Learning about Big Data
among Secondary School Students
in a technology-supported
collaborative learning environment
Einat Gil
University of Toronto & Fields Institute for Research in Mathematical Sciences
Presented at: Closing Conference: Statistical and Computational Analytics for Big Data, June 13, 2015 Dalhousie University, Halifax
Context
• Post doc within Big Data program
• Background: Statistics education
Technologies in education and design of learning environments
• Collaboration
2
Prof. Nancy Reid
Prof. Alison Gibbs
Prof. Jim Slotta (& Encore Lab)
School principal,
and secondary School Math teacher
Presentation overview
• Why, how, vision
• Research perspectives & theoretical framework
• Unit design
• Research methods
• Initial findings
• Discussion
Why, how..
4
• Big data
• Why introduce big data to secondary school students?
• Will it be relevant /interesting for them?
• How to introduce it, what aspects?
Characteristics of big data
Value
Variability
Visualization
Big Data –
emergent concept and reality in the world
The
inspiration of statistical
investigation The role
technology can play (represent, analyse and communicate) Knowledge building Learning and teaching processes enabled by design of a learning environment Statistical
reasoning Curious students
V
is
io
Research perspectives
» Statistics education:
» Learning about big data
» Covariational reasoning in the context of big data
» Learning Sciences/Computer Supported Collaborative Learning (CSCL):
» Knowledge community and Inquiry about big data in an Interactive Orchestrated Learning Space (IOLS)
Theoretical approach and design
considerations: statistics education
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Theoretical approach Design considerations
EDA & Inquiry based learning
Guided exploration of real authentic data Learning about topics of interest for that age Choice of topic/RQ/variables to investigate
Collecting live data (by students/through learning)
Promoting statistical reasoning
Promoting thinking about data and its analysis. Looking for meaning, with questions at a higher thinking level
Interaction with visual and dynamic big data sites/statistics tools
Emergence of ideas Encouraging active learning that reveals students concepts and guides the learning
Theoretical approach and design
considerations: Learning sciences
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Theoretical approach Design considerations
Fostering community of learners (Brown, 1997)
The class as a community of learners
Knowledge community
& inquiry (KCI) Developing shared knowledge through collaborative inquiry and advancing and interconnecting shared knowledge through further collaborative inquiry (Slotta & Najafi, 2012)
Future learning space
(FLS)
Take into account the classroom as a physical space
(Slotta , 2010)
Inspired by SAIL Smart Space, that provides scaffolding and sequenced interactions amongst people,
materials, tools and environments (Slotta, Tissenbaum, & Lui, 2013)
Research questions
1. Could students in secondary school learn about big data?
2. If yes, what learning and insights about big data could the students extract from the unit in this setting?
3. What types of activities and forms of collaborative knowledge construction are suitable to support KCI curriculum in topics of big data for secondary school students?
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Settings
• Statistics unit within Math for Data Management (MDM4U) curriculum, interdisciplinary content
• Teaching supported by a statistics professor, Math teacher and designer-researcher
• Two classes of 12th grade students (n
1=25, n2=30) from
above-average academic level secondary school
• 3 weeks, 5 activities (6 sessions)
• Unit website accompanying the program
Methods
Research tools
»
Mixed methods using:
˃ Pre-post test
˃ Video documentation of students in class
˃ Short interviews
˃ Students outcomes (e.g. shared files, PPT)
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Learning trajectory
Lesson Content
Activity 1 Trends in the World of Data
Introduction to world data explorations with GapMinder Activity 2 Big Data interactive
Experience and learn about Big Data in an interactive collaborative way
Activity 3 The faces of big data
Discussion; Elaboration on prediction with Google Flu; Learning iNZight Activity 4 Toronto explorations
Exploring mid-size data with iNZight (Well-being Toronto data) Activity 5 Addressing the Mayor
Students’ pitch
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Design
Data in learning trajectory
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Design
Exploring small/mid size data (that
represent big data)
1
4-5 2, (3)
Learning
Interactive Orchestrated Learning Space (IOLS)
• Take into account the classroom as a physical space
(Slotta, 2010) as well as a learning community, by Community of Learners (Brown & Campione, 1994).
• A technology infrastructure aimed to tap into the
connectivity of the internet and big data resources via the net and other technologies, whilst using generic
collaborative platform for communication and knowledge building (Gil & Slotta, 2015).
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Unit website:
bigdatamdm
Interactive Orchestrated Learning Space (IOLS)
• Collective knowledge building about big data with IOLS
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3. The faces of Big data
Introduction
In this activity you will build upon the knowledge and insights of various
groups’ work in the previous activity and discuss different aspects of Big Data. This discussion will lead us into the second part, in which we will start to
look more specifically at datasets and get to know some statistical software that comes all the way from New Zealand.
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Task part 1: Discussion – the faces of Big Data
1. Read through relevant answers from all of the groups work in Big Data interactive knowledge base.
2. Summarize the knowledge and insights from all groups in this shared PowerPoint document – with 1 (or 2 at the most) slides: Accumulating insights about big data – class 2
Activity 4: Toronto explorations
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map.toronto.ca/wellbeing http://
City explorations
Introduction
The deputy of the newly elected Mayor of Toronto is gathering information about what aspects of city life contribute most to the well-being of Toronto residents. They are calling on people to provide data-based arguments for which aspects should be given the most urgent attention. You are asked to suggest a well-supported argument based on the Toronto Wellbeing data on a selected topic from »Economy (Economics, Housing), »Health,
»Transportation, »Safety/Crime and/or »EEC – Environment + Education + Civics and equity. You will prepare a 3-5 minutes pitch presenting your argument.
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Toronto explorations
• Learning goals relevant to this activity
• Students will investigate mid-size and big data and will explore metropolitan issues (trends and correlations).
• Will build knowledge about big data and gain skills of statistical inquiry in a collaborative, technology supported way.
• Will explain and argue about trends and correlations in the data in the context of big data and informal inference.
• Will be introduced to- and experience statistics as a tool for research, gaining knowledge and for influencing/ creating change.
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Initial results
Initial results 1
RQ1: Could students in secondary school learn about big data?
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Results
Yes. Secondary school students can learn about some aspects of big data.
Initial results 2
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RQ2: What learning and insight could the students extract about big data from the unit in this setting?
• Learning gains (pre and post test)
• Example insights
Students’ learning gains
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Gains in students’
knowledge over the big data three week unit.
34/39 (87.2%) advanced in the familiarity with big data, a concept that was new to many of them.
Q7. How familiar are you with the term “Big Data”?
Examples: Description of big data
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Big data [..] is a collection of data that is very large. It usually requires
powerful computers to be processed effectively. Data can be taken from a variety of sources and used in a variety of fields (Sharon)
Big Data is a large amount of data that can be
processed to analyze trends from numerous sources at once. It compiles
information from multiple sources to allow us to see correlations between different things (Gayle)
Big data is a set of data that has a large volume. It can have a lot of variation and it can be used to find trends in the data (Annie).
There were a few students who knew about big data before the unit – some advanced in their knowledge and some did not Did not know Did know Pre Post
Results
Knowledge base (activity 2)
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Group number
1. What data Twitter analyses?
2. Suggest 1-3 questions that
could be interesting to investigate using Twitter data.
3. What did you learn about Big Data from this activity?
8 Social relationships, followers, tweets (text), subject matter, tags, @ replies, time posted, locations, demographics of users.
1. How do the interest levels vary for different events and topics, over time and in different locations?
2. What topics are discussed by different age groups?
3. How does Twitter activity relate to election results?
More data than one might expect is collected from tweets, even
though they only contain less than 140 characters each. 7 -Text -User Info -Retweets -Geographic location -External links -Social graphs -Time series -Interest graph
-Political groups - which use twitter, how many?
-Sports teams - fans, players, age group using twitter?
-Who else watches trailer park boys?
-Its really complex -Lots of data to analyze and understand
-Not all of it is understood
Students’ insights (activity 3)
• Characteristics of and insights about big data - summary from the classes work
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class 2 class 1
Use in the future
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• Most of the student think they will use big data in their academic or future career (e.g. engineering, law, scientific research and medicine).
• Feedback suggests it was an interesting and useful topic to study in secondary school.
Q10.Do you expect that you will use Big Data in your academic life or future career?
Use in the future (post)
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I'm going into engineering. I'm sure I'll use it at some point
(Sharon)
It is a very
applicable idea that can be used in all fields, e.g. in medicine (Carry) Uses in research, population dynamics, business reports (analysis of client populations (Dean) * Clinical research using hospital patient databases. *
Genomic sequencing (Andrei)
I would like to study computer science and
particularly how it relates to financial markets. It is easy to see big data playing a large role in my work (Carol) Not Expected to use Expected to use Pre Post
Results
Initial results 3
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RQ3: What types of activities and forms of collaborative knowledge construction are suitable to support KCI
curriculum in topics of big data for secondary school students?
Types of activities
1. Engaging activities
2. Interaction with real visual big data that present varied data sources and information about big data
3. Usages of dynamic visual statistics software suitable for secondary school (such iNZight, Fathom or plot.ly)
4. Collaborative and inquiry-base – that builds upon the interest and curiosity of students
5. Big screens are useful
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Summary
• Learning about big data was found to be a very relevant and interesting topic for secondary school students.
• Initial results from both qualitative and quantitative
methods show significant gain of knowledge in learning about big data.
• Engaging activities to learn about big data in the Interactive Orchestrated Learning Space within a
curricular unit that promoted interdisciplinary inquiry of mid-size data supported the learning.
Discussion
• Should we consider introducing more of learning about (big) data to secondary school curriculum?
• What additional aspects of big data can be introduced in such an informal and technological platform?
• How could we use such trajectory and designs to help students and teachers explore with- /teach statistics?
• Any other comments, suggestions, insights..
Thanks
• Fields Institute and UofT Statistics Department
• Prof. Nancy Reid and Prof. Alison Gibbs
• Prof. Jim Slotta
• 12th grade students, Math teacher and the principal of a Toronto secondary school
• To you for listening and for your comments.. Einat Gil [email protected]
Researcher & Designer of Learning (Environments)
Post doctorate fellow at The Fields Institute and the University of Toronto
