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5Qs: An Activity to Support Students Develop Skills to Interpret Research Observations

Author(s): Stephanie Conant1, Tom D'Elia2, Arturo Diaz3, Denise Monti4, Richard Pollenz5, Vassie Ware6, Kathy Takayama7, Viknesh Sivanathan7

1. University of Detroit Mercy 2. Indian River State College 3. La Sierra University 4. University of Alabama at Birmingham 5. University of South Florida 6. Lehigh University 7. Howard Hughes Medical Institute

263 total view(s), 68 download(s)

Summary:

5Qs (five questions) is a simple pedagogy tool research mentors can use to help students think through their observations systematically, to draw supported conclusions, and to decide on next steps, all of which can be done independently by students.

Description

Ambiguity in experimental outcomes is inherent in discovery research. Such ambiguity can be a source of frustration and self-doubt for novice/beginning scientists who have not had much experience managing and interpreting research data. 5Qs (five questions) is a simple pedagogy tool research mentors can use to help students think through their observations systematically, to draw supported conclusions, and to decide on next steps, all of which can be done independently and prior to seeking input/advice from their mentor. The 5Qs thus offers students a strategy to handle ambiguity in research data, ideally reducing frustration and bolstering their development and confidence as scientists.

Learning objectives: 

After completing this module, students should be able to:

  • draw supported conclusions from their observations/data
  • plan experiments based on supported conclusions from previous data
     

How is the module structured to promote student development as a scientist? 
By providing students with the tools to review their data both systematically and critically prior to seeking advice from fellow students or their research mentor, this exercise touches on 3 components of the SEA-PHAGES instructional model of “becoming a scientist”, which include 1) encouraging independence, 2) facing ambiguity, and 3) modelling scientific thinking. In addition, by placing the onus to review the data on the students, especially in the context of course-based research with high student-to-instructor ratios, the time an instructor has to work closely with and mentor each student can be used more effectively (instructor mentorship).

Intended Teaching Setting

Course level:  for all students, particularly for those with little or no prior research experience
Instructional Setting:  in-person classroom/laboratory
Implementation Time Frame:  ~ 10 minutes; often, and as necessary

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