Student’s Computational Thinking Process in Solving PISA Questions in Terms of Problem Solving Abilities

Authors

  • M. Gunawan Supiarmo UIN Maulana Malik Ibrahim Malang
  • Heri Sopian Hadi UIN Maulana Malik Ibrahim Malang
  • Tarmuzi Tarmuzi SDN 1 Sembalun Lombok Timur

DOI:

https://doi.org/10.22460/jiml.v5i1.p01-11

Keywords:

Computational Thinking, Problem Solving, PISA Problems

Abstract

Computational thinking is the process of solving problems using logic gradually and systematically needed in the field of mathematics. However, the learning applied by the teacher limits the student's ability to develop computational thinking skills. Teachers are accustomed to providing conventional learning and emphasize student's skills in using formulas. One of the treatments that can be used to stimulate student's computational thinking skills is PISA questions. The purpose of this study was to analyze student's computational thinking processes in solving PISA questions in terms of their problem solving abilities. The research data consisted of student answers, think aloud results, and semi-structured interviews. Data analysis techniques are data reduction, data presentation, and drawing conclusions or verification. The results showed that the computational thinking process of students with low problem solving abilities only reached the decomposition stage because students were able to simplify the problem even though it was incomplete, but they were not able to connect mathematical concepts or materials to build a solution. Meanwhile, students with moderate and high problem solving abilities are limited to the pattern recognition stage because they can simplify problems and develop strategies, but make mistakes in using patterns, and there are incomplete steps. So it can be concluded that the computational thinking process of students with low problem solving abilities only reaches the decomposition stage. The computational thinking process of students with moderate and high problem solving abilities is limited to pattern recognition indicators.

References

Angeli, C., & Giannakos, M. (2020). Computational thinking education: Issues and challenges. Computers in Human Behavior, 105. https://doi.org/10.1016/j.chb.2019.106185

Città , G., Gentile, M., Allegra, M., Arrigo, M., Conti, D., Ottaviano, S., Reale, F., & Sciortino, M. (2019). The effects of mental rotation on computational thinking. Computers and Education, 141(June), 0–10. https://doi.org/10.1016/j.compedu.2019.103613

Gadanidis, G., Cendros, R., Floyd, L., & Namukasa, I. (2017). Computational thinking in mathematics teacher education. Contemporary Issues in Technology & Teacher Education, 17(4), 458–477.

Gog, T. Van, Hoogerheide, V., & Harsel, M. Van. (2020). The Role of Mental Effort in Fostering Self-Regulated Learning with Problem solving Tasks.

Halpern, D. F. (2014). Thought and Knowledge: An Introduction to Critical Thinking. (5th ed.). Psychology Press.

Jose, F. G.-P. and A. J. M. (2017). Computers in Human Behavior Exploring the computational thinking effects in pre-university education. 1–5. https://doi.org/10.1016/j.chb.2017.12.005

Lee, T. Y., Mauriello, M. L., Ahn, J., & Bederson, B. B. (2014). CTArcade: Computational Thinking with Games in School Age Children. International Journal of Child-Computer Interaction, 2(1), 26–33. https://doi.org/10.1016/j.ijcci.2014.06.003

Mathew, R., Malik, S. I., & Tawafak, R. M. (2019). Teaching Problem Solving Skills using an Educational Game in a Computer Programming Course. 18(2), 359–373. https://doi.org/10.15388/infedu.2019.17

OECD. (2013). PISA 2012 Assessment and Analytical Framework: Mathematics, Reading, Science, Problem Solving and Financial Literacy.

German: OECD Publishing.

OECD. (2014). PISA 2012 Results in Focus: What 15 Year Olds Know and Qhat They Can Do with What They Know. German: OECD Publishing.

Samo, D. D. (2017). Kemampuan Pemecahan Masalah Mahasiswa Tahun Pertama pada Masalah Geometri Konteks Budaya Problem Solving Ability of First Year University Student in Cultural Context Geometry Problem. 4(2), 141–152.

Seymour Papert. (1980). Papert_Mindstorms.Pdf.

Supiarmo, M. G. (2021). Defragmenting Student’s Thinking Structures in Solving Mathematical Problems on Pisa Model. (JIML) Journal of Innovative Mathematics Learning, 4(4), 167–177.

Supiarmo, M. G., Mardhiyatirrahmah, L., & Turmudi, T. (2021). Pemberian Scaffolding untuk Memperbaiki Proses Berpikir Komputasional Siswa dalam Memecahkan Masalah Matematika. Jurnal Cendekia: Jurnal Pendidikan Matematika, 5(1), 368-382.

Supiarmo, M. G., & Susanti, E. (2021). Proses Berpikir Komputasional Siswa dalam Menyelesaikan Soal Pisa Konten Change and Relationship Berdasarkan Self-Regulated Learning. Numeracy, 8(1), 58-72.

Tambunan, H. (2019). The Effectiveness of the Problem Solving Strategy and the Scientific Approach to Student’s Mathematical Capabilities in High Order Thinking Skills. International Electronic Journal of Mathematics Education, 14(2), 293–302.

Tedre, M., & Denning, P. J. (2016). The long quest for computational thinking. ACM International Conference Proceeding Series, 120–129. https://doi.org/10.1145/2999541.2999542

Tippmann, E., Scott, P. S., & Parker, A. (2017). Boundary Capabilities in MNCs : Knowledge Transformation for Creative Solution Development. Journal of Management Studies, June. https://doi.org/10.1111/joms.12253

Weintrop, D., Beheshti, E., Horn, M., Orton, K., Jona, K., Trouille, L., & Wilensky, U. (2016). Defining Computational Thinking for Mathematics and Science Classrooms. Journal of Science Education and Technology, 25(1), 127–147. https://doi.org/10.1007/s10956-015-9581-5

Wing, J. (2014). Computational thinking benefits society. Journal of Computing Sciences in Colleges, 24(6), 6–7. https://doi.org/10.1145/1227504.1227378

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

Downloads

Published

2022-02-10

Issue

Vol. 5 No. 1 (2022): VOLUME 5 NUMBER 1, MARCH 2022

Section

Articles

License

The author is responsible for acquiring the permission(s) to reproduce any copyrighted figures, tables, data, or text that are being used in the submitted paper. Authors should note that text quotations of more than 250 words from a published or copyrighted work will require grant of permission from the original publisher to reprint. The written permission letter(s) must be submitted together with the manuscript.