score 8.11 7.62 8.39 6.64 Posttest N2-Score 8.70* 8.76* 10.14* 5.18 Pretest Knowledge 11.57 11.43 12.55* 10.42 Posttest Knowledge 12.90* 12.36* 14.40* 10.62 Pretest Ethical 3.44* 3.28 3.36 3.21 Sensitivity Posttest Ethical 3.48* 3.51* 3.60* 3.21 Sensitivity ____________________________________________________ Note: * indicates significantly higher than Control Group at p < .05 level.

  10. Outcomes by Language Group Measure Native English Non-Native English Mean Mean N = 54 N = 27 ____________________________________________________ Pretest N2-Score* 8.53 5.82 Posttest N2-Score* 9.28 5.12 Pretest Knowledge* 11.83 10.59 Posttest Knowledge* 13.30 10.74 Pretest Ethical 3.40 3.16 Sensitivity* Posttest Ethical 3.61 3.08 Sensitivity* ____________________________________________________ Note: * indicates significant group differences at the p < .05 level.

  11. Outcomes by Sex Group Measure Male Female N = 55 N = 26 Mean Mean ______________________________________________ Pretest N2-Score 7.31 8.30 Posttest N2-Score* 7.06 9.72 Pretest Knowledge 11.18 11.92 Posttest Knowledge* 12.02 13.35 Pretest Ethical Sensitivity 3.32 3.31 Posttest Ethical Sensitivity 3.42 3.45 ______________________________________________ Note: * indicates significant difference at the p < .05 level.

  12. Student-Instructor Interaction • Classroom dynamics similar across instructional models: • 1 group difference in interaction variables – verbal aggressiveness higher in Embedded than in Hybrid • All other interaction variables statistically the same across instructional groups • Out-of-class communication associations: • With posttest ethical sensitivity (r = -.35, p < ,01) • With posttest ethics discussions with lab directors (r = .34, p < .05) • Frequency of ethics conversations increased: • Significantly with peers • Not significantly with lab directors/PIs

  13. Implications • All models were effective in increasing knowledge, sensitivity, and moral reasoning • Knowledge gains highest in Hybrid Group: Consistent with previous research showing combining instructional modes more effective than either mode on its own • Language differences point to caution when using survey instruments with non-native English speaking samples • Sex differences might be related to language differences • Out-of-classroom communication points to importance of informal conversations and spillover effect of mentoring relationships • Students benefitted from flexible, interdisciplinary team of dedicated educators. • Successful integrative ethics education depends on commitment & cooperation of academic departments.

More Related