THINKING LIKE A SCIENTIST: COLLEGIATE SCIENCE DATA ANALYSIS PROCESS SKILLSColleen McLinn, Gigi Saunders, Rudi Thompson, Linda Vick

NORTH PARK UNIVERSITYNorth 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.

THE NEED 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 TASKCreate 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.

Employers Seeking

3 Million Unfilled Jobs

An Addendum

to:

Communication skillsAnalytical Research skillsComputer/technical literacyFlexibility/adaptability/managing multiple prioritiesInterpersonal abilitiesLeadership management skillsMulticulturally awarePlanning/organizingProblem solving/ reasoning/creativityTeamwork

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 techsTechnical Service RepScientific Company Sales RepGraduate SchoolProfessional School

Engaging Students in -

JOB

OUR PROCESSA. Identify the attributes desired by

employersB. Identify skills and sub-skills that build

these attributesC. Establish a customizable sequence for

building these skillsD. Identify experiences to present/practice

skills and skill setsE. Incorporate faculty buy-in

BACKWARD DESIGN

A. Identify attributes desired by employers

DESIRED ATTRIBUTES•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

FOCUS

National Association of Colleges and Employers (NACE)

STEP TWO

B. Identify skills and sub-skills that develop attributes

ANALYTICAL RESEARCH SKILLS 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 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 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 Assess a situation Seek multiple perspectives Gather more information if necessary Identify key issues that need to

be addressed Problem sets Brainstorming Implications Applications

PROBLEM SOLVING/REASONING/CREATIVITY

Problem solving Tests of correlation and/or causation Hypothesis formation Experimental design Thinking outside the box

COMPUTER/TECHNICAL LITERACY Spreadsheets Graphic analysis Report functions Locating and mining data

FLEXIBILITY/ADAPTABILITY Persistence

MANAGING MULTIPLE PRIORITIES Multitasking Leadership Prioritizing

PLANNING/ORGANIZING How to search How to test Teamwork

COMMUNICATION Organize and construct tables and

charts Lab report writing Presentation/Discussion Peer review

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

C. CUSTOMIZABLE SEQUENCE

Using Data:• Descriptive statistics• Comparison of two data sets

Evaluating Data:• Significance• Sample size

•Identifying and using data tools:

• Spreadsheet(s) – analysis

• Mathematical modeling

• Graphic analysis

Modules

•Visualizing Data:• Tables• Graphs: styles, formatting• Graphing skills

MODULES

•Finding Data:• Searching databases• Evaluating data• How to test

•Identifying and using data

tools:• Spreadsheet(s) – analysis

• Mathematical modeling

• Graphic analysis

D. IDENTIFY EXPERIENCES

Identifying data:

Using Data:•Comparison of Data Sets

Evaluating Data:

•Identifying and using data tools:

D. IDENTIFY EXPERIENCESExample Lessons

Visualizing data:

Finding Data:

•Identifying and using data tools:

COMPARISON OF DATA SETS

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

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

Pedagogical objectives Tools

• Database(s) BIRDD

• Excel Interactive Group Lesson(s) Inquiry-based Individual Challenge Assessment Rubric

Pedagogical objectives Tools Interactive Group Lesson

• Matrix Inquiry-based Individual Challenge Assessment Rubric

Analyzing Data Like a Scientist – Resources to develop skills 

 

Sample

BioQUEST

Modules Tools   Data concepts:subjective/objectivequantity, quality, reliability

Identifying and operationalizing variables

Descriptive Tools: Statistics and Phylogenetic description

Correlation and Causation

Comparison of Two Data Sets

Visual Representation of Data

Analysis of Graphical Representation of Data

Database Investigation

Creating models to explain data and make predictions to test hypotheses

  Gapminder       Introduc-ory concept

  XX      

  Arcview GIS           XX      BioQUEST Library Online: BIRDD: Beagle Investigations Return with Darwinian Data

  XX   XX   XX        

BioQUEST Library Online: Data Collection and Organization

Spreadsheets database, graphics and statistics packages

                 

Investigative Cases: As the Stomach Turns

  student-generated data

               

Investigative Cases: A Multidimensional Study of HIV

              Analysis of database

   

Esteem Collection: Two-Species Model

                  Introduction to models

Esteem Collection: Island Biogeography

              Analysis of student-generated data

   

Scale It: Cholera Next Door

  Diverse types of data, evaluate quality of data sets

              Model potential modes of disease transmission during an epidemic.

Scale it: Forest Fever

        XX       XX  

INTERACTIVE GROUP LESSON MATRIX

Problem Spaces: HIV

          DNA sequence comparison

       

Problem Spaces: Desiccation Tolerance

          DNA sequence comparison

XX     Modeling Spatial Distribution

Problem Spaces: Identifying biocontrol agents through applied systematics (Blunder Down Under)

      Phylogenetic tools

           

Pharmokinetics Models Lab

                  XX

2012 Association vs. Causation

        Investigation

         

Using Geo-referenced Animal Observations for Inquiry

  Diverse types of data, evaluate quality of data sets

Determination of variables from observations of bird song

          XX  

INTERACTIVE GROUP LESSON MATRIX

Pedagogical objectives Tools Interactive Group Lesson

• Matrix Inquiry-based Performance

Assessment• Doing Science

Assessment Rubric

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.

Inquiry-based Performance Assessment

  A B C D E F G H I J1 genovesa                  

2    body length wing length tail length beak height beak widthlower beak length

upper beak length nostril-upper tarsus length

3 mean 116.4 61.7 40.1 8.4 6.6 7.6 14.2 9.4 17.94 sd 4.4 2.2 1.7 0.3 0.3 0.5 0.8 0.5 0.85 n 9 9 9 9 9 9 9 9 96 se 1.47 0.73 0.57 0.10 0.10 0.17 0.27 0.17 0.277                    8                     

9  island x body length wing length tail length beak height beak widthlower beak length

upper beak length nostril-upper tarsus length

10 mean 117.6 62.2 39.3 8.1 6.5 6.3 12.4 8.4 18.811 sd 3 2 1.4 0.4 0.2 0.3 0.5 0.4 0.412 n 5 119 6 113 6 6 102 122 613 se 1.34 0.18 0.57 0.04 0.08 0.12 0.05 0.04 0.1614                    15                     

16  all islands body length wing length tail length beak height beak widthlower beak length

upper beak length nostril-upper tarsus length

17 mean 116.9 62 39.1 8.1 6.7 6.7 12.5 8.5 18.818 sd 5.5 2.3 3 0.5 0.3 0.5 0.7 0.5 0.819 n 180 1552 187 1355 188 186 1452 1561 18920 se 0.41 0.06 0.22 0.01 0.02 0.04 0.02 0.01 0.0621                    22                     23                     

24    body length wing length tail length beak height beak widthlower beak length

upper beak length nostril-upper tarsus length

25 genovesa                  26  plus2se 119.3 63.2 41.2 8.6 6.8 7.9 14.7 9.7 18.427 mean 116.4 61.7 40.1 8.4 6.6 7.6 14.2 9.4 17.928 minus2se 113.5 60.2 39.0 8.2 6.4 7.3 13.7 9.1 17.429                    30  1sland x                  31  plus2se 120.3 62.6 40.4 8.2 6.7 6.5 12.5 8.5 19.132 mean 117.6 62.2 39.3 8.1 6.5 6.3 12.4 8.4 18.833 minus2se 114.9 61.8 38.2 8.0 6.3 6.1 12.3 8.3 18.534                    35  all islands                  36  plus2se 117.7 62.1 39.5 8.1 6.7 6.8 12.5 8.5 18.937 mean 116.9 62 39.1 8.1 6.7 6.7 12.5 8.5 18.838 minus2se 116.1 61.9 38.7 8.1 6.7 6.6 12.5 8.5 18.7

Sheet1

STUDENT GENERATED DATA

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

FINCHES ASSESSMENT RUBRICCriteria• 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)

E. ENCOURAGE FACULTY BUY-IN Flexibility Independent modules Clear process-related objectives Ease of use Value for retention Value for assessment Value for student

placement

WHERE DO WE GO FROM HERE?1. Continue to locate/ develop

experiences that can be incorporated into the program

2. Develop an assessment strategy3. Test the elements of the program 4. Use science!5. Seek funding to support further

development of program