Difficult Concepts and Practices of Computational Thinking Using Block-based Programming
Keywords:
computational thinking, CT difficulties, CT challenges, block-based programming, ScratchAbstract
To help novice learners overcome the obstacles of learning computational thinking (CT) through programming, it is vital to identify difficult CT components. This study aimed to determine the computational concepts and practices that learners may have difficulties acquiring and discuss how programming instructions should be designed to facilitate learning CT in online learning environments. Participants included 92 undergraduate students enrolled in an online course. Data were collected from a CT knowledge test and coding journals. Results revealed that four computational concepts (i.e., parallelism, conditionals, data, and operators) and two computational practices (i.e., testing and debugging and abstracting and modularizing) were identified as CT components that were difficult to learn. The findings of this study imply that CT instructions should offer additional instructional supports to enhance the mastery of difficult computational concepts and practices. Further research is necessary to investigate instructional approaches to successful CT learning.
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References
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Kroesbergen, E. H., Van Luit, J. E., & Maas, C. J. (2004). Effectiveness of explicit and constructivist mathematics instruction for low-achieving students in the Netherlands. The Elementary School Journal, 104(3), 233-251. https://doi.org/10.1086/499751
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Jenkins, C. (2015). A work in progress paper: Evaluating a microworlds-based learning approach for developing literacy and computational thinking in cross-curricular contexts. In Proceedings of the Workshop in Primary and Secondary Computing Education, 61-64. ACM. https://doi.org/10.1145/2818314.2818316
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McCauley, R., Fitzgerald, S., Lewandowski, G., Murphy, L., Simon, B., Thomas, L., & Zander, C. (2008). Debugging: a review of the literature from an educational perspective. Computer Science Education, 18(2), 67-92. https://doi.org/10.1080/08993400802114581
Miles, M. B., Huberman, A. M., Huberman, M. A., & Huberman, M. (1994). Qualitative data analysis: An expanded sourcebook. Sage publications. https://doi.org/10.1016/0149-7189(96)88232-2
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Mühling, A., Ruf, A., & Hubwieser, P. (2015). Design and first results of a psychometric test for measuring basic programming abilities. In Proceedings of the workshop in primary and secondary computing education, 2-10. ACM. https://doi.org/10.1145/2818314.2818320
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Papert, S. (1980). Mindstorms: Children, computers and powerful ideas. New York: Basic Books. https://doi.org/10.5555/1095592
Resnick, M., Maloney, J., Monroy-Hernández, A., Rusk, N., Eastmond, E., Brennan, K., ... & Kafai, Y. B. (2009). Scratch: Programming for all. Communications of the ACM, 52(11), 60-67. https://web.media.mit.edu/~mres/papers/Scratch-CACM-final.pdf
Román-González, M. (2015). Computational thinking test: Design guidelines and content validation. In Proceedings of EDULEARN15 Conference, 2436-2444. https://doi.org/10.13140/RG.2.1.4203.4329
Román-González, M., Moreno-León, J., & Robles, G. (2017). Complementary tools for computational thinking assessment. In Proceedings of International Conference on Computational Thinking Education, S. C Kong, J Sheldon, and K. Y Li (Eds.). The Education University of Hong Kong, 154-159. https://doi.org/10.1007/978-981-13-6528-7_6
Román-González, M., Pérez-González, J. C., Moreno-León, J., & Robles, G. (2016). Does computational thinking correlate with personality?: The non-cognitive side of computational thinking. In Proceedings of the Fourth International Conference on Technological Ecosystems for Enhancing Multiculturality, 51-58. ACM. https://doi.org/10.1145/3012430.3012496
Romero, M., Lepage, A., & Lille, B. (2017). Computational thinking development through creative programming in higher education. International Journal of Educational Technology in Higher Education, 14(1), 42. https://doi.org/10.1186/s41239-017-0080-z
Rupley, W. H., Blair, T. R., & Nichols, W. D. (2009). Effective reading instruction for struggling readers: The role of direct/explicit teaching. Reading & Writing Quarterly, 25(2-3), 125-138. https://doi.org/10.1080/10573560802683523
Seiter, L., & Foreman, B. (2013, August). Modeling the learning progressions of computational thinking of primary grade students. In Proceedings of the ninth annual international ACM conference on International computing education research, 59-66. ACM. https://doi.org/10.1145/2493394.2493403
Sentance, S., & Csizmadia, A. (2015). Teachers' perspectives on successful strategies for teaching computing in school. In IFIP TCS. https://www.researchgate.net/profile/Sue-Sentance/publication/301525438_Teachers%27_perspectives_on_successful_strategies_for_teaching_Computing_in_school/links/57176e3708ae2679a8c76745/Teachers-perspectives-on-successful-strategies-for-teaching-Computing-in-school.pdf
Shepard, L. A. (2000). The role of assessment in a learning culture. Educational researcher, 29(7), 4-14. https://doi.org/10.3102/0013189X029007004
Shute, V. J., Sun, C., & Asbell-Clarke, J. (2017). Demystifying computational thinking. Educational Research Review, 22, 142-158. https://doi.org/10.1016/j.edurev.2017.09.003
Stanford, P., Crowe, M. W., & Flice, H. (2010). Differentiating with technology. TEACHING exceptional children plus, 6(4), 4. https://files.eric.ed.gov/fulltext/EJ907030.pdf
Teddlie, C., & Tashakkori, A. (2003). Major issues and controversies in the use of mixed methods in the social and behvioral sciences. Handbook of mixed methods in social & behavioral research, 3-50. https://doi.org/10.4135/9781506335193
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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 (TOCE), 14(1), 5. https://doi.org/10.1145/2576872
Zhong, B., Wang, Q., Chen, J., & Li, Y. (2016). An exploration of three-dimensional integrated assessment for computational thinking. Journal of Educational Computing Research, 53(4), 562-590. https://doi.org/10.1177/0735633115608444
Aho, A. V. (2012). Computation and computational thinking. The Computer Journal, 55(7), 832-835. https://doi.org/10.1093/comjnl/bxs074
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
Barr, D., Harrison, J., & Conery, L. (2011). Computational thinking: A digital age skill for everyone. Learning & Leading with Technology, 38(6), 20-23. https://edtechbooks.org/-HQ
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?. Inroads, 2(1), 48-54. https://doi.org/10.1145/1929887.1929905
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
Black, P., & Wiliam, D. (1998). Assessment and classroom learning. Assessment in Education: principles, policy & practice, 5(1), 7-74. https://doi.org/10.1080/0969595980050102
Brackmann, C. P., Román-González, M., Robles, G., Moreno-León, J., Casali, A., & Barone, D. (2017, November). Development of computational thinking skills through unplugged activities in primary school. In Proceedings of the 12th Workshop on Primary and Secondary Computing Education (pp. 65-72). https://doi.org/10.1145/3137065.3137069
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, Vancouver, Canada, 1, 25. https://www.media.mit.edu/publications/new-frameworks-for-studying-and-assessing-the-development-of-computational-thinking
Caeli, E. N., & Yadav, A. (2020). Unplugged approaches to computational thinking: A historical perspective. TechTrends, 64(1), 29-36. https://doi.org/10.1007/s11528-019-00410-5
Cetin, I. (2016). Preservice teachers' introduction to computing: Exploring utilization of scratch. Journal of Educational Computing Research, 54(7), 997-1021. https://doi.org/10.1177/0735633116642774
Creswell, J. W., & Clark, V. L. P. (2017). Designing and conducting mixed methods research. Sage publications.
CSTA & ISTE. (2011). Operational definition of computational thinking for k-12 education. http://csta.acm.org/curriculum/sub/currfiles/compthinkingflyer.pdf
Czerkawski, B. C., & Lyman, E. W. (2015). Exploring issues about computational thinking in higher education. TechTrends, 59(2), 57-65. https://doi.org/10.1007/s11528-015-0840-3
de Paula, B. H., Burn, A., Noss, R., & Valente, J. A. (2018). Playing Beowulf: Bridging computational thinking, arts and literature through game-making. International journal of child-computer interaction, 16, 39-46. https://doi.org/10.1016/j.ijcci.2017.11.003
Denner, J., Werner, L., Campe, S., & Ortiz, E. (2014). Pair programming: Under what conditions is it advantageous for middle school students?. Journal of Research on Technology in Education, 46(3), 277-296. https://doi.org/10.1080/15391523.2014.888272
Duncan, C., & Bell, T. (2015). A pilot computer science and programming course for primary school students. In Proceedings of the Workshop in Primary and Secondary Computing Education, 39-48. ACM. https://doi.org/10.1145/2818314.2818328
General, U. S. (2019). The age of digital interdependence. Report of the UN Secretary-General’s High-Level Panel on Digital Cooperation. https://www.un.org/en/pdfs/DigitalCooperation-report-for%20web.pdf
Google. (2016). Computational thinking for educators. https://edu.google.com/resources/programs/exploring-computational-thinking
Grover, S., Cooper, S., & Pea, R. (2014). Assessing computational learning in K-12. In Proceedings of the 2014 conference on innovation & technology in computer science education. 57-62. ACM. https://doi.org/10.1145/2591708.2591713
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
Grover, S., & Pea, R. (2018). Computational thinking: A competency whose time has come. Computer Science Education: Perspectives on teaching and learning in school. London: Bloomsbury Academic, 19-37. https://www.researchgate.net/profile/Shuchi-Grover-2/publication/322104135_Computational_Thinking_A_Competency_Whose_Time_Has_Come/links/5a457813a6fdcce1971a5ce5/Computational-Thinking-A-Competency-Whose-Time-Has-Come.pdf
Grover, S., Pea, R., & Cooper, S. (2015). Designing for deeper learning in a blended computer science course for middle school students. Computer Science Education, 25(2), 199-237. https://doi.org/10.1080/08993408.2015.1033142
Kroesbergen, E. H., Van Luit, J. E., & Maas, C. J. (2004). Effectiveness of explicit and constructivist mathematics instruction for low-achieving students in the Netherlands. The Elementary School Journal, 104(3), 233-251. https://doi.org/10.1086/499751
Israel, M., Pearson, J. N., Tapia, T., Wherfel, Q. M., & Reese, G. (2015). Supporting all learners in school-wide computational thinking: A cross-case qualitative analysis. Computers & Education, 82, 263-279. https://doi.org/10.1016/j.compedu.2014.11.022
Jenkins, C. (2015). A work in progress paper: Evaluating a microworlds-based learning approach for developing literacy and computational thinking in cross-curricular contexts. In Proceedings of the Workshop in Primary and Secondary Computing Education, 61-64. ACM. https://doi.org/10.1145/2818314.2818316
Johnson, R. B., Onwuegbuzie, A. J., & Turner, L. A. (2007). Toward a definition of mixed methods research. Journal of Mixed Methods Research, 1, 112-133. https://doi.org/10.1177/1558689806298224
Looi, C. K., How, M. L., Longkai, W., Seow, P., & Liu, L. (2018). Analysis of linkages between an unplugged activity and the development of computational thinking. Computer Science Education, 28(3), 255-279. https://doi.org/10.1080/08993408.2018.1533297
McCauley, R., Fitzgerald, S., Lewandowski, G., Murphy, L., Simon, B., Thomas, L., & Zander, C. (2008). Debugging: a review of the literature from an educational perspective. Computer Science Education, 18(2), 67-92. https://doi.org/10.1080/08993400802114581
Miles, M. B., Huberman, A. M., Huberman, M. A., & Huberman, M. (1994). Qualitative data analysis: An expanded sourcebook. Sage publications. https://doi.org/10.1016/0149-7189(96)88232-2
Moreno-León, J., Robles, G., & Román-González, M. (2015). Dr. Scratch: Automatic analysis of scratch projects to assess and foster computational thinking. Revista de Educación a Distancia, 46, 1-23. https://doi.org/10.6018/red/46/10
Mühling, A., Ruf, A., & Hubwieser, P. (2015). Design and first results of a psychometric test for measuring basic programming abilities. In Proceedings of the workshop in primary and secondary computing education, 2-10. ACM. https://doi.org/10.1145/2818314.2818320
National Research Council. (2010). Report of a workshop on the scope and nature of computational thinking. National Academies Press. https://doi.org/10.17226/12840
National Science Foundation. (2016) Computer science for all (CSforAll:RPP)(Dec. 1 2016). https://www.nsf.gov/funding/pgm_summ.jsp?pims_id=505359
Ozoran, D., Cagiltay, N., & Topalli, D. (2012). Using scratch in introduction to programming course for engineering students. In 2nd International Engineering Education Conference. 2, 125-132. https://www.academia.edu/25922529/Using_Scratch_in_introduction_to_programming_Course_for_Engineering_Students
Papert, S. (1980). Mindstorms: Children, computers and powerful ideas. New York: Basic Books. https://doi.org/10.5555/1095592
Resnick, M., Maloney, J., Monroy-Hernández, A., Rusk, N., Eastmond, E., Brennan, K., ... & Kafai, Y. B. (2009). Scratch: Programming for all. Communications of the ACM, 52(11), 60-67. https://web.media.mit.edu/~mres/papers/Scratch-CACM-final.pdf
Román-González, M. (2015). Computational thinking test: Design guidelines and content validation. In Proceedings of EDULEARN15 Conference, 2436-2444. https://doi.org/10.13140/RG.2.1.4203.4329
Román-González, M., Moreno-León, J., & Robles, G. (2017). Complementary tools for computational thinking assessment. In Proceedings of International Conference on Computational Thinking Education, S. C Kong, J Sheldon, and K. Y Li (Eds.). The Education University of Hong Kong, 154-159. https://doi.org/10.1007/978-981-13-6528-7_6
Román-González, M., Pérez-González, J. C., Moreno-León, J., & Robles, G. (2016). Does computational thinking correlate with personality?: The non-cognitive side of computational thinking. In Proceedings of the Fourth International Conference on Technological Ecosystems for Enhancing Multiculturality, 51-58. ACM. https://doi.org/10.1145/3012430.3012496
Romero, M., Lepage, A., & Lille, B. (2017). Computational thinking development through creative programming in higher education. International Journal of Educational Technology in Higher Education, 14(1), 42. https://doi.org/10.1186/s41239-017-0080-z
Rupley, W. H., Blair, T. R., & Nichols, W. D. (2009). Effective reading instruction for struggling readers: The role of direct/explicit teaching. Reading & Writing Quarterly, 25(2-3), 125-138. https://doi.org/10.1080/10573560802683523
Seiter, L., & Foreman, B. (2013, August). Modeling the learning progressions of computational thinking of primary grade students. In Proceedings of the ninth annual international ACM conference on International computing education research, 59-66. ACM. https://doi.org/10.1145/2493394.2493403
Sentance, S., & Csizmadia, A. (2015). Teachers' perspectives on successful strategies for teaching computing in school. In IFIP TCS. https://www.researchgate.net/profile/Sue-Sentance/publication/301525438_Teachers%27_perspectives_on_successful_strategies_for_teaching_Computing_in_school/links/57176e3708ae2679a8c76745/Teachers-perspectives-on-successful-strategies-for-teaching-Computing-in-school.pdf
Shepard, L. A. (2000). The role of assessment in a learning culture. Educational researcher, 29(7), 4-14. https://doi.org/10.3102/0013189X029007004
Shute, V. J., Sun, C., & Asbell-Clarke, J. (2017). Demystifying computational thinking. Educational Research Review, 22, 142-158. https://doi.org/10.1016/j.edurev.2017.09.003
Stanford, P., Crowe, M. W., & Flice, H. (2010). Differentiating with technology. TEACHING exceptional children plus, 6(4), 4. https://files.eric.ed.gov/fulltext/EJ907030.pdf
Teddlie, C., & Tashakkori, A. (2003). Major issues and controversies in the use of mixed methods in the social and behvioral sciences. Handbook of mixed methods in social & behavioral research, 3-50. https://doi.org/10.4135/9781506335193
Tomlinson, C. A. (2012). Differentiated instruction (pp. 307-320). Routledge. http://www.casenex.com/casenex/ericReadings/DifferentiationOfInstruction.pdf
Weintrop, D., & Wilensky, U. (2015). To block or not to block, that is the question: Students' perceptions of blocks-based programming. In Proceedings of the 14th International Conference on Interaction Design and Children, 199-208. ACM. https://doi.org/10.1145/2771839.2771860
Wing, J. M. (2006). Computational thinking. Communications of the ACM, 49(3), 33-35. https://doi.org/10.1145/1118178.1118215
Wing, J. M. (2011). Research Notebook: Computational thinking—What and why. The LINK. The Magazine of Carnegie Mellon University's School of Computer Science. Carnegie Mellon University, School of Computer Science. https://www.cs.cmu.edu/link/research-notebook-computational-thinking-what-and-why
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 (TOCE), 14(1), 5. https://doi.org/10.1145/2576872
Zhong, B., Wang, Q., Chen, J., & Li, Y. (2016). An exploration of three-dimensional integrated assessment for computational thinking. Journal of Educational Computing Research, 53(4), 562-590. https://doi.org/10.1177/0735633115608444
Published
2022-05-02
How to Cite
Moon, H., Cheon, J., & Kwon, K. (2022). Difficult Concepts and Practices of Computational Thinking Using Block-based Programming. International Journal of Computer Science Education in Schools, 5(3), 3–16. https://doi.org/10.21585/ijcses.v5i3.129
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- Kyungbin Kwon, Novice programmer’s misconception of programming reflected on problem-solving plans , International Journal of Computer Science Education in Schools: Vol. 1 No. 4 (2017): International Journal of Computer Science Education in Schools
- Kyungbin Kwon, Sang Joon Lee, Jaehwa Chung, Computational concepts reflected on Scratch programs , International Journal of Computer Science Education in Schools: Vol. 2 No. 3 (2018): International Journal Of Computer Science Education In Schools
- Kyungbin Kwon, Jongpil Cheon, Exploring problem decomposition and program development through block-based programs , International Journal of Computer Science Education in Schools: Vol. 3 No. 1 (2019): International Journal Of Computer Science Education In Schools
