Chapter 2: Thinking Like a Scientist Foundations Ms. Johnson.
Thinking like a scientist: Collegiate Science data analysis process skills
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Transcript of Thinking like a scientist: Collegiate Science data analysis process skills
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
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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