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Design, Implementation, and Evaluation of a Professional Development Program for Teachers to Teach Computational Thinking via Robotics

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Abstract

Since teaching robotics in schools is still new for teachers, studies on how to integrate computational thinking concepts in robotics courses are still rare. In this direction, teacher training sessions for teaching robotics should be visited. Accordingly, in this exploratory case study, a professional development program for teachers was suggested for teaching computational thinking (CT) by using virtual educational robotics. After constructing and delivering the instructional package to six high school computer science teachers, we assessed their pedagogical content knowledge (PCK) development with the assessment tools created through the indicators of integrating CT in robotics. We also monitored two of the teachers in the real classrooms. The results of the study showed that the computer science teachers’ content knowledge (CK) about robotics and CT, and also their PCK to integrate CK in robotics positively improved at sufficient and advanced levels. The fact that the CK was considered as the joint of CT and robotics provided important clues in organizing the training sessions in the context of integrating CT into robotics teaching. Activities about daily life problems, training techniques such as peer assessment, authentic lesson plans, and micro-teaching activities were prominent factors that positively contributed to the development of teachers’ CK and PCK. Structuring the feedback within the framework of CT in the training positively contributed to the teachers’ CK developments in terms of CT and robotics. Guiding teachers to exhibit their teaching roles by presenting concrete examples for individual and collaborative robotics activities also supported the development of teachers' PCK. The implications for course designers desiring to provide a better teaching experience for the teachers teaching CT via robotics were also included.

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References

  • Ackermann, E. (2001). Piaget’s constructivism, Papert’s constructionism: What’s the difference. Future of Learning Group Publication, 5(3), 438.

    Google Scholar 

  • Airasian, P. W. (2001). Classroom assessment: Concepts and applications. McGraw-Hill.

    Google Scholar 

  • Alimisis, D. (2013). Educational robotics: Open questions and new challenges. Themes in Science and Technology Education, 6(1), 63–71.

    Google Scholar 

  • Angeli, C., & Valanides, N. (2005). Preservice teachers as ICT designers: An instructional design model based on an expanded view of pedagogical content knowledge. Journal of Computer-Assisted Learning, 21(4), 292–302. https://doi.org/10.1111/j.1365-2729.2005.00135.x

    Article  Google Scholar 

  • Aparicio, J. T., Aparicio, M., & Costa, C. J. (2018). A virtual robot solution to support programming. In 50th International symposium on robotics-ISR 2018 (pp. 139–142). IEEE. https://doi.org/10.23919/CISTI.2018.8399263

  • Arık, M., & Topçu, M. S. (2022). Computational thinking integration into science classrooms: Example of digestive system. Journal of Science Education and Technology, 31(1), 99–115. https://doi.org/10.1007/s10956-021-09934-z

    Article  Google Scholar 

  • Atmatzidou, S., & Demetriadis, S. (2016). Advancing students’ computational thinking skills through educational robotics: A study on age and gender relevant differences. Robotics and Autonomous Systems, 75, 661–670. https://doi.org/10.1016/j.robot.2015.10.008

    Article  Google Scholar 

  • Barr, V., & Stephenson, C. (2011). Bringing computational thinking to K-12: What is involved and what is the role of the computer science education community? ACM Inroads, 2(1), 48–54. https://doi.org/10.1145/1929887.1929905

    Article  Google Scholar 

  • Benitti, F. B. V. (2012). Exploring the educational potential of robotics in schools: A systematic review. Computers & Education, 58(3), 978–988. https://doi.org/10.1016/j.compedu.2011.10.006

    Article  Google Scholar 

  • Berland, M., & Wilensky, U. (2015). Comparing virtual and physical robotics environments for supporting complex systems and computational thinking. Journal of Science Education and Technology, 24(5), 628–647. https://doi.org/10.1007/s10956-015-9552-x

    Article  Google Scholar 

  • Bers, M. U., Flannery, L., Kazakoff, E. R., & Sullivan, A. (2014). Computational thinking and tinkering: Exploration of an early childhood robotics curriculum. Computers & Education, 72, 145–157. https://doi.org/10.1016/j.compedu.2013.10.020

    Article  Google Scholar 

  • Bocconi, S., Chioccariello, A., Dettori, G., Ferrari, A., Engelhardt, K., Kampylis, P., & Punie, Y. (2016). Developing computational thinking in compulsory education. JRC Science for Policy Report. European Commission.

    Google Scholar 

  • Bower, M., Wood, L. N., Lai, J. W., Highfield, K., Veal, J., Howe, C., Lister, R., & Mason, R. (2017). Improving the computational thinking pedagogical capabilities of school teachers. Australian Journal of Teacher Education, 42(3), 53–72. https://doi.org/10.14221/ajte.2017v42n3.4

    Article  Google Scholar 

  • Brennan, K., & Resnick, M. (2012). New frameworks for studying and assessing the development of computational thinking. In Proceedings of the 2012 annual meeting of the American Educational Research Association (pp. 1–25). The University of British Columbia.

  • Çakır, R., Şahin, H., Balci, H., & Vergili, M. (2021). The effect of basic robotic coding in-service training on teachers’ acceptance of technology, self-development, and computational thinking skills in technology use. Journal of Computers in Education, 8(2), 237–265. https://doi.org/10.1007/s40692-020-00178-1

    Article  Google Scholar 

  • Çakıroğlu, Ü., & Kiliç, S. (2020). Assessing teachers’ PCK to teach computational thinking via robotic programming. Interactive Learning Environments. https://doi.org/10.1080/10494820.2020.1811734

    Article  Google Scholar 

  • Carlborg, N., Tyrén, M., Heath, C., & Eriksson, E. (2019). The scope of autonomy when teaching computational thinking in primary school. International Journal of Child-Computer Interaction, 21, 130–139. https://doi.org/10.1016/j.ijcci.2019.06.005

    Article  Google Scholar 

  • Chang, C. K. (2014). Effect of using Alice and Scratch in an introductory programming course for corrective instruction. Journal of Educational Computing Research, 51(2), 185–204. https://doi.org/10.2190/EC.51.2.c

    Article  Google Scholar 

  • Chen, G., Shen, J., Barth-Cohen, L., Jiang, S., Huang, X., & Eltoukhy, M. (2017). Assessing elementary students’ computational thinking in everyday reasoning and robotics programming. Computers & Education, 109, 162–175. https://doi.org/10.1016/j.compedu.2017.03.001

    Article  Google Scholar 

  • Computing at School. (2019). Computational thinking: How do we think about problems so that computers can help? Retrieved from https://community.computingatschool.org.uk/files/8221/original.pdf

  • CSTA & ISTE. (2011). Computational thinking in K–12 education leadership toolkit. Retrieved from https://id.iste.org/docs/ct-documents/ct-leadershipt-toolkit.pdf?sfvrsn=4

  • Curzon, P., Dorling, M., Ng, T., Selby, C., & Woollard, J. (2014). Developing computational thinking in the classroom: A framework. Retrieved from https://eprints.soton.ac.uk/369594/1/DevelopingComputationalThinkingInTheClassroomaFramework.pdf

  • de Raadt, M. (2008). Teaching programming strategies explicitly to novice programmers (Unpublished doctoral dissertation). University of Southern Queensland, Australia.

  • Dornisch, M. M., & McLoughlin, A. S. (2006). Limitations of web-based rubric resources: Addressing the challenges. Practical Assessment, Research & Evaluation, 11(3), 1–8. https://doi.org/10.7275/7pwk-fy43

    Article  Google Scholar 

  • Eguchi, A. (2010). What is educational robotics? Theories behind it and practical implementation. In D. Gibson (Ed.), In Society for Information Technology & Teacher Education İnternational Conference (pp. 4006–4014). Association for the Advancement of Computing in Education.

  • Eguchi, A. (2014). Educational robotics for promoting 21st century skills. Journal of Automation Mobile Robotics and Intelligent Systems, 8(1), 5–11.

    Article  Google Scholar 

  • Eguchi, A. (2016). RoboCupJunior for promoting STEM education, 21st century skills, and technological advancement through robotics competition. Robotics and Autonomous Systems, 75, 692–699. https://doi.org/10.1016/j.robot.2015.05.013

    Article  Google Scholar 

  • ET 2020 Working Group on Digital Skills and Competences. (2016). Coding and computational thinking on the curriculum. Key messages of PLA#2 Helsinki. Retrieved from http://ec.europa.eu/education/sites/education/files/2016-pla-coding-computationalthinking_en.pdf

  • Furber, S. (2012). Shut down or restart? The way forward for computing in UK schools. The Royal Society. Retrieved from https://royalsociety.org//media/education/computing-in-schools/2012-01-12-computing-in-schools.pdf

  • Gabriele, L., Bertacchini, F., Tavernise, A., Vaca-Cárdenas, L., Pantano, P., & Bilotta, E. (2019). Lesson planning by computational thinking skills in Italian pre-service teachers. Informatics in Education, 18(1), 69–104. https://doi.org/10.15388/infedu.2019.04

    Article  Google Scholar 

  • Gess-Newsome, J. (2015). A model of teacher professional knowledge and skill including PCK. In A. Berry, P. Friedrichsen, & J. Loughran (Ed.), Re-examining pedagogical content knowledge in science education (pp. 28–42). Routledge.

  • Gouws, L. A., Bradshaw, K., & Wentworth, P. (2013). Computational thinking in educational activities: An evaluation of the educational game light-bot. In T. J. Cortine (Ed.), In Proceedings of the 18th ACM conference on innovation and technology in computer science education (pp. 10–15). Association for Computing Machinery.

  • Grossman, P. L. (1990). The making of a teacher: Teacher knowledge and teacher education. Teachers College Press.

    Google Scholar 

  • Grover, S., & Pea, R. (2013). Computational thinking in K-12: A review of the state of the field. Educational Researcher, 42(1), 38–43. https://doi.org/10.3102/0013189X12463051

    Article  Google Scholar 

  • Gülbahar, Y. (Ed.). (2018). From computing thinking to programming (2nd ed.). Pegem Academy.

    Google Scholar 

  • Guven, G., & Kozcu Cakir, N. (2020). Investigation of the opinions of teachers who received ın-service training for arduino-assisted robotic coding applications. Educational Policy Analysis and Strategic Research, 15(1), 253–274. https://doi.org/10.29329/epasr.2020.236.14

    Article  Google Scholar 

  • Han, S., & Bhattacharya, K. (2001). Constructionism, learning by design, and project-based learning. In M. Orey (Ed.), Emerging perspectives on learning, teaching, and technology (pp. 1–18). Retrieved from http://epltt.coe.uga.edu/

  • Harel, I. E., & Papert, S. E. (1991). Constructionism. Ablex Publishing.

    Google Scholar 

  • Hashweh, M. Z. (2005). Teacher pedagogical constructions: A reconfiguration of pedagogical content knowledge. Teachers and Teaching, 11(3), 273–292. https://doi.org/10.1080/13450600500105502

    Article  Google Scholar 

  • Hill, H. C., Ball, D. L., & Schilling, S. G. (2008). Unpacking pedagogical content knowledge: Conceptualizing and measuring teachers’ topic-specific knowledge of students. Journal for Research in Mathematics Education, 39(4), 372–400. https://doi.org/10.5951/jresematheduc.39.4.0372

    Article  Google Scholar 

  • Hubwieser, P., Magenheim, J., M€uhling, A., & Ruf, A. (2013). Towards a conceptualization of pedagogical content knowledge for computer science. In Proceedings of the ninth annual international ACM conference on international computing education research-ICER’13 (pp. 1–8). ACM. https://doi.org/10.1145/2493394.2493395

  • ISTE. (2015). It’s time to demystify computational thinking. Retrieved from https://www.iste.org/explore/articleDetail?articleid=501

  • ISTE. (2016). ISTE standarts for students. Retrieved from http://www.iste.org/standarts/for-students

  • Jaipal-Jamani, K., & Angeli, C. (2017). Effect of robotics on elementary preservice teachers’ self-efficacy, science learning, and computational thinking. Journal of Science Education and Technology, 26(2), 175–192. https://doi.org/10.1007/s10956-016-9663-z

    Article  Google Scholar 

  • Karim, M. E., Lemaignan, S., & Mondada, F. (2015). A review: Can robots reshape K-12 STEM education? In 2015 IEEE international workshop on Advanced robotics and its social impacts (ARSO) (pp. 1–8). IEEE.

  • Kartal, B. (2017). Examining the development of pre-service mathematics teachers' technological pedagogical content knowledge: The case of polygons (Unpublished doctoral dissertation). İnönü University, Institute of Educational Sciences, Malatya.

  • Keith, P. K., Sullivan, F. R., & Pham, D. (2019). Roles, collaboration, and the development of computational thinking in a robotics learning environment. Computational Thinking Education (pp. 223–245). Springer.

    Chapter  Google Scholar 

  • Kert, S. B. (2018). Introduction to computer science education. Pegem Academy.

  • Kılıç, S., & Gökoğlu, S. (2021). Exploring the usability of virtual robotics programming curriculum for robotics programming teaching. Informatics in Education. https://doi.org/10.15388/infedu.2022.20

  • Kirkpatrick, D. (1996). Revisiting Kirkpatrick’s four-level model. Training & Development, 50(1), 54–57.

    Google Scholar 

  • Koehler, M., & Mishra, P. (2009). What is technological pedagogical content knowledge (TPACK)? Contemporary Issues in Technology and Teacher Education, 9(1), 60–70.

    Google Scholar 

  • Kwon, K. (2017). Novice programmer’s misconception of programming reflected on problem-solving plans. International Journal of Computer Science Education in Schools, 1(4), 4. https://doi.org/10.21585/ijcses.v1i4.19

    Article  Google Scholar 

  • Leonard, J., Buss, A., Gamboa, R., Mitchell, M., Fashola, O. S., Hubert, T., & Almughyirah, S. (2016). Using robotics and game design to enhance children’s self-efficacy, STEM attitudes, and computational thinking skills. Journal of Science Education and Technology, 25(6), 860–876. https://doi.org/10.1007/s10956-016-9628-2

  • Liu, A. S., Schunn, C. D., Flot, J., & Shoop, R. (2013). The role of physicality in rich programming environments. Computer Science Education, 23(4), 315–331. https://doi.org/10.1080/08993408.2013.847165

    Article  Google Scholar 

  • Ministry of National Education. (2019). Sample in-service training programs prepared in accordance with the developed and updated standard criteria. http://oygm.meb.gov.tr/dosyalar/StPrg/

  • Mouza, C., Yang, H., Pan, Y. C., Ozden, S. Y., & Pollock, L. (2017). Resetting educational technology coursework for pre-service teachers: A computational thinking approach to the development of technological pedagogical content knowledge (TPACK). Australasian Journal of Educational Technology, 33(3), 1–16. https://doi.org/10.14742/ajet.3521

  • Negrini, L. (2018). Teacher training in educational robotics. In International conference on robotics and education-RiE 2017 (pp. 92–97). Springer.

  • Numanoğlu, M., & Keser, H. (2017). Robot usage in programming teaching-Mbot Example. Bartin University Faculty of Education Journal, 6(2), 497. https://doi.org/10.14686/buefad.306198

  • Oliver, R. (1993). Measuring hierarchical levels of programming knowledge. Journal of Educational Computing Research, 9(3), 299–312. https://doi.org/10.2190/0LGX-M45X-2WBK-B7A6

    Article  Google Scholar 

  • Papert, S. (1996). An exploration in the space of mathematics educations. International Journal of Computers for Mathematical Learning, 1(1), 95–123.

    Article  Google Scholar 

  • Penmetcha, M. R. (2012). Exploring the effectiveness of robotics as a vehicle for computational thinking (Unpublished doctoral dissertation). Purdue University, USA.

  • Peters-Burton, E., Rich, P. J., Kitsantas, A., Laclede, L., & Stehle, S. M. (2021). High school science teacher use of planning tools to integrate computational thinking. Journal of Science Teacher Education. https://doi.org/10.1080/1046560X.2021.1970088

    Article  Google Scholar 

  • Ronsivalle, G. B., Boldi, A., Gusella, V., Inama, C., & Carta, S. (2019). How to implement educational robotics’ programs in Italian schools: A brief guideline according to an instructional design point of view. Technology, Knowledge and Learning, 24(2), 227–245. https://doi.org/10.1007/s10758-018-9389-5

    Article  Google Scholar 

  • Saks, A. M., & Burke, L. A. (2012). An investigation into the relationship between training evaluation and the transfer of training. International Journal of Training and Development, 16(2), 118–127. https://doi.org/10.1111/j.1468-2419.2011.00397.x

    Article  Google Scholar 

  • Selby, C., & Woollard, J. (2013). Computational thinking: The developing definition. http://eprints.soton.ac.uk/356481

  • Shoop, R., Flot, J., Friez, T., Schunn, C., & Witherspoon, E. (2016). Can computational thinking practices be taught in robotics classrooms? https://www.cmu.edu/roboticsacademy/PDFs/Research/CTP_RoboticsClassrooms.pdf

  • Shulman, L. (1987). Knowledge and teaching: Foundations of the new reform. Harvard Educational Review, 57(1), 1–23.

    Article  Google Scholar 

  • Slangen, L., Van Keulen, H., & Gravemeijer, K. (2011). What pupils can learn from working with robotic direct manipulation environments. International Journal of Technology and Design Education, 21(4), 449–469.

    Article  Google Scholar 

  • Stewart, W. H., Baek, Y., Kwid, G., & Taylor, K. (2021). Exploring factors that influence computational thinking skills in elementary students’ collaborative robotics. Journal of Educational Computing Research, 59(6), 1208–1239. https://doi.org/10.1177/0735633121992479

    Article  Google Scholar 

  • Sullivan, F. R., & Moriarty, M. A. (2009). Robotics and discovery learning: Pedagogical beliefs, teacher practice and technology integration. Journal of Technology and Teacher Education, 17(1), 81–114.

    Google Scholar 

  • Sun, L., Hu, L., & Zhou, D. (2021). Improving 7th-graders’ computational thinking skills through unplugged programming activities: A study on the influence of multiple factors. Thinking Skills and Creativity, 42, 100926.

    Article  Google Scholar 

  • Threekunprapa, A., & Yasri, P. (2020). Unplugged coding using flow blocks for promoting computational thinking and programming among secondary school students. International Journal of Instruction, 13(3), 207–222.

    Article  Google Scholar 

  • Üçgül, M. (2013). History and educational potential of Lego Mindstorms NXT. Mersin Üniversitesi Eğitim Fakültesi Dergisi, 9(2), 127–137.

    Google Scholar 

  • Usengül, L., & Bahçeci, F. (2020). The effect of LEGO WeDo 2.0 education on academic achievement and attitudes and computational thinking skills of learners toward science. World Journal of Education, 10(4), 83–93. https://doi.org/10.5430/wje.v10n4p83

    Article  Google Scholar 

  • Vainio, V., & Sajaniemi, J. (2007). Factors in novice programmers’ poor tracing skills. ACM SIGCSE Bulletin (pp. 236–240). Association for Computing Machinery.

    Google Scholar 

  • Wing, J. M. (2006). Computational thinking. Communications of the ACM, 49(3), 33–35. https://doi.org/10.1145/1118178.1118215

    Article  Google Scholar 

  • Wing, J. M. (2008). Computational thinking and thinking about computing. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 366(1881), 3717–3725. https://doi.org/10.1098/rsta.2008.0118

    Article  Google Scholar 

  • Wing, J. M. (2011). Research notebook: Computational thinking what and why. http://www.cs.cmu.edu/link/research-notebook-computational-thinking-what-and-why

  • Witherspoon, E. B., Higashi, R. M., Schunn, C. D., Baehr, E. C., & Shoop, R. (2017). Developing computational thinking through a virtual robotics programming curriculum. ACM Transactions on Computing Education (TOCE), 18(1), 4–24. https://doi.org/10.1145/3104982

    Article  Google Scholar 

  • Yadav, A., Berges, M., Sands, P., & Good, J. (2016). Measuring computer science pedagogical content knowledge: An exploratory analysis of teaching vignettes to measure teacher knowledge. In Proceedings of the 11th workshop in primary and secondary computing education (pp. 92–95). ACM. . https://doi.org/10.1145/2978249.2978264

  • Yadav, A., Mayfield, C., Zhou, N., Hambrusch, S., & Korb, J. T. (2014). Computational thinking in elementary and secondary teacher education. ACM Transactions on Computing Education, 14(1), 5–25. https://doi.org/10.1145/2576872

    Article  Google Scholar 

  • Yadav, A., Stephenson, C., & Hong, H. (2017). Computational thinking for teacher education. Communications of the ACM, 60(4), 55–62. https://doi.org/10.1145/2994591

    Article  Google Scholar 

  • Yadav, A., Zhou, N., Mayfield, C., Hambrusch, S., & Korb, J. T. (2011). Introducing computational thinking in education courses. In T. J. Cortine (Ed.), In Proceedings of the 42nd ACM technical symposium on computer science education (pp. 465–470). Association for Computing Machinery. https://doi.org/10.1145/1953163.1953297

  • Yalın, H. İ, Hedges, L., & Özdemir, S. (1996). In-service training program development handbook. National Education Press.

    Google Scholar 

  • Yildiz Durak, H., Saritepeci, M., & Durak, A. (2021). Modeling of relationship of personal and affective variables with computational thinking and programming. Technology, Knowledge and Learning, 66, 1–20.

    Google Scholar 

  • Zhang, L., & Nouri, J. (2019). A systematic review of learning computational thinking through Scratch in K-9. Computers & Education, 141, 103607. https://doi.org/10.1016/j.compedu.2019.103607

    Article  Google Scholar 

Download references

Acknowledgements

This study was derived from a part of a doctoral dissertation of the first author submitted to Karadeniz Technical University, Turkey.

Author information

Authors and Affiliations

  1. Computer Technologies Department, Technical Sciences Vocational High School, Ordu University, PK 52200, Ordu, Turkey

    Servet Kılıç

  2. Computer Education and Instructional Technology Department, Fatih Faculty of Education in Trabzon, Trabzon University, Trabzon, Turkey

    Ünal Çakıroğlu

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  1. Servet Kılıç
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Correspondence to Servet Kılıç.

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Appendices

Appendix 1

Behaviors of the activities and data analysis form

Acquisitions

Criterion

Level

General E

Robot component and programming structure knowledge (C1)

Knowledge of programming structures usage (C2)

1

2

3

1

2

3

1. Programming the robot using move steering and move tank block to move wheels and give different positions (A1)

        

2. Programming the robot using the large motor block to move the arm (A2)

        

3. Using the distance sensor and programming the robot by using the wait block with the sensor (A3)

        

4. Using the color sensor and programming the robot using the sensor and switch block (A4)

        

5. Using gyro sensor and programming the robot using the sensor and wait block (A5)

        

6. Using the loop block to repeat movements (A6)

        

Robot component and programming structure knowledge: (3p-Advanced: The programming block required to run the related component of the robot was correctly determined on the first attempt. 2p-Sufficient: The programming block required to run the related component of the robot was determined correctly by making a few attempts. 1p-Insufficient: The programming block required to run the related component of the robot could not be determined correctly)

Programming structure usage knowledge: (3p-Advanced: Appropriate parameter values were reached in using the block with an error below the average number of errors. 2p-Sufficient: Appropriate parameter values were reached by using the block with a value between the average number of errors. 1p-Insufficient: Appropriate parameter values were reached in using the block with an error on the average number of errors)

Appendix 2

Data analysis form for revealing semantic knowledge

Activity Name: Teacher Code:

Indicators

General Level

Creating an algorithm (Se1)

Using unnecessary code block (Se2)

1

2

3

1

2

3

Creating an algorithm (3p-Advanced: The algorithm created provided the activity to be fully realized. 2p-Sufficient: The algorithm created provided the majority of the activity to be realized. 1p-Insufficient: The algorithm created provided some of the activity to be realized)

Using unnecessary code block (3p-Advanced: An algorithm was created using the unnecessary block of code under the average value. 2p-Sufficient: An algorithm was created by using the unnecessary block of code between average values. 1p-Insufficient: An algorithm was created using unnecessary code block on average value)

Appendix 3

Data analysis form for revealing strategic knowledge

Activity Name: Teacher Code:

Indicators

General L

Noticing and correcting the error (St1)

Problem Solving Status (St2)

Time to solve the Problem (St3)

1

2

3

1

2

3

1

2

3

Noticing and correcting the error: (3p-Advanced: The activity was carried out without making a mistake affecting the solution. 2p-Sufficient: There was a small amount of error that affected reaching the solution. 1p-Insufficient: Too many errors have been made that affect reaching the solution)

Problem solving status: (3p-Advanced: The activity was carried out by testing the algorithm under the average value. 2p-Sufficient: The activity was carried out by testing the algorithm among the average values. 1p-Insufficient: The activity was carried out by testing the algorithm on the average value)

Time to solve the problem: (3p-Advanced: The activity was held before the given time. 2p-Sufficient: The activity was held within the given time. 1p-Insufficientnt: The activity was held in the additional time given)

Appendix 4

Data analysis form for revealing the CK about CT

Components

1

2

3

Level

General level

Decomposition

Crit.-1 (D1)

     

Crit.-2 (D2)

     

Abstraction

Crit.-1 (Ab1)

     

Crit.-2 (Ab2)

     

Generalization

Crit.-1 (G1)

     

Crit.-2 (G2)

     

Algorithmic Thinking

     

Debugging

     

Indicators of the criteria

Decomposition, Abstraction, Generalization

3p-Advanced: All items are suitable for the skill

Criterion-1

2p-Sufficient: Most of the items are suitable for the skill

1p-Insufficient: Some of the items are suitable for the skill

Decomposition, Abstraction, Generalization

3p-Advanced: Items handling of the whole activity

Criterion-2

2p-Sufficient: Items handling most of the activity

1p-Insufficient: Items handling some of the activity

Algorithmic Thinking

3p-Advanced: Realization of the whole activity with the created algorithm

2p-Sufficient: Realization of most of the activity with the created algorithm

1p-Insufficient: Realization of some of the activity with the created algorithm

Debugging

3P-Advanced: Awareness and regulation of all errors

2P-Sufficient: Awareness and regulation of most errors

1P-Insufficient: Awareness and regulation of some of the errors

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Kılıç, S., Çakıroğlu, Ü. Design, Implementation, and Evaluation of a Professional Development Program for Teachers to Teach Computational Thinking via Robotics. Tech Know Learn 28, 1539–1569 (2023). https://doi.org/10.1007/s10758-022-09629-3

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  • DOI: https://doi.org/10.1007/s10758-022-09629-3

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