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CAS data literacy

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Presentation at Computing at Schools conference, June 2017.

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CAS data literacy

  1. 1. Improving data literacy Annika Wolff, Michel Wermelinger School of Computing & Communications The Open University
  2. 2. The Urban Data School • Developed an approach for using large, complex data sets to teach data literacy skills in schools • Used data collected as part of a large ‘smart city’ project (http://mksmart.org) • Designed approach in collaboration with teachers and students in Milton Keynes
  3. 3. Key idea 1: Teach in a context that the student can relate to • We used data about ‘energy’ – a topic that students had already started learning • We used data that was from their own town Solar panel map Heat map Data from smart meters (70 MK houses)
  4. 4. Key idea 2: follow an inquiry process Students start by interpreting outcome of existing inquiry (guided inquiry) and this prompts new round of questions (open inquiry) We based all of our lesson plans on energy research that was being conducted at the Open University.
  5. 5. Key idea 3: work ‘out’ not ‘in’ • The guided part of the inquiry should always start from a ‘snapshot’ of the data • Students interpret one day of ‘whole house’ energy consumption from smart meter data. • Students expand inquiry to ask questions that interest them – look at individual appliances – other days/seasons – other houses…
  6. 6. Key idea 4: students collect their own data Help students understand the data by getting them to collect similar data of their own • Students ‘become’ a smart meter for a week and record when energy is used at home • Students use plasticine models to learn how LiDAR data is collected
  7. 7. Key idea 5: include creative activities alongside practical ones Novel data visualisation App design
  8. 8. Summary of Engagement School Sessions Wavendon Gate (Primary) Energy tasks The Academy (Secondary) Energy tasks + app design Denbigh (S) Energy tasks + app design Stantonbury (S) Energy tasks Urban Scholars (various S) App design UCMK Graduate Fair (S) Energy tasks Wavendon Gate (P) Energy tasks + plasticine modelling 13 SCHOOLS IN TOTAL ≈ 150 students
  9. 9. Important Findings • Students are engaged by local data. Driven to search for the familiar and pose questions about things they know • Teachers need support, too - but they think the data is an excellent teaching resource
  10. 10. Learn to Code for Data Analysis • Free introduction to coding and data analysis – Coding for reproducible research • Futurelearn MOOC: http://tiny.cc/lcda – 5+ h/week, 4 weeks, in May + Oct each year • Also on OpenLearn: http://tiny.cc/lcda-ol – 24/7 but no discussion forums, no support – used by Space Science MSc module – materials available under CC-BY-NC-SA license • 40min session based on week 1 for Year 10
  11. 11. Learning • International open data: WHO, WU, WB, UN. • Weekly project: – Start with research questions – We answer them, introducing necessary concepts – Interleave reading and doing exercises – End with data analysis report • Show and tell: share report on own region
  12. 12. First Principles of Instruction Problem-centred: Base the teaching and learning on interesting and progressively more complex real-world problems. 1. Activation: Help the learners activate past experience, information or mental models that can be used to organise the new knowledge. 2. Demonstration: Show the learners the new knowledge, e.g. through worked examples, preferably with multiple viewpoints. 3. Application: Give learners a sequence of varied problems for them to apply the new knowledge. Provide feedback and diminishing guidance, e.g. on how to correct mistakes. 4. Integration: Encourage learners to discuss, reflect on, and publicly demonstrate their new knowledge or skill, to integrate it into their lives. More details: http://tiny.cc/fpoi
  13. 13. Technology • Python: easy to learn, used in STEM Faculty • Pandas: R-like data analysis library • Jupyter notebooks for exercises and reports • http://continuum.io/anaconda: computer app – free for Windows, Mac, Linux • http://cocalc.com: free web service – features to distribute, collect, grade assignments
  14. 14. Jupyter notebooks • text editor/formatter + code editor/interpreter – free professional but easy-to-use software – data scientists use for reproducible research – we use in under- and post-graduate courses • text, code, and outputs (tables, charts, ...) – handouts with examples and exercises – assignment reports – text in Markdown, code in Python, both widely used • code one line at a time, with immediate feedback
  15. 15. Jupyter notebooks • create and edit notebooks in browser – students can add own notes and fix typos quickly • publish read-only version, e.g. Y10 notebook – export to HTML or PDF and share file – single-click publish in CoCalc – put notebook file online, paste URL in http://nbviewer.jupyter.org – publish on GitHub (with version control for free!)
  16. 16. Demo time! Plan A: live demo (fingers crossed…) Plan B: a screencast Plan C: more slides
  17. 17. Problems • Keeping up with software and site changes • Provide all data offline because: – Online (historic) data changes (teaching point!) – Free CoCalc account doesn’t allow API calls • File encoding issues • [](){} and its combinations can be confusing • Convincing the Excel fans
  18. 18. Take away • Free course for CPD and re-purposing • Follow First Principles of Instruction • Problem-driven data analysis is engaging – It’s interdisciplinary, local, global, personal
  19. 19. Take away • Notebooks are an alternative approach to coding – code + text (explanation, analysis, study notes) – Incremental and iterative coding/debugging • exploratory data analysis without clear research questions – Require some discipline • Be aware and prepared for data + software issues • Coding is a powerful way to improve data literacy
  20. 20. Contact We’d be happy to help you, learn from you, and work with you. E-mail: first.lastname@open.ac.uk

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