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Course, academic year 2024/2025
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Teaching approaches to computational thinking development - OKNI3I025A
Title: Metodické přístupy k rozvoji informatického myšlení
Guaranteed by: Katedra informačních technologií a technické výchovy (41-KITTV)
Faculty: Faculty of Education
Actual: from 2024
Semester: summer
E-Credits: 5
Examination process: summer s.:
Hours per week, examination: summer s.:0/0, MC [HS]
Extent per academic year: 15 [hours]
Capacity: unknown / unknown (unknown)
Min. number of students: unlimited
4EU+: no
Virtual mobility / capacity: no
State of the course: taught
Language: Czech
Teaching methods: combined
Note: course can be enrolled in outside the study plan
enabled for web enrollment
priority enrollment if the course is part of the study plan
Guarantor: doc. RNDr. Miroslava Černochová, CSc.
Teacher(s): doc. RNDr. Miroslava Černochová, CSc.
Pre-requisite : OKNI3I015A
Annotation -
The main attention will be paid to the concept of computational thinking (CT) and didactic ways of developing pupil´s CT at primary and secondary schools. The issue of CT in the context of education is addressed worldwide, not only by ministries of education, governmental strategies for education, educational institutions, but also by global associations focused on computer science, computing and informatics. The course will analyze examples of methodological approaches to the development of CT, using experience not only from the Czech Republic but also from abroad. Emphasis will also be placed on interdisciplinary (interdisciplinary) contexts. The course will focus on the following topics: (1) The concept of CT in the context of the concepts of algorithmic thinking, logical thinking, learning programming. What is not CT. (2) Theoretical basis for methodological approaches to the development of CT. Informatics thinking and tools, applications and a set of skills. Informatics thinking as a language, as an automation of abstractions, as a cognitive means. Computer thinking in context without computer programming. Collaborative dimensions of computer thinking. (3) Samples from educational practice and their didactic analysis. (4) Proposals for educational practice (STEM, AI, etc.). Attention is also paid to the findings of research on computer thinking of pupils of basic and secondary schools.
Last update: Černochová Miroslava, doc. RNDr., CSc. (30.01.2025)
Descriptors - Czech

Metodická opora v moodle (link a přístup bude studentům sdělen na první lekci)

V případě potřeby bude využita také platforma ZOOM.

Příprava na výuku

Doba očekávané přípravy na 1 hodinu přednášky – 30 minut

Doba očekávané přípravy na 1 cvičení – 30 minut

Doba očekávané přípravy na 1 hodinu praxe – 0 minut

Samostudium literatury (za semestr) – 15 hodin

Práce se studijními materiály (za semestr) – 5 hodin

Plnění průběžných úkolů (za semestr) – 30 hodin

Plnění předmětu

Seminární práce – 20 hodin

Příprava na zápočet – 5 hodin

Příprava na zkoušku a zkouška – 0 hodin

Last update: Černochová Miroslava, doc. RNDr., CSc. (18.01.2024)
Course completion requirements -

1. Active work and successful completion of all tasks assigned to solve in F2F seminars.

2. Active work and fulfillment of all tasks assigned in the system of online support of F2F education/ seminars.

3. Design of a methodical approach to the development of computationalr thinking at lower/ upper secondary schools education. Studens will defense their presentation in the last lesson.

Last update: Černochová Miroslava, doc. RNDr., CSc. (30.01.2025)
Literature -

1.      Atieno, Loice Victorine, Richard Kwabena Akrofi Baafi and Turcsanyi-Szabo Marta. (2020) Are Computational Thinking Skills Measurable? An Analysis. International Conference on Applied Informatics (2020).

2.      BATI, K. (2018) Computational Thinking Test (CTT) for Middle School Students. In: Mediterranean Journal of Educational Research, Issue 23, Year 2018. DOI: 10.29329/mjer.2018.138.6

3.      BOCCONI, S., CHIOCCARIELLO, A., KAMPYLIS, P., DAGIENĖ, V., WASTIAU, P., ENGELHARDT, K., EARP, J., HORVATH, M.A., JASUTĖ, E., MALAGOLI, C., MASIULIONYTĖ-DAGIENĖ, V. and STUPURIENĖ, G., Reviewing Computational Thinking in Compulsory Education, Inamorato dos Santos, A., Cachia, R., Giannoutsou, N. and Punie, Y. editor(s), Publications Office of the European Union, Luxembourg, 2022, ISBN 978-92-76-47208-7, doi:10.2760/126955, JRC128347

4.      CURZON, P. et al. (2014) Developing computational thinking in the classroom: a framework. CAS, 2014.

5.      DENNING, P.J. (2017) Remaining Trouble Spots with Computational Thinking. In: COMMUNICATIONS OF THE ACM, JUNE 2017, 60(6), pp. 33-39.

6.      DENNING, P.J., TEDRE, M. (2019) Computational Thinking. MIT.

7.      GOODE, J., MARGOLIS, J. (2011) Exploring Computer Science: A Case Study of School Reform. ACM Transactions on Computing Education, July 2011, 11(2), 2.

8.      HUMPHREYS, S. et al. (2015) Computational thinking. A guide for teachers. CAS, 2015.

9.      CHEN, G., SHEN, J., BARTH-COHEN, L., JIANG, S. (2017) Assessing elementary students’ computational thinking in everyday reasoning and robotics programming. In Computers & Education, 109, pp. 162-175.

10.      IOANNIDOU, A., BENNETT, V., REPENNING, A., KOH, K.H. (2011) Computational Thinking Patterns. Annual Meeting of the American Educational Research Association (AERA), Section 7: Technology Research symposium “Merging Human Creativity and the Power of Technology: Computational Thinking in the K-12 Classroom”, 2011.

11.  KELLEHER, C., PAUSCH, R. (2007) Using Storytelling To Motivate Programming. In: COMMUNICATIONS OF THE ACM, July 2007, 50(7), pp. 58-64.

12.   LESSNER, D. (2014) How do we translate computational thinking into Czech? Journal of Technology and Information Education 1/2014, Volume 6, Issue 1. Časopis pro technickou a informační výchovu ISSN 1803-537X

12.  National Research Council (2009) Report of a Workshop on The Scope and Nature of Computational Thinking. Committee for the Workshops on Computational Thinking. National Academy of Science. Washington, DC: The National Academies Press, 2009.

13.   Palts, Tauno & Pedaste, Margus. (2020) A Model for Developing Computational Thinking Skills. Informatics in Education. 19. 113-128. 10.15388/infedu.2020.06:

14.  PAPERT, S. (1993) Mindstorms. Children, Computers, And Powerful Ideas. Basic Books, 1980, 1993.

15.  SELBY, C.S., WOOLLARD, J. (2013) Computational Thinking: The Developing Definition. Available at <https://eprints.soton.ac.uk/id/eprint/356481>

16.  SHUTE, V.J., SUN, C. & ASBELL-CLARKE, J. (2017). Demystifying computational thinking. In: Educational Research Review, 22(1), 142-158. Elsevier Ltd. Retrieved August 29, 2019 from https://www.learntechlib.org/p/204418/.

17.  WERNER, L., DENNER, J., CAMPE, S. (2012) The Fairy Performance Assessment: Measuring Computational Thinking in Middle School. SIGCSE’12, February29–March 3, 2012 Raleigh, North Carolina, USA. ACM, 2012.

18.  WING, J. (2006) Computational thinking. IN: Communications of the ACM, 2006, 49, pp. 33-36.

19.  YADAV, A., STEPHENSON, HONG, H. (2017) Computational Thinking for Teacher Education. In COMMUNICATIONS OF THE ACM, APRIL 2017, 60(4), pp. 55-62.

Materials, books and documents published by UNESCO, ACM, IFIP, CONSTRUCTIONISM CONFERENCES

Last update: Černochová Miroslava, doc. RNDr., CSc. (30.01.2025)
Syllabus -

Topic 1: The concept of computational thinking

Topic 2: Theoretical basis for methodological approaches to computational thinking development

Topic 3: Components of computational thinking and their didactical analysis

Topic 4: Examples of didactical approaches to computational thinking

Topic 4: Computational thinking development in other educational fields/branches

Topic 6: AI and computational thinking

Topic 7: How can be measured/ investigated a level of computational measured?

 

Last update: Černochová Miroslava, doc. RNDr., CSc. (30.01.2025)
Learning resources -

There is a moodle as a system on-line support for F2F teaching (link and access for using moodle course will be announced to students at the first lesson).

If necessary, the ZOOM platform will also be used.

Last update: Černochová Miroslava, doc. RNDr., CSc. (30.01.2025)
Learning outcomes -

Knowledge-Based Learning Outcomes:

  1. Understand the concept of Computational Thinking (CT) and its relationship to algorithmic thinking, logical thinking, and programming, including an awareness of what does not fall under the scope of CT.
  2. Critically examine theoretical frameworks underlying the development of CT, including the concept of informatics thinking and its role in abstraction, cognitive processes, and language.
  3. Identify and explain the interdisciplinary nature of CT, recognizing its application across various subjects and contexts, including STEM fields and AI.
  4. Analyze global and local educational approaches to teaching CT, with particular focus on methodologies from the Czech Republic and international case studies.
  5. Familiarize with research findings regarding the development of CT skills in primary and secondary school students, understanding common challenges and best practices.

Skills-Based Learning Outcomes:

  1. Apply theoretical knowledge to practice, designing and implementing effective teaching strategies that foster the development of CT in primary and secondary school students.
  2. Evaluate and adapt existing educational materials and case studies for teaching CT, taking into account their didactic value and potential for student engagement.
  3. Design interdisciplinary lessons that integrate CT across subjects, particularly STEM fields, and effectively leverage AI or other relevant tools.
  4. Develop collaborative teaching strategies that promote collective problem-solving and enhance students' understanding of CT through teamwork.

Attitude and Reflection-Based Learning Outcomes:

  1. Reflect on the role of CT in modern education, appreciating its significance not only as a technical skill but also as a cognitive and problem-solving tool.
  2. Cultivate a proactive attitude towards continuous professional development in the field of CT, keeping up with new research, methodologies, and technologies in educational settings.
  3. Demonstrate an understanding of the ethical implications of teaching CT, particularly in relation to AI and automation, and how these issues should be addressed in the classroom.

These outcomes ensure that student teachers not only gain a deep understanding of computational thinking but are also equipped to apply and innovate with CT methodologies in diverse classroom settings.

 

Last update: Černochová Miroslava, doc. RNDr., CSc. (30.01.2025)
 
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