Thinking like a scientist: Collegiate Science data analysis process skills

1. Colleen McLinn, Gigi Saunders, Rudi Thompson, Linda Vick Thinking like a scientist: Collegiate Science data analysis process skills

2. North Park University serves a diverse student population. Our Biology major allows great freedom in the selection and sequencing of courses. We have a need for some means of establishing a coherent path for the development of fundamental skills that will provide a foundation for scientific engagement and thinking. North Park University

3. Prepare students to develop advanced skills Enhance student engagement through participatory experiences Provide opportunities for assessment Improve student retention Development of skills desired by employers The need

4. Create a sequenced program of experiences to introduce and/or reinforce basic knowledge and tools that will enable students to develop the skills that will equip them to participate effectively as scientists and prepare them for employment. The task

5. Engaging Students in - JOB An Addendum to: Employers Seeking Communication skills Analytical Research skills Computer/technical literacy Flexibility/adaptability/managing multiple priorities Interpersonal abilities Leadership management skills Multiculturally aware Planning/organizing Problem solving/ reasoning/creativity Teamwork 3 Million Unfilled Jobs Contract Research Organizations: product development, formulation and manufacturing, clinical trial management, safety, preclinical toxicology, clinical lab, data management, biostatistics, medical writing Clinical, Medical, micro, life sciences lab techs Technical Service Rep Scientific Company Sales Rep Graduate School Professional School

6. A.Identify the attributes desired by employers B. Identify skills and sub-skills that build these attributes C. Establish a customizable sequence for building these skills D. Identify experiences to present/practice skills and skill sets E. Incorporate faculty buy-in OUR Process

7. A. Identify attributes desired by employers Backward design

8. Communication skills (listening, verbal, written) • Analytical research skills • assess a situation • seek multiple perspectives • gather more information if necessary • identify key issues that need to be addressed • Computer/technical literacy • computer – literate performance with extensive software proficiency covering a wide variety of applications. • Flexibility/adaptability/managing multiple priorities • Planning/organizing • Problem solving/reasoning/creativity • Teamwork • Interpersonal abilities • Leadership management skills • Multicultural aware desired Attributes FOCUS National Association of Colleges and Employers (NACE)

9. B. Identify skills and sub-skills that develop attributes Step two

10. Assess a situation • What do I know/want/need • Descriptive statistics (central tendency, variability, etc.) • Comparison of two data sets • Identify variables: independent and dependent • Identify constraints or boundaries of a situation • Seek multiple perspectives • Gather more information if necessary • Identify key issues that need to be addressed Analytical research skills

11. Assess a situation • Seek multiple perspectives • Experimental/null/alternate hypothesis • Multiple data sets • Source evaluation • Gather more information if necessary • Identify key issues that need to be addressed Analytical research skills

12. Assess a situation • Seek multiple perspectives • Gather more information if necessary • Quantitative/qualitative data • Subjective/objective data • Discrete/continuous data • When is enough, enough? • What is the value of the info? • Identify key issues that need to be addressed Analytical research skills

13. Assess a situation • Seek multiple perspectives • Gather more information if necessary • Identify key issues that need to be addressed • Problem sets • Brainstorming • Implications • Applications Analytical research skills

14. Problem solving • Tests of correlation and/or causation • Hypothesis formation • Experimental design • Thinking outside the box Problem solving/reasoning/creativity

15. Spreadsheets • Graphic analysis • Report functions • Locating and mining data Computer/technical literacy

16. Persistence Flexibility/adaptability

17. Multitasking • Leadership • Prioritizing Managing multiple priorities

18. How to search • How to test • Teamwork Planning/organizing

19. Organize and construct tables and charts • Lab report writing • Presentation/Discussion • Peer review communication

20. C. Customizable Sequence Modules • Identifying data: • Assessment of situation [what do I know, what do I want to discover, what do I need to know] • Data: subjective/ objective; quantitative/ qualitative; precision, accuracy, reliability • Correlation and causation • Hypothesis formulation • Using Data: • Descriptive statistics • Comparison of two data sets • Identifying and using data tools: • Spreadsheet(s) – analysis • Mathematical modeling • Graphic analysis • Evaluating Data: • Significance • Sample size

21. modules • Visualizing Data: • Tables • Graphs: styles, formatting • Graphing skills • Finding Data: • Searching databases • Evaluating data • How to test • Identifying and using data tools: • Spreadsheet(s) – analysis • Mathematical modeling • Graphic analysis

22. D. Identify experiences • Identifying data: • Using Data: • Comparison of Data Sets • Identifying and using data tools: Evaluating Data:

23. Example Lessons D. Identify experiences • Visualizing data: • Identifying and using data tools: Finding Data:

24. Pedagogical objectives • Tools • Interactive Group Lesson • Inquiry-based Individual Challenge • Assessment Rubric Comparison of data sets

25. Pedagogical objectives • Utilize descriptive statistics to explain values in a sample population • compare two value sets to identify separation or overlap of the data sets • Tools • Interactive Group Lesson • Inquiry-based Individual Challenge • Assessment Rubric

26. Pedagogical objectives • Tools • Database(s) • BIRDD • Excel • Interactive Group Lesson(s) • Inquiry-based Individual Challenge • Assessment Rubric

27. Pedagogical objectives • Tools • Interactive Group Lesson • Matrix • Inquiry-based Individual Challenge • Assessment Rubric

28. Analyzing Data Like a Scientist – Resources to develop skills Sample BioQUEST INTERACTIVE GROUP LESSON MATRIX

29. INTERACTIVE GROUP LESSON MATRIX

30. Pedagogical objectives • Tools • Interactive Group Lesson • Matrix • Inquiry-based Performance Assessment • Doing Science • Assessment Rubric

31. Inquiry-based Performance Assessment Challenge:  Apply your skills in describing and comparing data sets by using them to compare morphometric data of finches from the Galapagos Islands. These islands and the finches that are endemic to the islands have provided a classic example of adaptive radiation.  The data that you will use has been collected from subpopulations of birds on several of the islands. Your task is to compare these subpopulations: are the subpopulations on individual islands distinctive?  1.      Go to the BIRDD site http://bioquest.org/bird/index.php Open Islands and habitats and note the general location and layout of the islands. 2.     Open Morphological Data.  Familiarize yourself with the morphometric measurements that have been collected.  Why might these measurements have been chosen? Scan the tables of data.  What information have you been given? 3.     Go to http://people.rit.edu/rhrsbi/galapagospages/Darwinfinch.html to see images of the 13 species of Galapagos finches.  Are all of these species included in this data set?  4.     Choose a species represented on two of the three islands that are listed separately [Genovesa, Santa Cruz, and Island X].  5.     Are the populations on either of the islands significantly different from each other in any of the measurements?  Are either of the populations significantly different from the “all islands” values? 6.     Construct an Excel spreadsheet to use in organizing and calculating your data.  You may also wish to construct  charts or graphs to visually present your data. 7.     Explain how you have compared the data sets, and how you have reached your conclusions.

32. Sheet1 STUDENT GENERATED DATA

33. Pedagogical objectives • Tools • Interactive Group Lesson • Inquiry-based Individual Challenge • Assessment Rubric

34. Finches Assessment rubric • Criteria • Select an appropriate dataset: identify a species found on at least two islands (1.1.5, 2.1, 3.4, 6.2) • Properly set up spreadsheet from data provided (1.14, 2.4, 3.1, 7.1) • Calculate standard error for each trait and population (1.1.2, 3.1) • Calculate mean +/- 2 standard errors for each trait and population (1.1.2, 3.1) • Compare the three populations for each of the nine morphometric traits (either numerically or with graphs) (1.1.3, 3.2, 3.3, 3.4, 7.1) • Identify where there is no overlap between mean +/- SE’s and recognize what that means (1.1.3, 3.2) • Between island populations • Between the island populations and species summary data • Write explanatory paragraph (how compared the datasets and reached conclusions) • Interpret the data or graphs, describe what the data told them, describe how they got their answer (3.2, 7.2) • Interpret what the observed patterns mean at an evolutionary/population level and hypothesize what might have caused those differences (1.3.5, 1.4.3, 2.3, 7.2) • Levels: Beginning (0-3) Developing (4-7) Proficient (8-10)

35. Flexibility Independent modules Clear process-related objectives Ease of use Value for retention Value for assessment Value for studentplacement E. Encourage faculty buy-in

36. Continue to locate/ develop experiences that can be incorporated into the program Develop an assessment strategy Test the elements of the program Use science! Seek funding to support further development of program Where do we go from here?