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Computational thinking and making in the age of machine learning

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Vartiainen, H. & Tedre, M. (2019). Computational thinking and making in the age of machine learning - and its challenges to Design and Technology education. Presentation in
NordFo Make&Learn Conference, 18.9.2019 Gothenburg.

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Computational thinking and making in the age of machine learning

  1. 1. Computational thinking and making in the age of machine learning and its challenges to Design and Technology education Dr. Henriikka Vartiainen Professor Matti Tedre University of Eastern Finland
  2. 2. Design skills of designing, making and evaluating prototypes and products Understanding the technology -infused world
  3. 3. D&T education also provides a rich context for connecting computational thinking (CT) with digital and material making (e.g. Kafai, Fields, & Searle, 2014; Blikstein, 2016; Resnick, 2017; Fields, Shaw, & Kafai 2018).
  4. 4. ● Computational thinking was first introduced by Seymour Papert (1980) ● He elaborated the constructivist ideas of Jean Piaget into a learning theory he labeled constructionism ● The Logo programming language was the first attempt in education to show that the computer is a powerful tool for design and construction (Papert, 1980; Enkenberg, 1993; Blikstein, 2013; Tedre & Denning, 2016)
  5. 5. In the next wave, Paperts visions were brought to the material world, for example, through Mindstorm kits and the Scratch programming language (Resnick, 2017)
  6. 6. Developments such as laser cutters, 3D modelling, and microcontrollers gave new tools for making (Blikstein, 2013; P)
  7. 7. Design-oriented learning process for computational/material thinking and making (Vartiainen, Tedre, Salonen & Valtonen)
  8. 8. However, the next big wave in D&T education is on it's way
  9. 9. AI and machine learning
  10. 10. Today’s students' lives will be heavily influenced by AI and machine learning.
  11. 11. ML enables new classes of jobs to be automated by learning from examples
  12. 12. How does that feature in D&T education?
  13. 13. Machine Learning in everyday life ● Machine learning: identifying patterns in massive datasets ● Usually relies on user-generated data ● Works the better the more training data it gets ● By combining data from millions of users ML systems can predict what people want to see, do, or experience next
  14. 14. User-generated data opens new ways to affect people’s everyday lives , social interactions, and sway elections
  15. 15. Hybrid technology and transformation of material world ● Nowadays, sensors are everywhere ■ Walls, doors, piping, tables, carpets, cars, clothes, ... ● And they can sense everything measurable ■ Noise, air pollution, heart beat, calories burned, ...
  16. 16. Sensing technologies can ● Help us to monitor our well-being ● Automate many of the routine tasks of daily life ● Privacy?
  17. 17. From coding to data-driven design
  18. 18. Growing understanding and design skills (Adapted from Ceschin & Gaziulusoy, 2019)
  19. 19. What does all this mean for D&T education?
  20. 20. When machines perform predictable, repeatable and routine knowledge work tasks, they simultaneously release people to focus on imaginative and creative tasks Mitchel Resnick
  21. 21. ● Data agency ● Curiosity, creativity and imagination ● Sense making and breaking ● Social and emotional intelligence ● Design skills and mindset ● Computational thinking ● And other skills that are difficult to automate? How can we support our students to develop their (e.g Denning, & Tedre, 2019; Resnick, 2017; Kafai, 2016; Pendleton-Jullian, & Brown, 2018; Valtonen et al., 2019: Mariescu-Istodor, & Jormanainen, 2019).)
  22. 22. Thank You! Dr. Henriikka Vartiainen University of Eastern Finland School of Applied Educational Science and Teacher Education henriikka.vartiainen@uef.fi Professor Matti Tedre University of Eastern Finland School of Computing matti.tedre@uef.fi
  23. 23. References Blikstein, P. & Worsley, M. (2016). Children are not hackers: Building a culture of powerful ideas, deep learning, and equity in the Maker Movement. In K. Peppler, E. Halverson, & Y.B. Kafai (Eds.), Makeology: Makerspaces as learning environments (pp. 64-79). New York, NY: Routledge Ceschin, F. & Gaziulusoy, I. (2019). Design for Sustainability: A Multi-level Framework from Products to Socio-technical Systems. London: Routledge. Denning, P. J. & Tedre, M. (2019). Computational Thinking. Cambridge, MA: MIT Press. Enkenberg, J. (1993). Situation graphs as tools for ordering of students thinking and understanding of actual existing servo mechanisms. Teoksessa B. Dennis, Editor, Control technology in elementary education (s.133g-150). NATO ASI Series. Fields, D. Shaw.M., & Kafai, Y. (2018). Personal Learning Journeys: Reflective Portfolios as “Objects-to-Learn-With” in an E-textiles High School Class. In V. Dagiene & E. Jastuė, Constructionism 2018: Constructionism, Computational Thinking and Educational Innovation: conference proceedings. Retrieved Feb 28, 2019, from http://www. constructionism2018.fsf.vu.lt/proceedings. Mariescu-Istodor, R., & Jormanainen, I. (2019). Machine Learning for High School Students. Proceedings of the 19th Koli Calling International Conference on Computing Education Research, November 21–24, 2019, Koli, Finland. Accepted for publication. Resnick M. (2017). Lifelong Kindergarten: Cultivate Creativity Through Projects, Passion, Peers, and Play. Cambridge, MA: MIT Press. Also https://medium.com/@mres/creativity-and-learning-in-the-era-of-ai-57eea387249d Kafai, Y. B. (2016). From computational thinking to computational participation in K–12 education. Communications of the ACM, 59(8):26–27. Kafai, Y. B. Fields, D. A., & Searle, K. A. (2014). Electronic textiles as disruptive designs in schools: Supporting and challenging maker activities for learning. Harvard Educational Review, 84(4), 532-556. Papert, S. (1980). Mindstorms: Children, Computers, and Powerful Ideas. New York, NY, USA: Basic Books. Pendleton-Jullian, A. & Brown, J. S. (2018). Design Unbound: Designing for Emergence in a White Water World. Cambridge, MA: The MIT Press. Tedre, M., & P. J. Denning. (2016). The long quest for computational thinking. Proceedings of the 16th Koli Calling Conference on Computing Education Research, November 24–27, 2016, Koli, Finland, pp. 120–129. Valtonen, T., Tedre, M., Mäkitalo, K. & Vartiainen, H. (2019). Media Literacy Education in the Age of Machine Learning. Journal of Media Literacy Education, 11(2), 20 -36.

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