Computation Thinking describes the ability to purposefully use computers for problem solving. Computation Thinking and Acting focuses on using technologies for solving real world problems. The slides give examples and solutions how to include COTA in primary schools.

1. Computational Thinking and
Acting: Future Technologies
for Future Generations
09.10.2020, eASEM RN1
Prof. Dr. Jan Pawlowski
Ruhr West University of Applied
Sciences / University of Jyväskylä

2. 2Prof. Dr. Jan M. Pawlowski
Welcome to the Ruhr
Metropolitan Area 
Mülheim/Bottrop

3. 3Prof. Dr. Jan M. Pawlowski
Focus Areas
• Global Process Management
• Collaborative Innovation
Management
• Competence Management,
Knowledge Management and E-
Learning
• Open Innovation, Open
Educational Resources
Researching Processes and Systems in a Global
Context
Glo-Link
Global Learning, Innovation and Knowledge Management

4. The COTA Consortium
Estonia
Greece
Germany
Finland

5. Digital Transformation Challenges
Emerging Challenges
– New skills required in all industries,
on all educational levels
– Preparing teachers and kids for the
challenges of digital transformation
– Ensuring appropriate strategies for
schools and educational institutions
Automation
https://www.tesla.com/videos/autopilot-self-driving-hardware-neighborhood-long%20
Artificial Intelligence
https://commons.wikimedia.org/wiki/File:Factory_Automation_Robotics_Palettizing_Bread.jpg
Technologies and Data

6. Computational Thinking
• “solving problems, designing systems, and understanding
human behavior, by drawing on the concepts fundamental to
computer science” (Wing, 2006)
• “… solutions are represented in a form that can be effectively
carried out by an information-processing agent” (Cuny et al,
2010)
• “the ability to think with the computer-as-tool” (Berland &
Wilensky, 2015)
• “The ability to understand information and communication
technologies and their key concepts, methods and tools to
purposefully utilize those for problem solving” (Pawlowski,
2019)

7. Computational Thinking: Typical
Competencies (cf. Brennan & Resnick,
2012)
• Practice (problem
solving practices,
experimenting and
iterating, testing and
debugging, reusing and
mixing, and abstracting
and modularisation)
• Perspectives
(understandings of
themselves, their
relationships with
others, and the digital
world around)
• Core Concepts
(sequences, loops,
events, parallelism,
conditionals, operators,
and data)
• New technologies?
– AI
– Robotics
– (Big) Data
– Social media

8. Research Gaps
• Focus on coding and STEM
• Often „artificial“ problems – lack of transfer skills
for real world problems
• Attitudes play an important role but are not
modelled in competency frameworks
• Lack of inclusion of disruptive technologies such
as Artificial Intelligence or Internet of Things
• Our solution: moving from computational thinking
to computational thinking and acting, based on the
concepts of physical computing

9. Computational Thinking and Acting
project in a nutshell
- Objective: Develop
Physical computing skills
in primary level (grade 3-
6)
- Connect real world
problems with computer
solutions
- Creating tangible
outcomes
- Increasing motivation and
positive attitudes
Key Outcomes
- 130 Learning scenarios in
physical computing with CC
license
- COTA Pedagogical
Framework
- Competence framework for
Computational Thinking
- More than 200 teachers
across Europe using the
approach and materials across
subjects

10. Physical Computing?
“A holistic enabling approach to building
programming competencies including haptic
experiences accompanied by physical activities”
– Physical Input: Observing real life activities
– Physical Transfer: Utilizing ICT solutions to solve real-
life activities
– Physical Output: Devices such as small robots are
used to create haptic, tangible experiences

11. COTA Principles and Curriculum
• Real World Problem-Oriented:
Learners should experience and
identify problems from the real
world
• Learner-Centric: Learners should
be engaged and empowered to
create own ideas and solution
strategies.
• Cross-subject: Learning
scenarios should not be restricted
to computer science / ICT.
• Physical: each learning scenarios
should incorporate physical
activities as input and output.
• Transferable: Each solution
should be reflected to understand
the transfer to other problems /
subjects / domains.

12. COTA: Experiencing Machine
Learning
Real World Scenario: Recognize an Object, in our cases plants
Computer Scenario 1: Classification using pseudo code
Learning Activitie Phases of the learning scenario
LA1: Context setting Students should discuss how to distinguish plants - what are criteria to distinguish leaves
LA2: Exploration Students will do a short walk with the task to find three different trees. They should take pictures on their mobile
phones from different perspectives.
Students should formulate the problem (“identifying objects…”)
Students should find criteria how to match a picture with the name of the tree.
LA3: Elaboration Two sorts of solutions can be elaborated:
Developing an algorithm in pseudo code. This should define the steps from matching their (self defined) criteria
with attributes of a tree (e.g. leave form, colour, …)
As a second activity, students can use the teachable machine to train the recognition of different pictures of leaves
/ trees.
The elaboration includes support by the teacher regarding formulating / coding as well as the use of the teachable
machine
LA4: Production Students present their solution
In a group review, suggestions for improvements are given
Students then take their trained machine to the same trees to find out whether the AI program works
LA5: Reflection Students find further examples where pattern recognition / machine learning is used in everyday life (e.g. driving,
face recognition with a mobile phone). Students also discuss what can happen when training data are
manipulated.

13. COTA: Experiencing Machine
Learning
Real World Scenario: Recognize an Object, in our cases plants
Computer Scenario 2: Classification using google’s teachable machine
https://teachablemachine.withgoogle.com/

14. COTA: Experiencing Machine
Learning
Discussing the limits of ML – Confusing teachablemachine to
encourage critical thinking
https://teachablemachine.withgoogle.com/

15. Aquaponics or The Fish Garden (Grade
5-6)
Real World Scenario
• Circular systems
• Growing plants
• Food production
(fishes)
Computer Scenario
• Control systems
• IoT & Sensors
• Arduino programming
(Open Roberta /
NEPO)
• 3D printing
• …
Tangible Outcome
• A running, (almost)
self-sustained system
for the school
Learning Scenario Title Aquaponics
Context / Target group 6
th
grade
Curricula topics /
competencies
• Problem identification, algorithms, generalization,
programming, 3D printing, sensor systems
Educational approach • Problem-based learning
Methods and materials • Arduino, sensors (temperature, water level, pH
value, humidity), pumps, aquarium 60l, …
• Worksheets
Learning Activities (LA) • Context setting
• Problem exploration and identification
• Transfer and Elaboration
• Production
• Reflection
https://youtu.be/MH22edm0S9M

16. COTA: Preparations / control center
Computer Scenario
• Arduino
• Pump
• Moisture sensor
• Temperature sensor
• Warning system
• …
• Coding: Open Roberta / https://youtu.be/MH22edm0S9M

17. Fish Garden in Use
Physical Outcome
• 10 Guppies
• Mince / basil
• Pumps incl. bell syphon
• 3D printed brackets
• Display for warning
messages
• Possible extensions: Feeding
/ light system
https://youtu.be/MH22edm0S9M

18. Initial Evaluation
• Interviews with 41 individuals: teachers (n2=30),
headmasters (n=5), university lecturers (n=4), teacher
training specialist (n=1) and educational technologist
(n=1)
• Positive perception of the following elements
– Real-life problem solving
– Use of robots and micro-controllers
– Collaborative “hands-on” activities
• Competency framework
– Top Competencies: 1) Problem solving, 2) Algorithmic
thinking, 3) Digital / media literacy, 4) Utilizing programs for
problem solving
– CT more important than programming

19. Initial Evaluation
• Support needs to implement the
approaches

20. Conclusion and Outlook
• Initial validation results show
– The need of modernizing teacher education
– The need for strategies for teaching and learning new
skills
– COTA approach as a promising concept if the support
needs are considered
– COTA has specific learning scenarios for disruptive
technologies
• Next steps
– Stage-wise evaluation with teachers and students
– Applying the concept to different subject combinations
– Long-term validations, e.g., regarding employability /
academic success
– Collaborations across cultures

21. Contact
• Hochschule Ruhr West, Germany
Martin Idzik, Prof. Dr. Jan M. Pawlowski,
{martin.idzik|jan.pawlowski@hs-
ruhrwest.de}
• Coordinator
Dr. Kati Clements, University of Jyväskylä,
Finland, kati.clements@jyu.fi
• Project website: http://cota-project.eu/
• Youtube video on Aquaponics:
https://youtu.be/MH22edm0S9M

22. Useful references
• Bocconi, S., Chioccariello, A., Dettori, G., Ferrari, A., & Engelhardt, K. (2016). Developing
computational thinking in compulsory education-Implications for policy and practice (No.
JRC104188). Joint Research Centre (Seville site).
• Caeli, E. N., & Bundsgaard, J. (2019). Computational thinking in compulsory education: a
survey study on initiatives and conceptions. Educational Technology Research and
Development, 1-23.
• Shute, V. J., Sun, C., & Asbell-Clarke, J. (2017). Demystifying computational
thinking. Educational Research Review, 22, 142-158.
• Brennan, K., & Resnick, M. (2012, April). New frameworks for studying and assessing the
development of computational thinking. In Proceedings of the 2012 annual meeting of the
American Educational Research Association, Vancouver, Canada (Vol. 1, p. 25).
• Cuny, J., Snyder, L., Wing, J.M., (2010). Demystifying computational thinking for non-
computer scientists. Unpublished manuscript.
• Wing, J.M., (2006). Computational thinking. Communications of the ACM 49 (3), 33–35
• Rees, A., García-Peñalvo, F. J., Jormanainen, I., Tuul, M., & Reimann, D. (2016). An
overview of the most relevant literature on coding and computational thinking with
emphasis on the relevant issues for teachers.
• Wing, J. (2011). Research notebook: Computational thinking—What and why. The Link
Magazine, 20-23.
• CSTA (2017). K12-Computer science standards. Computer Science Teachers Association.