SlideShare a Scribd company logo
DATA ANALYSIS TASKS

EDUC W200 Week 9
WHAT ARE DATA ANALYSIS TASKS?
• It’s all about organizing and looking for patterns in
data.
o Provide students with raw materials
• facts, numbers, words, etc

o Asks them to draw conclusions on their own (based on the
data)
o Not limited to numbers – it’s about patterns of all types

EDUC W200 Week 9
EXAMPLES:
• Kids Science Projects
• Statistical Analysis to Rank Baseball Player
(Grades 11-12, Math, Statistics)

EDUC W200 Week 9
MORE EXAMPLES OF ANALYZING DATA
•
•
•

•
•
•
•

Taking measurements of three different plants over the course of a month
(science)
Looking up the parties of presidential election winners and recording them for each
election year (social studies)
Looking at multiple poems and finding the common meters, themes, structures
(ELA)
Measuring angles and using these measurements to create “rules” for geometry
(math)
Using surveys to compare favorite style of music, favorite instrument, favorite
concept studied in Music Appreciation class across grade levels (Fine Arts)
Children use the balance scale to compare a variety of small objects and determine
which item is heavier and record it on the sheet (early childhood)
Investigating the number of football related deaths in high schools across the
country for the past 30 years to detect trends (Health/Physical Education)

EDUC W200 Week 9
WHY BOTHER WITH DATA ANALYSIS TASKS?
• Analyzing your real data makes learning more
meaningful
• Provides a visual way to view information (Ex: Wordle
or InspireData)
• It also teaches problem solving

EDUC W200 Week 9
TOOLS FOR DATA ANALYSIS TASKS

EDUC W200 Week 9
TOOLS FOR DATA ANALYSIS
• Numbers, Tables and
Graphs
o
o
o
o

InspireData
Excel
Google Spreadsheets
Tinkerplots (4th – 8th
grade)
o Fathom

EDUC W200 Week 9

• Visualizing verbal data
o
o
o
o

Wordle
Tagxedo
Excel/Spreadsheets
Visualization Tools
(Mashable)
TINKERPLOTS (VIDEO)

EDUC W200 Week 9
WORDLE EXAMPLES

EDUC W200 Week 9
EDUC W200 Week 9
EDUC W200 Week 9
How can a tool like this be used in the classroom?
EDUC W200 Week 9

More Related Content

Viewers also liked

Data Collection Tools Session 1 (Participant)
Data Collection Tools Session 1 (Participant)Data Collection Tools Session 1 (Participant)
Data Collection Tools Session 1 (Participant)
Nudibranch04
 
Types of data collection tools
Types of data collection tools Types of data collection tools
Types of data collection tools
Vipin Kumar
 
Research methodology TOOLS FOR COLLECTION OF DATA
Research methodologyTOOLS FOR COLLECTION OF DATAResearch methodologyTOOLS FOR COLLECTION OF DATA
Research methodology TOOLS FOR COLLECTION OF DATA
Suvin Lal
 
Research data collection methods and tools
Research data collection methods and toolsResearch data collection methods and tools
Research data collection methods and tools
Likhila Abraham
 
Data collection presentation
Data collection presentationData collection presentation
Data collection presentation
Kanchan Agarwal
 
Tools of data collection
Tools of data collectionTools of data collection
Tools of data collection
Dr.Suresh Isave
 
Chapter 9-METHODS OF DATA COLLECTION
Chapter 9-METHODS OF DATA COLLECTIONChapter 9-METHODS OF DATA COLLECTION
Chapter 9-METHODS OF DATA COLLECTION
Ludy Mae Nalzaro,BSM,BSN,MN
 

Viewers also liked (7)

Data Collection Tools Session 1 (Participant)
Data Collection Tools Session 1 (Participant)Data Collection Tools Session 1 (Participant)
Data Collection Tools Session 1 (Participant)
 
Types of data collection tools
Types of data collection tools Types of data collection tools
Types of data collection tools
 
Research methodology TOOLS FOR COLLECTION OF DATA
Research methodologyTOOLS FOR COLLECTION OF DATAResearch methodologyTOOLS FOR COLLECTION OF DATA
Research methodology TOOLS FOR COLLECTION OF DATA
 
Research data collection methods and tools
Research data collection methods and toolsResearch data collection methods and tools
Research data collection methods and tools
 
Data collection presentation
Data collection presentationData collection presentation
Data collection presentation
 
Tools of data collection
Tools of data collectionTools of data collection
Tools of data collection
 
Chapter 9-METHODS OF DATA COLLECTION
Chapter 9-METHODS OF DATA COLLECTIONChapter 9-METHODS OF DATA COLLECTION
Chapter 9-METHODS OF DATA COLLECTION
 

Similar to Data analysis

Data analysis
Data analysisData analysis
Data analysis
educw200
 
Sp2013 9 2
Sp2013 9 2Sp2013 9 2
Sp2013 9 2
educw200
 
Fa12 w200-w9-part 3
Fa12 w200-w9-part 3Fa12 w200-w9-part 3
Fa12 w200-w9-part 3
educw200
 
Fa12 w200-w9-part 3
Fa12 w200-w9-part 3Fa12 w200-w9-part 3
Fa12 w200-w9-part 3
educw200
 
Data collection
Data collectionData collection
Data collection
educw200
 
week9 sp2012 updated Lecture part1 definitions
week9 sp2012 updated Lecture part1 definitionsweek9 sp2012 updated Lecture part1 definitions
week9 sp2012 updated Lecture part1 definitions
educw200
 
Week9 sp2012_Lecture part1 defintions
Week9 sp2012_Lecture part1 defintionsWeek9 sp2012_Lecture part1 defintions
Week9 sp2012_Lecture part1 defintions
educw200
 
Lecture part1 definitions
Lecture part1 definitionsLecture part1 definitions
Lecture part1 definitions
educw200
 
Marc-Bonne-iSchool
Marc-Bonne-iSchoolMarc-Bonne-iSchool
Marc-Bonne-iSchool
Marc Bonne
 
Whwg
WhwgWhwg
Whwg
swiss1234
 
TeachingWithData.org ASA Presentation 2010
TeachingWithData.org ASA Presentation 2010TeachingWithData.org ASA Presentation 2010
TeachingWithData.org ASA Presentation 2010
ICPSR
 
Data collection and analysis
Data collection and analysisData collection and analysis
Data collection and analysis
educw200
 
STEM Mom Speaks to Teachers at Princeton University
STEM Mom Speaks to Teachers at Princeton University STEM Mom Speaks to Teachers at Princeton University
STEM Mom Speaks to Teachers at Princeton University
Darci the STEM Mom
 
Asa integrating data 2 19-2014 with cites
Asa integrating data 2 19-2014 with citesAsa integrating data 2 19-2014 with cites
Asa integrating data 2 19-2014 with cites
ICPSR
 
Datadriven
DatadrivenDatadriven
Datadriven
Patricia Hutton
 
Lesson plans holiday learning fest mathematics
Lesson plans holiday learning fest mathematicsLesson plans holiday learning fest mathematics
Lesson plans holiday learning fest mathematics
MyraAkpabio
 
Action research workshop
Action research workshopAction research workshop
Action research workshop
JOHNLOULUCENARIO1
 
Internationalizing STEM by Moss
Internationalizing STEM by MossInternationalizing STEM by Moss
Internationalizing STEM by Moss
Center for Global Education at Asia Society
 
R&D for the High School Classroom -Week 2 early
R&D for the High School Classroom -Week 2 earlyR&D for the High School Classroom -Week 2 early
R&D for the High School Classroom -Week 2 early
Darci the STEM Mom
 
STEM beliefs and self-efficacy 2015 (provo peaks)
STEM beliefs and self-efficacy 2015 (provo peaks)STEM beliefs and self-efficacy 2015 (provo peaks)
STEM beliefs and self-efficacy 2015 (provo peaks)
Peter Rich
 

Similar to Data analysis (20)

Data analysis
Data analysisData analysis
Data analysis
 
Sp2013 9 2
Sp2013 9 2Sp2013 9 2
Sp2013 9 2
 
Fa12 w200-w9-part 3
Fa12 w200-w9-part 3Fa12 w200-w9-part 3
Fa12 w200-w9-part 3
 
Fa12 w200-w9-part 3
Fa12 w200-w9-part 3Fa12 w200-w9-part 3
Fa12 w200-w9-part 3
 
Data collection
Data collectionData collection
Data collection
 
week9 sp2012 updated Lecture part1 definitions
week9 sp2012 updated Lecture part1 definitionsweek9 sp2012 updated Lecture part1 definitions
week9 sp2012 updated Lecture part1 definitions
 
Week9 sp2012_Lecture part1 defintions
Week9 sp2012_Lecture part1 defintionsWeek9 sp2012_Lecture part1 defintions
Week9 sp2012_Lecture part1 defintions
 
Lecture part1 definitions
Lecture part1 definitionsLecture part1 definitions
Lecture part1 definitions
 
Marc-Bonne-iSchool
Marc-Bonne-iSchoolMarc-Bonne-iSchool
Marc-Bonne-iSchool
 
Whwg
WhwgWhwg
Whwg
 
TeachingWithData.org ASA Presentation 2010
TeachingWithData.org ASA Presentation 2010TeachingWithData.org ASA Presentation 2010
TeachingWithData.org ASA Presentation 2010
 
Data collection and analysis
Data collection and analysisData collection and analysis
Data collection and analysis
 
STEM Mom Speaks to Teachers at Princeton University
STEM Mom Speaks to Teachers at Princeton University STEM Mom Speaks to Teachers at Princeton University
STEM Mom Speaks to Teachers at Princeton University
 
Asa integrating data 2 19-2014 with cites
Asa integrating data 2 19-2014 with citesAsa integrating data 2 19-2014 with cites
Asa integrating data 2 19-2014 with cites
 
Datadriven
DatadrivenDatadriven
Datadriven
 
Lesson plans holiday learning fest mathematics
Lesson plans holiday learning fest mathematicsLesson plans holiday learning fest mathematics
Lesson plans holiday learning fest mathematics
 
Action research workshop
Action research workshopAction research workshop
Action research workshop
 
Internationalizing STEM by Moss
Internationalizing STEM by MossInternationalizing STEM by Moss
Internationalizing STEM by Moss
 
R&D for the High School Classroom -Week 2 early
R&D for the High School Classroom -Week 2 earlyR&D for the High School Classroom -Week 2 early
R&D for the High School Classroom -Week 2 early
 
STEM beliefs and self-efficacy 2015 (provo peaks)
STEM beliefs and self-efficacy 2015 (provo peaks)STEM beliefs and self-efficacy 2015 (provo peaks)
STEM beliefs and self-efficacy 2015 (provo peaks)
 

More from educw200

8. tip 1 week 1
8. tip 1 week 18. tip 1 week 1
8. tip 1 week 1
educw200
 
5. course overview and purpose week 1
5. course overview and purpose week 15. course overview and purpose week 1
5. course overview and purpose week 1
educw200
 
10. what's due week 1
10. what's due week 110. what's due week 1
10. what's due week 1
educw200
 
9. digital story week 1
9. digital story week 19. digital story week 1
9. digital story week 1
educw200
 
7. create accounts week 1
7. create accounts week 17. create accounts week 1
7. create accounts week 1
educw200
 
6. shift week 1
6. shift week 16. shift week 1
6. shift week 1
educw200
 
3. course expectations week 1
3. course expectations week 13. course expectations week 1
3. course expectations week 1
educw200
 
2. today's agenda week 1
2. today's agenda week 12. today's agenda week 1
2. today's agenda week 1
educw200
 
4. ttl week 1
4. ttl week 14. ttl week 1
4. ttl week 1
educw200
 
Shift 3
Shift 3Shift 3
Shift 3
educw200
 
9. digital story week 1
9. digital story week 19. digital story week 1
9. digital story week 1
educw200
 
10. what's due week 1
10. what's due week 110. what's due week 1
10. what's due week 1
educw200
 
7. create accounts week 1
7. create accounts week 17. create accounts week 1
7. create accounts week 1
educw200
 
8. tip 1 week 1
8. tip 1 week 18. tip 1 week 1
8. tip 1 week 1
educw200
 
4. ttl week 1
4. ttl week 14. ttl week 1
4. ttl week 1
educw200
 
2. today's agenda week 1
2. today's agenda week 12. today's agenda week 1
2. today's agenda week 1
educw200
 
3. course expectations week 1
3. course expectations week 13. course expectations week 1
3. course expectations week 1
educw200
 
5. course overview and purpose week 1
5. course overview and purpose week 15. course overview and purpose week 1
5. course overview and purpose week 1
educw200
 
6. shift week 1
6. shift week 16. shift week 1
6. shift week 1
educw200
 
8. tip#15
8. tip#158. tip#15
8. tip#15
educw200
 

More from educw200 (20)

8. tip 1 week 1
8. tip 1 week 18. tip 1 week 1
8. tip 1 week 1
 
5. course overview and purpose week 1
5. course overview and purpose week 15. course overview and purpose week 1
5. course overview and purpose week 1
 
10. what's due week 1
10. what's due week 110. what's due week 1
10. what's due week 1
 
9. digital story week 1
9. digital story week 19. digital story week 1
9. digital story week 1
 
7. create accounts week 1
7. create accounts week 17. create accounts week 1
7. create accounts week 1
 
6. shift week 1
6. shift week 16. shift week 1
6. shift week 1
 
3. course expectations week 1
3. course expectations week 13. course expectations week 1
3. course expectations week 1
 
2. today's agenda week 1
2. today's agenda week 12. today's agenda week 1
2. today's agenda week 1
 
4. ttl week 1
4. ttl week 14. ttl week 1
4. ttl week 1
 
Shift 3
Shift 3Shift 3
Shift 3
 
9. digital story week 1
9. digital story week 19. digital story week 1
9. digital story week 1
 
10. what's due week 1
10. what's due week 110. what's due week 1
10. what's due week 1
 
7. create accounts week 1
7. create accounts week 17. create accounts week 1
7. create accounts week 1
 
8. tip 1 week 1
8. tip 1 week 18. tip 1 week 1
8. tip 1 week 1
 
4. ttl week 1
4. ttl week 14. ttl week 1
4. ttl week 1
 
2. today's agenda week 1
2. today's agenda week 12. today's agenda week 1
2. today's agenda week 1
 
3. course expectations week 1
3. course expectations week 13. course expectations week 1
3. course expectations week 1
 
5. course overview and purpose week 1
5. course overview and purpose week 15. course overview and purpose week 1
5. course overview and purpose week 1
 
6. shift week 1
6. shift week 16. shift week 1
6. shift week 1
 
8. tip#15
8. tip#158. tip#15
8. tip#15
 

Recently uploaded

Fueling AI with Great Data with Airbyte Webinar
Fueling AI with Great Data with Airbyte WebinarFueling AI with Great Data with Airbyte Webinar
Fueling AI with Great Data with Airbyte Webinar
Zilliz
 
How to Interpret Trends in the Kalyan Rajdhani Mix Chart.pdf
How to Interpret Trends in the Kalyan Rajdhani Mix Chart.pdfHow to Interpret Trends in the Kalyan Rajdhani Mix Chart.pdf
How to Interpret Trends in the Kalyan Rajdhani Mix Chart.pdf
Chart Kalyan
 
GNSS spoofing via SDR (Criptored Talks 2024)
GNSS spoofing via SDR (Criptored Talks 2024)GNSS spoofing via SDR (Criptored Talks 2024)
GNSS spoofing via SDR (Criptored Talks 2024)
Javier Junquera
 
5th LF Energy Power Grid Model Meet-up Slides
5th LF Energy Power Grid Model Meet-up Slides5th LF Energy Power Grid Model Meet-up Slides
5th LF Energy Power Grid Model Meet-up Slides
DanBrown980551
 
Y-Combinator seed pitch deck template PP
Y-Combinator seed pitch deck template PPY-Combinator seed pitch deck template PP
Y-Combinator seed pitch deck template PP
c5vrf27qcz
 
What is an RPA CoE? Session 1 – CoE Vision
What is an RPA CoE?  Session 1 – CoE VisionWhat is an RPA CoE?  Session 1 – CoE Vision
What is an RPA CoE? Session 1 – CoE Vision
DianaGray10
 
Mutation Testing for Task-Oriented Chatbots
Mutation Testing for Task-Oriented ChatbotsMutation Testing for Task-Oriented Chatbots
Mutation Testing for Task-Oriented Chatbots
Pablo Gómez Abajo
 
Apps Break Data
Apps Break DataApps Break Data
Apps Break Data
Ivo Velitchkov
 
Christine's Product Research Presentation.pptx
Christine's Product Research Presentation.pptxChristine's Product Research Presentation.pptx
Christine's Product Research Presentation.pptx
christinelarrosa
 
“Temporal Event Neural Networks: A More Efficient Alternative to the Transfor...
“Temporal Event Neural Networks: A More Efficient Alternative to the Transfor...“Temporal Event Neural Networks: A More Efficient Alternative to the Transfor...
“Temporal Event Neural Networks: A More Efficient Alternative to the Transfor...
Edge AI and Vision Alliance
 
A Deep Dive into ScyllaDB's Architecture
A Deep Dive into ScyllaDB's ArchitectureA Deep Dive into ScyllaDB's Architecture
A Deep Dive into ScyllaDB's Architecture
ScyllaDB
 
"Choosing proper type of scaling", Olena Syrota
"Choosing proper type of scaling", Olena Syrota"Choosing proper type of scaling", Olena Syrota
"Choosing proper type of scaling", Olena Syrota
Fwdays
 
JavaLand 2024: Application Development Green Masterplan
JavaLand 2024: Application Development Green MasterplanJavaLand 2024: Application Development Green Masterplan
JavaLand 2024: Application Development Green Masterplan
Miro Wengner
 
Leveraging the Graph for Clinical Trials and Standards
Leveraging the Graph for Clinical Trials and StandardsLeveraging the Graph for Clinical Trials and Standards
Leveraging the Graph for Clinical Trials and Standards
Neo4j
 
Skybuffer SAM4U tool for SAP license adoption
Skybuffer SAM4U tool for SAP license adoptionSkybuffer SAM4U tool for SAP license adoption
Skybuffer SAM4U tool for SAP license adoption
Tatiana Kojar
 
Your One-Stop Shop for Python Success: Top 10 US Python Development Providers
Your One-Stop Shop for Python Success: Top 10 US Python Development ProvidersYour One-Stop Shop for Python Success: Top 10 US Python Development Providers
Your One-Stop Shop for Python Success: Top 10 US Python Development Providers
akankshawande
 
Freshworks Rethinks NoSQL for Rapid Scaling & Cost-Efficiency
Freshworks Rethinks NoSQL for Rapid Scaling & Cost-EfficiencyFreshworks Rethinks NoSQL for Rapid Scaling & Cost-Efficiency
Freshworks Rethinks NoSQL for Rapid Scaling & Cost-Efficiency
ScyllaDB
 
Columbus Data & Analytics Wednesdays - June 2024
Columbus Data & Analytics Wednesdays - June 2024Columbus Data & Analytics Wednesdays - June 2024
Columbus Data & Analytics Wednesdays - June 2024
Jason Packer
 
zkStudyClub - LatticeFold: A Lattice-based Folding Scheme and its Application...
zkStudyClub - LatticeFold: A Lattice-based Folding Scheme and its Application...zkStudyClub - LatticeFold: A Lattice-based Folding Scheme and its Application...
zkStudyClub - LatticeFold: A Lattice-based Folding Scheme and its Application...
Alex Pruden
 
[OReilly Superstream] Occupy the Space: A grassroots guide to engineering (an...
[OReilly Superstream] Occupy the Space: A grassroots guide to engineering (an...[OReilly Superstream] Occupy the Space: A grassroots guide to engineering (an...
[OReilly Superstream] Occupy the Space: A grassroots guide to engineering (an...
Jason Yip
 

Recently uploaded (20)

Fueling AI with Great Data with Airbyte Webinar
Fueling AI with Great Data with Airbyte WebinarFueling AI with Great Data with Airbyte Webinar
Fueling AI with Great Data with Airbyte Webinar
 
How to Interpret Trends in the Kalyan Rajdhani Mix Chart.pdf
How to Interpret Trends in the Kalyan Rajdhani Mix Chart.pdfHow to Interpret Trends in the Kalyan Rajdhani Mix Chart.pdf
How to Interpret Trends in the Kalyan Rajdhani Mix Chart.pdf
 
GNSS spoofing via SDR (Criptored Talks 2024)
GNSS spoofing via SDR (Criptored Talks 2024)GNSS spoofing via SDR (Criptored Talks 2024)
GNSS spoofing via SDR (Criptored Talks 2024)
 
5th LF Energy Power Grid Model Meet-up Slides
5th LF Energy Power Grid Model Meet-up Slides5th LF Energy Power Grid Model Meet-up Slides
5th LF Energy Power Grid Model Meet-up Slides
 
Y-Combinator seed pitch deck template PP
Y-Combinator seed pitch deck template PPY-Combinator seed pitch deck template PP
Y-Combinator seed pitch deck template PP
 
What is an RPA CoE? Session 1 – CoE Vision
What is an RPA CoE?  Session 1 – CoE VisionWhat is an RPA CoE?  Session 1 – CoE Vision
What is an RPA CoE? Session 1 – CoE Vision
 
Mutation Testing for Task-Oriented Chatbots
Mutation Testing for Task-Oriented ChatbotsMutation Testing for Task-Oriented Chatbots
Mutation Testing for Task-Oriented Chatbots
 
Apps Break Data
Apps Break DataApps Break Data
Apps Break Data
 
Christine's Product Research Presentation.pptx
Christine's Product Research Presentation.pptxChristine's Product Research Presentation.pptx
Christine's Product Research Presentation.pptx
 
“Temporal Event Neural Networks: A More Efficient Alternative to the Transfor...
“Temporal Event Neural Networks: A More Efficient Alternative to the Transfor...“Temporal Event Neural Networks: A More Efficient Alternative to the Transfor...
“Temporal Event Neural Networks: A More Efficient Alternative to the Transfor...
 
A Deep Dive into ScyllaDB's Architecture
A Deep Dive into ScyllaDB's ArchitectureA Deep Dive into ScyllaDB's Architecture
A Deep Dive into ScyllaDB's Architecture
 
"Choosing proper type of scaling", Olena Syrota
"Choosing proper type of scaling", Olena Syrota"Choosing proper type of scaling", Olena Syrota
"Choosing proper type of scaling", Olena Syrota
 
JavaLand 2024: Application Development Green Masterplan
JavaLand 2024: Application Development Green MasterplanJavaLand 2024: Application Development Green Masterplan
JavaLand 2024: Application Development Green Masterplan
 
Leveraging the Graph for Clinical Trials and Standards
Leveraging the Graph for Clinical Trials and StandardsLeveraging the Graph for Clinical Trials and Standards
Leveraging the Graph for Clinical Trials and Standards
 
Skybuffer SAM4U tool for SAP license adoption
Skybuffer SAM4U tool for SAP license adoptionSkybuffer SAM4U tool for SAP license adoption
Skybuffer SAM4U tool for SAP license adoption
 
Your One-Stop Shop for Python Success: Top 10 US Python Development Providers
Your One-Stop Shop for Python Success: Top 10 US Python Development ProvidersYour One-Stop Shop for Python Success: Top 10 US Python Development Providers
Your One-Stop Shop for Python Success: Top 10 US Python Development Providers
 
Freshworks Rethinks NoSQL for Rapid Scaling & Cost-Efficiency
Freshworks Rethinks NoSQL for Rapid Scaling & Cost-EfficiencyFreshworks Rethinks NoSQL for Rapid Scaling & Cost-Efficiency
Freshworks Rethinks NoSQL for Rapid Scaling & Cost-Efficiency
 
Columbus Data & Analytics Wednesdays - June 2024
Columbus Data & Analytics Wednesdays - June 2024Columbus Data & Analytics Wednesdays - June 2024
Columbus Data & Analytics Wednesdays - June 2024
 
zkStudyClub - LatticeFold: A Lattice-based Folding Scheme and its Application...
zkStudyClub - LatticeFold: A Lattice-based Folding Scheme and its Application...zkStudyClub - LatticeFold: A Lattice-based Folding Scheme and its Application...
zkStudyClub - LatticeFold: A Lattice-based Folding Scheme and its Application...
 
[OReilly Superstream] Occupy the Space: A grassroots guide to engineering (an...
[OReilly Superstream] Occupy the Space: A grassroots guide to engineering (an...[OReilly Superstream] Occupy the Space: A grassroots guide to engineering (an...
[OReilly Superstream] Occupy the Space: A grassroots guide to engineering (an...
 

Data analysis

  • 2. WHAT ARE DATA ANALYSIS TASKS? • It’s all about organizing and looking for patterns in data. o Provide students with raw materials • facts, numbers, words, etc o Asks them to draw conclusions on their own (based on the data) o Not limited to numbers – it’s about patterns of all types EDUC W200 Week 9
  • 3. EXAMPLES: • Kids Science Projects • Statistical Analysis to Rank Baseball Player (Grades 11-12, Math, Statistics) EDUC W200 Week 9
  • 4. MORE EXAMPLES OF ANALYZING DATA • • • • • • • Taking measurements of three different plants over the course of a month (science) Looking up the parties of presidential election winners and recording them for each election year (social studies) Looking at multiple poems and finding the common meters, themes, structures (ELA) Measuring angles and using these measurements to create “rules” for geometry (math) Using surveys to compare favorite style of music, favorite instrument, favorite concept studied in Music Appreciation class across grade levels (Fine Arts) Children use the balance scale to compare a variety of small objects and determine which item is heavier and record it on the sheet (early childhood) Investigating the number of football related deaths in high schools across the country for the past 30 years to detect trends (Health/Physical Education) EDUC W200 Week 9
  • 5. WHY BOTHER WITH DATA ANALYSIS TASKS? • Analyzing your real data makes learning more meaningful • Provides a visual way to view information (Ex: Wordle or InspireData) • It also teaches problem solving EDUC W200 Week 9
  • 6. TOOLS FOR DATA ANALYSIS TASKS EDUC W200 Week 9
  • 7. TOOLS FOR DATA ANALYSIS • Numbers, Tables and Graphs o o o o InspireData Excel Google Spreadsheets Tinkerplots (4th – 8th grade) o Fathom EDUC W200 Week 9 • Visualizing verbal data o o o o Wordle Tagxedo Excel/Spreadsheets Visualization Tools (Mashable)
  • 12. How can a tool like this be used in the classroom? EDUC W200 Week 9

Editor's Notes

  1. Data Analysis TasksLooking for patterns or trendsClassifying, sequencing, categorizingDoesn’t have to be complicated to be effective!Can be done with collected data or database dataIt’s all about thinking about, moving around, organizing and looking for patterns in data.
  2. For the first example, it contains different science project ideas for teachers to design kids science projects. For example, the project: Why do leaves change color in the fall? Students can use chromatography to separate the colors in a leaf, observe how light affects color development and so on..The video(0:00~3:15): Student exercise for Statistical Analysis to Rank Baseball Players
  3. Based on your students’ subject area, pick up some examples to talk.Examples of analyzing dataDistributing survey of height, weight, and gender (math/statistics)Plotting various combinations of the data for statistical analysis or “eyeball testing”Taking measurements of three different plants over the course of a month (science)Plotting the growth and comparingLooking up the parties of presidential election winners and recording them for each election year (social studies)Creating a timeline of the winners and major historical events and looking for patterns
  4. Letting students analyze their own data instead of “just telling them the rule/pattern” adds authenticityIt gives students a chance to look at unifying themes (Ex: Wordle) or visual representations of many details (InspireData)It also teaches problem solving - they won’t always have you to tell them the rule or pattern! If the student can figure out the
  5. Mashable: http://mashable.com/2007/05/15/16-awesome-data-visualization-tools/
  6. These tables are a good example of what the students will be looking at on their computers during a given lesson. The simplicity of the program aloows for creation of such tables in mere seconds given a sufficient list of data. Within a few clicks from our toolbar, students can easily relate different data sets to explore and discover new information. After gathering results, the student can easily manipulate each table using the Separate or Stack tools. Students can even place new data sets into the same table to seek different results and explore new relations. Or if the data sets given don't fit the criteria to answer a question given, students can use formula tables to creat new assets to dive deeper into the relations given. Tinkerplots is a simple yet creative program that engages students in efficient data organization, and could be a very useful technology in today's classroom. Teachers shoud take note of the simplistic nature of the program, and try to develope challenging questions along with the data and activity they give to the class. Make sure to challenge the student to use all his tools to critically analyze the material given. Problem solving skills can be highly developed with such an intuitive program as Tinkerplots.
  7. US Constitution.
  8. MacBeth. Shakespeare./ Also talk about wordle is good for foreign language learning
  9. This Wordle was created from the NETS-T standard. The larger words are the words that are repeated most often.