The Analyze Comparative of Physics Computational Thinking Skill (CTs) in Experiment Laboratory

Authors

  • Suritno Fayanto Department of Education Technology, State University of Malang, Malang, Indonesia
  • Sul Daeng Naba Department of Master in Physics Education, Yogyakarta State University, Yogyakarta, Indonesia
  • Aris Kurniawan Department of Master in Physics Education, Yogyakarta State University, Yogyakarta, Indonesia
  • Utami Putri Department of Master in Physics Education, Yogyakarta State University, Yogyakarta, Indonesia
  • Veronika Dua Padang Department of Master in Physics Education, Yogyakarta State University, Yogyakarta, Indonesia

DOI:

https://doi.org/10.48161/qaj.v4n3a699

Keywords:

computational thinking, abstraction, decomposition, algorithm thinking, evaluation, & generalization, experiment

Abstract

Objective: This study aimed to analyze students' response to the use of computational thinking from the perspective of computational tools and to analyze the influence of gender on students' computational thinking skills. Method: Research design using a comparative approach with data collection techniques involved a survey using a Likert scale questionnaire comprising 25 items, covering five dimensions of computational thinking skills: abstraction, decomposition, algorithm thinking, evaluation, and generalization. The study subjects involved five classes: physics, physics education, geography, mining engineering, and vocational-technical education, focusing on students' ability to analyze data using JASP and IBM SPSS. The data analyze methods included: (1). Comparative Analyze; (2). Correlation analyzes (Spearman); (3). Chi-square test. Finding: The results showed that the computational thinking skills of students from various classes varied, with significant correlations between the skill dimensions. Physics and Physics Education stood out with exemplary achievements, while Geography and Mining Engineering also showed good progress. The vocational-technical education program displayed nearly perfect correlations in all aspects of computational thinking skills. Meanwhile, from the gender aspect, gender significantly influenced computational thinking skills (Sig<0.00). The analyze highlighted the differences in computational thinking skills between classes and the significant influence of gender. Implication: This emphasized the importance of developing computational thinking skills in higher education and the need for inclusive approaches to enhance computational excellence among students. The implications of this study give valuable insights for improving the teaching of computational thinking in physics education. Steps that might be addressed include identifying and enhancing weak components, such as abstraction and generalization, and using particular tactics to increase students' knowledge.

Downloads

Download data is not yet available.

Author Biographies

Sul Daeng Naba, Department of Master in Physics Education, Yogyakarta State University, Yogyakarta, Indonesia

Department of Master in Physics Education

Aris Kurniawan, Department of Master in Physics Education, Yogyakarta State University, Yogyakarta, Indonesia

Department of Master in Physics Education

Utami Putri, Department of Master in Physics Education, Yogyakarta State University, Yogyakarta, Indonesia

Department of Master in Physics Education

Veronika Dua Padang, Department of Master in Physics Education, Yogyakarta State University, Yogyakarta, Indonesia

Department of Master in Physics Education

References

J. Nouri, L. Zhang, L. Mannila, and E. Norén, “Development of computational thinking, digital competence and 21st century skills when learning programming in K-9,” Educ. Inq., vol. 11, no. 1, 2020, doi: 10.1080/20004508.2019.1627844.

A. Yadav, C. Mayfield, N. Zhou, S. Hambrusch, and J. T. Korb, “Computational Thinking in Elementary and Secondary Teacher Education,” ACM Trans. Comput. Educ., vol. 14, no. 1, pp. 1–16, Mar. 2014, doi: 10.1145/2576872.

S. Grover and R. Pea, “Computational Thinking in K-12: A Review of the State of the Field,” Educational Researcher, vol. 42, no. 1. 2013. doi: 10.3102/0013189X12463051.

M. N. O. Sadiku, A. E. Shadare, and S. M. Musa, “Computational Physics: An Introduction,” Int. J. Eng. Res., vol. 6, no. 9, p. 427, 2017, doi: 10.5958/2319-6890.2017.00054.X.

T. O. B. Odden, E. Lockwood, and M. D. Caballero, “Physics computational literacy: An exploratory case study using computational essays,” Phys. Rev. Phys. Educ. Res., vol. 15, no. 2, p. 020152, Dec. 2019, doi: 10.1103/PhysRevPhysEducRes.15.020152.

R. D. Handayani, A. D. Lesmono, S. B. Prastowo, B. Supriadi, and N. M. Dewi, “Bringing Computational Thinking Skills Into Physics Classroom Through Project-Based Learning,” in 2022 8th International Conference on Education and Technology (ICET), Oct. 2022, pp. 76–80. doi: 10.1109/ICET56879.2022.9990631.

E. Bufasi, M. Hoxha, K. Cuka, and S. Vrtagic, “Developing Student’s Comprehensive Knowledge of Physics Concepts by Using Computational Thinking Activities: Effects of a 6-Week Intervention,” Int. J. Emerg. Technol. Learn., vol. 17, no. 18, pp. 161–176, Sep. 2022, doi: 10.3991/ijet.v17i18.31743.

N. D. Anderson, “A Call for Computational Thinking in Undergraduate Psychology,” Psychol. Learn. Teach., vol. 15, no. 3, pp. 226–234, Nov. 2016, doi: 10.1177/1475725716659252.

I. Kumar and N. Mohd, “Ways of Using Computational Thinking to Improve Students’ Ability to Think Critically,” in Infrastructure Possibilities and Human-Centered Approaches With Industry 5.0, 2024, pp. 253–266. doi: 10.4018/979-8-3693-0782-3.ch015.

N. D. Saidin, F. Khalid, R. Martin, Y. Kuppusamy, and N. A. Munusamy, “Benefits and Challenges of Applying Computational Thinking in Education,” Int. J. Inf. Educ. Technol., vol. 11, no. 5, pp. 248–254, 2021, doi: 10.18178/ijiet.2021.11.5.1519.

J. Pérez, J. Castro, and O. Pedroza, “Shortcomings in the evaluation of computational thinking,” Rev. Filos., vol. 38, no. 99, 2021, doi: 10.5281/zenodo.5651282.

T. A. Tene-Tenempaguay, A. García-Holgado, F. J. García-Peñalvo, and J. P. Hernández-Ramos, “An Overview of European Projects About Computational Thinking,” in Lecture Notes in Educational Technology, 2023, pp. 60–74. doi: 10.1007/978-981-99-0942-1_6.

“An Assessment of the Mobile Games Utilization and It’s Effect to One’s Computational Thinking Skills,” Int. J. Innov. Technol. Explor. Eng., vol. 8, no. 9S2, pp. 548–552, Aug. 2019, doi: 10.35940/ijitee.I1115.0789S219.

A. F. Farias and D. Augusto Couto Barone, “Computational thinking through an online game to develop soft and hard skills,” in EAEEIE 2023 - Proceedings of the 2023 32nd Annual Conference of the European Association for Education in Electrical and Information Engineering, 2023. doi: 10.23919/EAEEIE55804.2023.10181711.

D. S. Rana, S. C. Dimri, P. Malik, and S. A. Dhondiyal, “Impact of Computational Thinking in Engineering and K12 Education,” in 4th International Conference on Inventive Research in Computing Applications, ICIRCA 2022 - Proceedings, 2022. doi: 10.1109/ICIRCA54612.2022.9985593.

B. H. Majeed, L. F. Jawad, and H. T. S. ALRikabi, “Computational Thinking (CT) Among University Students,” Int. J. Interact. Mob. Technol., vol. 16, no. 10, 2022, doi: 10.3991/ijim.v16i10.30043.

K. S. Ting, O. Talib, A. F. Mohd Ayub, M. Zolkepli, C. C. Yee, and T. C. Hoong, “Word Problems as a Vehicle for Teaching Computational Thinking,” Int. J. Acad. Res. Progress. Educ. Dev., vol. 12, no. 1, 2023, doi: 10.6007/ijarped/v12-i1/16543.

C. F. Hu and C. C. Wu, “A Computational Thinking Test for Senior High School Students,” in Annual Conference on Innovation and Technology in Computer Science Education, ITiCSE, 2023, vol. 2. doi: 10.1145/3587103.3594151.

S. Yadav and P. Chakraborty, “Introducing schoolchildren to computational thinking using smartphone apps: A way to encourage enrollment in engineering education,” Comput. Appl. Eng. Educ., vol. 31, no. 4, 2023, doi: 10.1002/cae.22609.

A. De Santo et al., “Promoting Computational Thinking Skills in Non-Computer-Science Students: Gamifying Computational Notebooks to Increase Student Engagement,” IEEE Trans. Learn. Technol., vol. 15, no. 3, pp. 392–405, 2022, doi: 10.1109/TLT.2022.3180588.

T. T. Yuen and K. A. Robbins, “A qualitative study of students’ computational thinking skills in a data-driven computing class,” ACM Trans. Comput. Educ., vol. 14, no. 4, 2014, doi: 10.1145/2676660.

P. Sengupta, J. S. Kinnebrew, S. Basu, G. Biswas, and D. Clark, “Integrating computational thinking with K-12 science education using agent-based computation: A theoretical framework,” Educ. Inf. Technol., vol. 18, no. 2, pp. 351–380, Jun. 2013, doi: 10.1007/s10639-012-9240-x.

V. J. Shute, C. Sun, and J. Asbell-Clarke, “Demystifying computational thinking,” Educational Research Review, vol. 22. 2017. doi: 10.1016/j.edurev.2017.09.003.

H. P. Liu, S. M. Perera, and J. W. Klein, “Using Model-Based Learning to Promote Computational Thinking Education,” in Emerging Research, Practice, and Policy on Computational Thinking, 2017. doi: 10.1007/978-3-319-52691-1_10.

D. Weintrop et al., “Defining Computational Thinking for Mathematics and Science Classrooms,” J. Sci. Educ. Technol., vol. 25, no. 1, pp. 127–147, 2016, doi: 10.1007/s10956-015-9581-5.

N. Shin et al., “A framework for supporting systems thinking and computational thinking through constructing models,” Instructional Science, vol. 50, no. 6. 2022. doi: 10.1007/s11251-022-09590-9.

F. Falah and D. Sulisworo, “Enhancing Computational Thinking Skills Through Physics-Based Worksheet in Linear Motion,” Radiasi J. Berk. Pendidik. Fis., vol. 16, no. 1, pp. 1–8, Apr. 2023, doi: 10.37729/radiasi.v16i1.2133.

Herwinarso, E. Pratidhina, P. Adam, H. Kuswanto, and A. D. Rahmat, “Investigation of science process skills and computational thinking dispositions during the implementation of collaborative modeling-based learning in high school physics class,” J. Educ. e-Learning Res., vol. 10, no. 4, pp. 753–760, Dec. 2023, doi: 10.20448/jeelr.v10i4.5200.

A. Akmam, R. Anshari, N. Jalinus, and A. Amran, “Factors influencing the critical and creative thinking skills of college students in computational physics courses,” in Journal of Physics: Conference Series, 2019, vol. 1317, no. 1. doi: 10.1088/1742-6596/1317/1/012172.

A. Saad, “Students’ computational thinking skill through cooperative learning based on hands-on, inquiry-based, and student-centric learning approaches,” Univers. J. Educ. Res., vol. 8, no. 1, 2020, doi: 10.13189/ujer.2020.080135.

H. W. Fennell, J. A. Lyon, A. J. Magana, S. Rebello, C. M. Rebello, and Y. B. Peidrahita, “Designing hybrid physics labs: combining simulation and experiment for teaching computational thinking in first-year engineering,” in 2019 IEEE Frontiers in Education Conference (FIE), Oct. 2019, pp. 1–8. doi: 10.1109/FIE43999.2019.9028390.

A. J. Magana and G. Silva Coutinho, “Modeling and simulation practices for a computational thinking‐enabled engineering workforce,” Comput. Appl. Eng. Educ., vol. 25, no. 1, pp. 62–78, Jan. 2017, doi: 10.1002/cae.21779.

L. Rakhmawati, A. I. Agung, and M. Rohman, “Virtual Laboratory-Based Student Worksheets Development for Computational Thinking Practices,” in 2022 Fifth International Conference on Vocational Education and Electrical Engineering (ICVEE), Sep. 2022, pp. 221–225. doi: 10.1109/ICVEE57061.2022.9930462.

Z. Liu and J. Xia, “Enhancing computational thinking in undergraduate engineering courses using model‐eliciting activities,” Comput. Appl. Eng. Educ., vol. 29, no. 1, pp. 102–113, Jan. 2021, doi: 10.1002/cae.22357.

E. Bufasi, M. Hoxha, K. Cuka, and S. Vrtagic, “Developing Student’s Comprehensive Knowledge of Physics Concepts by Using Computational Thinking Activities: Effects of a 6-Week Intervention,” Int. J. Emerg. Technol. Learn., vol. 17, no. 18, 2022, doi: 10.3991/ijet.v17i18.31743.

A. Jimoyiannis and V. Komis, “Computer simulations in physics teaching and learning : a case study on students ’ understanding of trajectory motion,” vol. 36, pp. 183–204, 2001.

N. D. Finkelstein et al., “When learning about the real world is better done virtually: A study of substituting computer simulations for laboratory equipment,” Phys. Rev. Spec. Top. - Phys. Educ. Res., vol. 1, no. 1, 2005, doi: 10.1103/PhysRevSTPER.1.010103.

V. Potkonjak et al., “Virtual laboratories for education in science, technology, and engineering: A review,” Comput. Educ., vol. 95, 2016, doi: 10.1016/j.compedu.2016.02.002.

E. Bufasi, M. Hoxha, and S. Vrtagic, “using computational modeling in physics: introduction to projectile motion,” conference: 7th international symposium bosnia and herzegovina, 2021. https://www.researchgate.net/publication/360603773_using_computational_modeling_in_physics_introduction_to_projectile_motion (accessed Feb. 09, 2023).

C. M. Orban and R. M. Teeling-Smith, “Computational Thinking in Introductory Physics,” Phys. Teach., vol. 58, no. 4, 2020, doi: 10.1119/1.5145470.

D. P. Weller, T. E. Bott, M. D. Caballero, and P. W. Irving, “Developing a learning goal framework for computational thinking in computationally integrated physics classrooms,” arxiv physics Cornel University, 2021. http://arxiv.org/abs/2105.07981 (accessed Feb. 09, 2024).

D. P. Weller, T. E. Bott, M. D. Caballero, and P. W. Irving, “Development and illustration of a framework for computational thinking practices in introductory physics,” Phys. Rev. Phys. Educ. Res., vol. 18, no. 2, 2022, doi: 10.1103/PhysRevPhysEducRes.18.020106.

P. C. Hamerski, D. Mcpadden, M. D. Caballero, and P. W. Irving, “Students ’ perspectives on computational challenges in physics class,” Phys. Rev. Phys. Educ. Res., vol. 18, no. 2, p. 20109, 2022, doi: 10.1103/PhysRevPhysEducRes.18.020109.

Y. Dori and J. Belcher, “Learning electromagnetism with visualizations and active learning visualization in science education,” Vis. Sci. Educ., vol. 1, 2005.

F. Khusnul, M. Nasir, and A. Azhar, “Optics Visualization Web-Based as A Physics Learning Media in Senior High School,” J. Educ. Sci., vol. 6, no. 1, 2022, doi: 10.31258/jes.6.1.p.188-199.

S. Gross, M. Kim, J. Schlosser, C. Mohtadi, D. Lluch, and D. Schneider, “Fostering computational thinking in engineering education: Challenges, examples, and best practices,” in IEEE Global Engineering Education Conference, EDUCON, 2014. doi: 10.1109/educon.2014.6826132.

H. Montiel and M. G. Gomez-Zermeño, “Educational challenges for computational thinking in k–12 education: A systematic literature review of ‘scratch’ as an innovative programming tool,” Computers, vol. 10, no. 6, 2021, doi: 10.3390/computers10060069.

F. Buitrago-Flórez, G. Danies, S. Restrepo, and C. Hernández, “Fostering 21st century competences through computational thinking and active learning: A mixed method study,” International Journal of Instruction, vol. 14, no. 3. 2021. doi: 10.29333/iji.2021.14343a.

X. P. Voon, S. L. Wong, L. H. Wong, M. N. M. Khambari, and S. I. S. Syed-Abdullah, “Developing Computational Thinking Competencies through Constructivist Argumentation Learning: A Problem-Solving Perspective,” Int. J. Inf. Educ. Technol., vol. 12, no. 6, 2022, doi: 10.18178/ijiet.2022.12.6.1650.

N. I. Zakaria and Z. H. Iksan, “Computational thinking among high school students,” Univers. J. Educ. Res., vol. 8, no. 11 A, 2020, doi: 10.13189/ujer.2020.082102.

C. Martino Otero Avila, L. Foss, A. Bordini, M. Simone Debacco, and S. Andre Da Costa Cavalheiro, “Evaluation rubric for computational thinking concepts,” Proc. - IEEE 19th Int. Conf. Adv. Learn. Technol. ICALT 2019, pp. 279–281, 2019, doi: 10.1109/ICALT.2019.00089.

V. J. Shute, C. Sun, and J. Asbell-Clarke, “Demystifying computational thinking,” Educ. Res. Rev., vol. 22, no. November (11), pp. 142–158, Nov. 2017, doi: 10.1016/j.edurev.2017.09.003.

A. R. López and F. J. García-Peñalvo, “Relationship of knowledge to learn in programming methodology and evaluation of computational thinking,” in Proceedings of the Fourth International Conference on Technological Ecosystems for Enhancing Multiculturality, Nov. 2016, pp. 73–77. doi: 10.1145/3012430.3012499.

M. J. Tsai, J. C. Liang, S. W. Y. Lee, and C. Y. Hsu, “Structural Validation for the Developmental Model of Computational Thinking,” J. Educ. Comput. Res., vol. 60, no. 1, 2022, doi: 10.1177/07356331211017794.

E. KILIÇOĞLU and A. KAPLAN, “An Examination of Middle School 7th Grade Students’ Mathematical Abstraction Processes,” J. Comput. Educ. Res., vol. 7, no. 13, 2019, doi: 10.18009/jcer.547975.

C. Izu, “Modelling the Use of Abstraction in Algorithmic Problem Solving,” in Annual Conference on Innovation and Technology in Computer Science Education, ITiCSE, 2022, vol. 1. doi: 10.1145/3502718.3524758.

A. Khanfor, “Tasks Decomposition Approaches in Crowdsourcing Software Development,” in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2023, vol. 14016 LNCS. doi: 10.1007/978-3-031-35129-7_35.

D. Gonda, V. Ďuriš, A. Tirpáková, and G. Pavlovičová, “Teaching Algorithms to Develop the Algorithmic Thinking of Informatics Students,” Mathematics, vol. 10, no. 20, 2022, doi: 10.3390/math10203857.

M. M. Syslo, “From Algorithmic to Computational Thinking: On the Way for Computing for All Students,” Proc. 2015 ACM Conf. Innov. Technol. Comput. Sci. Educ., 2015.

Y. Caballero-Gonzalez, “Computational Thinking, a Discipline to Enhance Digital Skills Such as Programming,” in Proceedings - 2022 8th International Engineering, Sciences and Technology Conference, IESTEC 2022, 2022. doi: 10.1109/IESTEC54539.2022.00061.

R. Zakwandi and E. Istiyono, “A framework for assessing computational thinking skills in the physics classroom : study on cognitive test development,” SN Soc. Sci., vol. 3, no. 3, pp. 1–15, 2023, doi: 10.1007/s43545-023-00633-7.

J. Gambrell and E. Brewe, “Towards a Generalized Assessment of Computational Thinking for Introductory Physics Students,” Philadelphia, 2023. [Online]. Available: http://arxiv.org/abs/2308.03593

A. Fauzi and S. H. Zahroh, “Opportunities and challenges of implementing computational thinking through unplugged activities in physics learning,” Gravity, vol. 9, no. 2, pp. 96–104, 2023, doi: 10.30870/gravity.v9i2.17456.

Y. I. Tanjung et al., “State of The Art Review: Building Computational Thinking on Science Education,” J. Pendidik. Fis. Indones., vol. 19, no. 1, 2023, doi: 10.15294/jpfi.v19i1.41745.

E. E. E. Espino and C. S. G. González, “Influence of gender on computational thinking,” in ACM International Conference Proceeding Series, 2015, vol. 07-09-September-2015. doi: 10.1145/2829875.2829904.

R. Niousha, D. Saito, H. Washizaki, and Y. Fukazawa, “Investigating the Effect of Binary Gender Preferences on Computational Thinking Skills,” Educ. Sci., vol. 13, no. 5, 2023, doi: 10.3390/educsci13050433.

P. J. Denning and M. Tedre, Computational Thinking. The MIT Press, 2019. doi: 10.7551/mitpress/11740.001.0001.

J. M. Wing, “Computational thinking,” in 2011 IEEE Symposium on Visual Languages and Human-Centric Computing (VL/HCC), Sep. 2011, pp. 3–3. doi: 10.1109/VLHCC.2011.6070404.

J. M. Wing, “Computational thinking’s influence on research and education for all,” Ital. J. Educ. Technol., vol. 25, no. 2, 2017.

P. Sengupta, J. S. Kinnebrew, S. Basu, G. Biswas, and D. Clark, “Integrating computational thinking with K-12 science education using agent-based computation: A theoretical framework,” Educ. Inf. Technol., vol. 18, no. 2, pp. 351–380, 2013, doi: 10.1007/s10639-012-9240-x.

S. K. Reed, “Computational and Mathematical Thinking,” in Cognitive Skills You Need for the 21st Century, Oxford University Press, 2020, pp. 221–231. doi: 10.1093/oso/9780197529003.003.0019.

C. K. Chang, “Integrate social simulation content with game designing curriculum to foster computational thinking,” in Proceedings - 7th International Conference on Digital Content, Multimedia Technology and Its Applications, IDCTA 2011, 2011.

M. Aydeniz, “Integrating Computational Thinking in School Curriculum,” in Computational Thinking in the STEM Disciplines, Cham: Springer International Publishing, 2018, pp. 253–277. doi: 10.1007/978-3-319-93566-9_13.

J. M. Wing, “Computational thinking and thinking about computing,” Philos. Trans. R. Soc. A Math. Phys. Eng. Sci., vol. 366, no. 1881, pp. 3717–3725, Oct. 2008, doi: 10.1098/rsta.2008.0118.

S. W.-Y. Lee, H.-Y. Tu, G.-L. Chen, and H.-M. Lin, “Exploring the multifaceted roles of mathematics learning in predicting students’ computational thinking competency,” Int. J. STEM Educ., vol. 10, no. 1, p. 64, Nov. 2023, doi: 10.1186/s40594-023-00455-2.

N. M. Hutchins et al., “C2STEM: a System for Synergistic Learning of Physics and Computational Thinking,” J. Sci. Educ. Technol., vol. 29, no. 1, pp. 83–100, 2020, doi: 10.1007/s10956-019-09804-9.

H. Maulina, A. Abdurrahman, and I. Sukamto, “How to Bring Computational Thinking Approach to The Non-Computer Science Student’s Class???,” J. Pembelajaran Fis., vol. 9, no. 1, pp. 101–112, Jun. 2019, doi: 10.23960/jpf.v9.n1.202109.

H. Ma, M. Zhao, H. Wang, X. Wan, T. W. Cavanaugh, and J. Liu, “Promoting pupils’ computational thinking skills and self-efficacy: a problem-solving instructional approach,” Educ. Technol. Res. Dev., vol. 69, no. 3, pp. 1599–1616, 2021, doi: 10.1007/s11423-021-10016-5.

T.-T. Wu and J.-M. Chen, “Combining Webduino Programming With Situated Learning to Promote Computational Thinking, Motivation, and Satisfaction Among High School Students,” J. Educ. Comput. Res., vol. 60, no. 3, pp. 631–660, Jun. 2022, doi: 10.1177/07356331211039961.

U. Pratiwi and D. Nanto, “Students’ Strategic Thinking Ability Enhancement in Applying Scratch for Arduino of Block Programming in Computational Physics Lecture,” J. Penelit. Pengemb. Pendidik. Fis., vol. 5, no. 2, pp. 193–202, Dec. 2019, doi: 10.21009/1.05215.

X. Li, K. Xie, V. Vongkulluksn, D. Stein, and Y. Zhang, “Developing and Testing a Design-Based Learning Approach to Enhance Elementary Students’ Self-Perceived Computational Thinking,” J. Res. Technol. Educ., vol. 55, no. 2, pp. 344–368, 2023, doi: 10.1080/15391523.2021.1962453.

G. Stoet and D. C. Geary, “The Gender-Equality Paradox in Science, Technology, Engineering, and Mathematics Education,” Psychol. Sci., vol. 29, no. 4, pp. 581–593, Apr. 2018, doi: 10.1177/0956797617741719.

A. Yadav, C. Stephenson, and H. Hong, “Computational thinking for teacher education,” Commun. ACM, vol. 60, no. 4, pp. 55–62, 2017, doi: 10.1145/2994591.

K. Brennan and M. Resnick, “New frameworks for studying and assessing the development of computational thinking,” Annu. Am. Educ. Res. Assoc. Meet. Vancouver, BC, Canada, 2012.

A. Repenning, A. Basawapatna, and N. Escherle, “Computational thinking tools,” in 2016 IEEE Symposium on Visual Languages and Human-Centric Computing (VL/HCC), Sep. 2016, pp. 218–222. doi: 10.1109/VLHCC.2016.7739688.

S. Atmatzidou and S. Demetriadis, “Advancing students’ computational thinking skills through educational robotics: A study on age and gender relevant differences,” Rob. Auton. Syst., vol. 75, pp. 661–670, 2016, doi: 10.1016/j.robot.2015.10.008.

C. Angeli and M. Giannakos, “Computational thinking education: Issues and challenges,” Comput. Human Behav., vol. 105, p. 106185, Apr. 2020, doi: 10.1016/j.chb.2019.106185.

C. P. Brackmann, M. Román-González, G. Robles, J. Moreno-León, A. Casali, and D. Barone, “Development of Computational Thinking Skills through Unplugged Activities in Primary School,” in Proceedings of the 12th Workshop on Primary and Secondary Computing Education, Nov. 2017, pp. 65–72. doi: 10.1145/3137065.3137069.

M. J. Tsai, J. C. Liang, and C. Y. Hsu, “The Computational Thinking Scale for Computer Literacy Education,” J. Educ. Comput. Res., vol. 59, no. 4, 2021, doi: 10.1177/0735633120972356.

G. Merkys and D. Bubeliene, “Optimization of Data Processing and Presentation in Social Surveys: From Likert-Means to ‘Yes Percentage,’” in Modeling and Simulation of Social-Behavioral Phenomena in Creative Societies, 2019, pp. 12–25. doi: 10.1007/978-3-030-29862-3_2.

A. J. Bishara and J. B. Hittner, “Testing the significance of a correlation with nonnormal data: Comparison of Pearson, Spearman, transformation, and resampling approaches.,” Psychol. Methods, vol. 17, no. 3, pp. 399–417, Sep. 2012, doi: 10.1037/a0028087.

A. J. Bishara and J. B. Hittner, “Reducing Bias and Error in the Correlation Coefficient Due to Nonnormality,” Educ. Psychol. Meas., vol. 75, no. 5, pp. 785–804, Oct. 2015, doi: 10.1177/0013164414557639.

A. J. Bishara and J. B. Hittner, “Confidence intervals for correlations when data are not normal,” Behav. Res. Methods, vol. 49, no. 1, pp. 294–309, Feb. 2017, doi: 10.3758/s13428-016-0702-8.

D. A. Powell and W. D. Schafer, “The Robustness of the Likelihood Ratio Chi-Square Test for Structural Equation Models: A Meta-Analysis,” J. Educ. Behav. Stat., vol. 26, no. 1, pp. 105–132, Mar. 2001, doi: 10.3102/10769986026001105.

T. M. Franke, T. Ho, and C. A. Christie, “The Chi-Square Test,” Am. J. Eval., vol. 33, no. 3, pp. 448–458, Sep. 2012, doi: 10.1177/1098214011426594.

G. Pavlov, D. Shi, and A. Maydeu-Olivares, “Chi-square Difference Tests for Comparing Nested Models: An Evaluation with Non-normal Data,” Struct. Equ. Model. A Multidiscip. J., vol. 27, no. 6, pp. 908–917, Nov. 2020, doi: 10.1080/10705511.2020.1717957.

Emzir, Metodologi Penelitian Pendidikan Kuantitatif dan Kualitatatif [Quantitative and Qualitative Education Research Methodology]. Jakarta: Raja Grafindo Persada, 2009.

B. C. Czerkawski and E. W. Lyman, “Exploring Issues About Computational Thinking in Higher Education,” TechTrends, vol. 59, no. 2, pp. 57–65, Mar. 2015, doi: 10.1007/s11528-015-0840-3.

N. V. Mendoza Diaz, R. Meier, D. A. Trytten, and S. Yoon Yoon, “Computational Thinking Growth During a First-Year Engineering Course,” in 2020 IEEE Frontiers in Education Conference (FIE), Oct. 2020, pp. 1–7. doi: 10.1109/FIE44824.2020.9274250.

D. Vasileska and S. M. Goodnick, “Computational electronics,” Synth. Lect. Comput. Electromagn., vol. 6, 2006, doi: 10.2200/S00026ED1V01Y200605CEM006.

J. F. Sanford, “Core concepts of computational thinking,” Int. J. Teach. Case Stud., vol. 4, no. 1, 2013, doi: 10.1504/ijtcs.2013.053383.

O. Yaşar, “The Essence of Computational Thinking,” Comput. Sci. Eng., vol. 19, no. 4, 2017, doi: 10.1109/MCSE.2017.3151241.

F. K. Cansu and S. K. Cansu, “An Overview of Computational Thinking,” Int. J. Comput. Sci. Educ. Sch., vol. 3, no. 1, pp. 17–30, 2019, doi: 10.21585/ijcses.v3i1.53.

A. Juškevičienė, “Developing Algorithmic Thinking Through Computational Making,” in Data Science: New Issues, Challenges and Applications, Springer, 2020, pp. 183–197. doi: 10.1007/978-3-030-39250-5_10.

P. MIHCI Türker and F. K. Pala, “The Effect of Algorithm Education on Students’ Computer Programming Self-Efficacy Perceptions and Computational Thinking Skills,” Int. J. Comput. Sci. Educ. Sch., vol. 3, no. 3, pp. 19–32, Jan. 2020, doi: 10.21585/ijcses.v3i3.69.

M. J. Tsai, J. C. Liang, and C. Y. Hsu, “The Computational Thinking Scale for Computer Literacy Education,” J. Educ. Comput. Res., vol. 59, no. 4, pp. 579–602, 2021, doi: 10.1177/0735633120972356.

S. W. Chan, C. K. Looi, and B. Sumintono, “Assessing computational thinking abilities among Singapore secondary students: a Rasch model measurement analysis,” J. Comput. Educ., vol. 8, no. 2, 2021, doi: 10.1007/s40692-020-00177-2.

L. S. Danindra, Masriyah, and U. Hanifah, “Computational Thinking Processes of Junior High School Students in Solving Problems of Number Patterns in Terms of Gender Differences,” SHS Web Conf., vol. 149, 2022, doi: 10.1051/shsconf/202214901012.

C. J. Park and J. S. Hyun, “Gender and abstract thinking disposition difference- Analyses of visual diagram structuring for computational thinking ability,” J. Korean Assoc. Comput. Educ., vol. 21, no. 3, 2018.

R. Paucar-Curasma et al., “Development of Computational Thinking through STEM Activities for the Promotion of Gender Equality,” Sustain., vol. 15, no. 16, 2023, doi: 10.3390/su151612335.

Published

2024-07-07

How to Cite

Fayanto, S., Naba, S. D., Kurniawan, A., Putri, U., & Padang, V. D. (2024). The Analyze Comparative of Physics Computational Thinking Skill (CTs) in Experiment Laboratory . Qubahan Academic Journal, 4(3), 13–32. https://doi.org/10.48161/qaj.v4n3a699

Issue

Section

Articles