ISSN: 2226-6348
Open access
The 4th Industrial Revolution is sweeping the world, incorporating technology into communities. High-tech tools and resources are being developed as part of the digital revolution. In the realm of education, there has been a larger emphasis on coding instruction in order to develop a sufficient number of young people to fill fifty percent of computing-related STEM (science, technology, engineering, and mathematics) job openings. Our children and grandchildren must think critically in order to solve the world's ill-structured, unexpected, and intricate issues. Computational thinking, when combined with critical thinking, can produce automatic or semi-automated problem-solving solutions. As a result, computational thinking is becoming increasingly important in science, mathematics, and nearly every other topic. This is demonstrated by Malaysia's recent implementation of computer coding into the school curriculum in order to build 21st century competencies in students. Computational thinking (CT) is one of the conceptual underpinnings required to solve problems successfully and efficiently by combining facts and ideas. In order to solve problems and offer solutions that can be applied in a variety of contexts, computational thinking—i.e., algorithmically, with or without the use of computers—is crucial in computing and information science. It is critical to foster a diversity of abilities and competences among students as to successfully navigate the complexity challenge in the real world. As a result, building a learning environment can be a daunting task for many instructors who lack the tools and research-based expertise to redesign their teaching methods. This conceptual framework has two objectives: (1) developing deep learning and connected computational thinking through a mathematical curriculum instructional model which is capable of improving students' problem-solving skills; and (2) implementing the designed model to assist teachers in education who encountered difficulties when using problem-based curriculum materials. A computational mathematics problem-based learning (CM-PBL) teaching technique is developed to achieve these goals. Students can use the CM-PBL learning framework to simulate and build their own computational models as to aid their self-learning and comprehension of mathematical concepts. It demonstrates that how students can use coding to improve their soft engineering skills.
Aho, A. V. (2012). Computation and computational thinking. The Computer Journal, 55(7), 833–835. https://doi.org/10.1093/comjnl/bxs074
Albanese, M. A., & Mitchell, S. (1993). Problem-based learning: A review of literature on its outcomes and implementation issues. Academic Medicine, 68(1), 52–81. https://doi.org/10.1097/00001888-199301000-00012
Allan, V., Barr, V., Brylow, D., & Hambrusch, S. (2010). Computational thinking in high school courses. SIGCSE’10 - Proceedings of the 41st ACM Technical Symposium on Computer Science Education, 390–391. https://doi.org/10.1145/1734263.1734395
Antonakos, J. L. (2011). Computer Technology and Computer Programming: Research and Strategies. Apple Academic Press, Inc.
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
Barrows, H. S. (2000). Problem-based Learning Applied to Medical Education (Revised Ed). Southern Illinois University School of Medicine. https://books.google.com.my/books?id=L6ZXAAAACAAJ&dq=Problem-Based+Learning+Applied+to+Medical+Education&hl=en&sa=X&ved=0ahUKEwiC7IbF0fnjAhVk73MBHQ1PCtYQ6AEILjAB
Barrows, H. S., & Tamblyn, R. M. (1980). Problem-Based Learning: An Approach to Medical Education. Springer Publishing Company, Inc.
Bers, M. U., Flannery, L., Kazakoff, E. R., & Sullivan, A. (2014). Computational thinking and tinkering: Exploration of an early childhood robotics curriculum. Computers and Education, 72, 145–157. https://doi.org/10.1016/j.compedu.2013.10.020
Boud, D. (1985). Problem-based learning in perspective. In D. Boud (Ed.), Problem-based learning in education for the professions (pp. 13–18). Higher Education Research and Development Society of Australasia.
Charntaweekhun, K., & Wangsiripitak, S. (2006). Visual Programming Using Flowchart. International Symposium on Communications and Information Technologies, 1062–1065.
Chi, M. T. H., Feltovich, P. J., & Glaser, R. (1981). Categorization and Representation. Cognitive Science, 5(2), 121–152. https://doi.org/10.1207/s15516709cog0502_2
Collins, A., Brown, J. S., & Newman, S. E. (1987). Cognitive Apprenticeship: Teaching the craft of reading, writing, and mathematics. In Center for the Study of Reading.
Dabbagh, N., & Denisar, K. (2005). Assessing team-based instructional design problem solutions of hierarchical versus heterarchical web-based hypermedia cases. Educational Technology Research and Development, 53(2), 5–22.
Durak, H. Y., & Guyer, T. (2018). Design and development of an instructional program for teaching programming processes to gifted students using scratch. In J. Cannaday (Ed.), Curriculum Development for Gifted Education Programs (1st ed., pp. 61–99). IGI Global.
Felleisen, M., & Krishnamurthi, S. (2009). Viewpoint: Why computer science doesn’t matter. Communications of the ACM, 52(7), 37–40. https://doi.org/10.1145/1538788.1538803
Gallagher, S. A., Stepien, W. J., & Rosenthal, H. (1992). The Effects of Problem-Based Learning On Problem Solving. Gifted Child Quarterly, 36(4), 195–200. https://doi.org/10.1177/001698629203600405
Gomes, A., & Mendes, A. J. (2007). Learning to program - difficulties and solutions. ICEE 2007-International Conference on Engineering Education. https://www.researchgate.net/publication/228328491_Learning_to_program_-_difficulties_and_solutions
Green, Alison, J. K., & Gilhooly, K. (2005). Problem solving. In N. Braisby & A. Gellatly (Eds.), Cognitive Physcology (1st ed., pp. 347–381). Oxford University Press.
Grover, S., & Pea, R. D. (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
Haapasalo, L., & Zimmerman, B. (2015). Investigating mathematical beliefs by using a framework from the history of mathematics. In C. Bernack-Schler, R. Erens, T. Leuders, & A. Eichler (Eds.), Views and Beliefs in Mathematics Education (pp. 197–211). Springer.
Hambrusch, S., Hoffmann, C., Korb, J. T., Haugan, M., & Hosking, A. L. (2009). A multidisciplinary approach towards computational thinking for science majors. ACM SIGCSE Bulletin, 41(1), 183–187. https://doi.org/10.1145/1539024.1508931
Hmelo-Silver, C. E. (2004). Problem-Based Learning: What and How Do Students Learn? Educational Psychology Review, 16(3), 235–266. https://doi.org/10.1023/B:EDPR.0000034022.16470.f3
Hmelo-Silver, C. E., & Ferrari, M. (1997). The problem-based learning tutorial: Cultivating higher order thinking skills. Journal for the Education of the Gifted, 20(4), 401–422. https://doi.org/10.1177/016235329702000405
Hoffer, B. M. (2012). Satisfying STEM Education Using the Arduino Microprocessor in C Programming. ProQuest Dissertations and Theses, 220. http://login.proxy.library.vanderbilt.edu/login?url=http://search.proquest.com/docview/1069255002?accountid=14816%5Cnhttp://sfx.library.vanderbilt.edu/vu?url_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:dissertation&genre=dissertations+%26+theses&sid=
Honey, M., Pearson, G., & Schweingruber, H. (2014). STEM integration in K-12 education: Status, prospects, and an agenda for research. National Academies Press.
Jaworski, B. (2015). Mathematics meaning-making and its relation to design of teaching. PNA, 9(4), 261–272.
Jimenez, L., & Verschaffel, L. (2014). Development of children’s solutions of non-standard arithmetic word problem solving non-standard arithmetic word problems. Revista de Psicodidáctica, 19, 93–123.
Kalelioglu, F. (2015). A new way of teaching programming skills to K-12 students: Code.org. Computers in Human Behavior, 52, 200–210. https://doi.org/10.1016/j.chb.2015.05.047
Kilpatrick, J., & Swafford, J. (2002). Helping children learn mathematics. National Academies Press.
Kolodner, J. L., Camp, P. J., Crismond, D., Fasse, B., Gray, J., Holbrook, J., Puntambekar, S., & Ryan, M. (2003). Problem-Based Learning Meets Case-Based Reasoning in the Middle- School Science Classroom?: Putting Learning by Design (tm) Into Practice. Journal of the Learning Sciences, 12(4), 495–547. https://doi.org/10.1207/S15327809JLS1204_2
Langdon, D., Mckittrick, G., Beede, D., Khan, B., & Doms, M. (2011). STEM: Good Jobs Now and for the Future. https://www.purdue.edu/hhs/hdfs/fii/wp-content/uploads/2015/07/s_iafis04c01.pdf
Lappan, G., Philips, E., Winter, M. J., Shroyer, J., & Fitzgerald, W. (1986). Middle grades mathematics project: Probability. Addison-Wesley Publishing Company.
Lau, W. W. F., & Yuen, A. H. K. (2011). Modelling programming performance: Beyond the influence of learner characteristics. Computers and Education, 57(1), 1202–1213. https://doi.org/10.1016/j.compedu.2011.01.002
Lean, G. A., Clemens, M. A., & Del Campo, G. (1990). Linguistic and pedagogical factors affecting children’s understanding of arithmetic word problems: A comparative study. Educational Studies in Mathematics, 21, 165–191.
Lee, I., Martin, F., Denner, J., Coulter, B., Allan, W., Erickson, J., Malyn-Smith, J., & Werner, L. (2011). Computational thinking for youth in practice. ACM Inroads, 2(1), 32–37. https://doi.org/10.1145/1929887.1929902
Liu, J. L., & Wang, L. H. (2010). Computational Thinking in Discrete Mathematics. 2010 Second International Workshop on Education Technology and Computer Science, 413–416.
Lu, J. J., & Fletcher, G. H. L. (2009). Thinking about computational thinking. SIGCSE Bulletin Inroads, 41(1), 260–264. https://doi.org/10.1145/1539024.1508959
Lye, S. Y., & Koh, J. H. L. (2014). Review on teaching and learning of computational thinking through programming: What is next for K-12? Computers in Human Behavior, 41, 51–61. https://doi.org/10.1016/j.chb.2014.09.012
Ministry of Education Malaysia. (2013). Malaysia Education Blueprint 2013-2025: Preschool to Post-Secondary Education. https://www.moe.gov.my/muat-turun/penerbitan-dan-jurnal/dasar/1207-malaysia-education-blueprint-2013-2025/file
Papert, S. (1980). Mindstorms: Children, Compuers, and Powerful Ideas. Basic Books Inc.
Papert, S. (1988). A critique of technocentrism in thinking about the school of the future. Children in the Information Age, 3–18.
Papert, S. (1996). An exploration in the space of mathematics educations. International Journal of Computers for Mathematical Learning, 1(1), 95–123.
Patel, V. L., Groen, G. J., & Norman, G. R. (1991). Effects of conventional and problem-based medical curricula on problem solving. Academic Medicine, 66(7), 380–389.
Patel, V. L., Groen, G. J., & Norman, G. R. (1993). Reasoning and Instruction in Medical Curricula. Cognition and Instruction, 10(4), 335–378. https://doi.org/10.1207/s1532690xci1004_2
Polya, G. (1973). How To Solve It Mathematical Method. Princeton University Press.
Resnick, M. (2013). Learn to Code , Code to Learn. EdSurge. https://www.edsurge.com/n/2013-05-08-learn-to-code-code-to-learn
Resnick, M., Maloney, J., Monroy-Hernandez, A., Rusk, N., Eastmond, E., & Brennan, K. (2009). Scratch:Programming for all. Communications of the ACM, 52(11), 60–67.
Robertson, J., & Howells, C. (2008). Computer game design: Opportunities for successful learning. Computers and Education, 50(2), 559–578. https://doi.org/10.1016/j.compedu.2007.09.020
Savery, J. R., & Duffy, T. M. (1996). Problem based learning: An instructional model and its constructivist framework. In B. Wilson (Ed.), Constructivist Learning Environments: Case Studies in Instructional Design (pp. 135–148). Educational Technology Publications.
Schmidt, H. G., & Moust, J. H. C. (2000). Factors affecting small-group tutorial learning: A review of research. In D. H. Evenson & C. E. Hmelo (Eds.), Problem-based learning: A research perspective on learning interactions (pp. 19–51). Lawrence Erlbaum Associates Publishers.
Sneider, C., Stephenson, C., Schafer, B., & Flick, L. (2014). Computational Thinking in High School Science Classrooms: Exploring the Science “Framework” and “NGSS.” Science Teacher, 81(5), 10–15. https://doi.org/10.2505/4/tst14
Stigler, J., & Hiebert, J. (2004). Improving mathematics teaching. Educational Leadership, 61(5), 12–17.
Strampel, K., & Oliver, R. (2007). Using technology to foster reflection in higher education. Proceedings Ascilite Singapore 2007 - ICT:Providing Choices for Learners and Learning, 973–982. http://www.ascilite.org.au/conferences/singapore07/procs/strampel.pdf%0A
Strobel, J., & van Barneveld, A. (2009). When is PBL More Effective? A Meta-synthesis of Meta-analyses Comparing PBL to Conventional Classrooms. Interdisciplinary Journal of Problem-Based Learning, 3(1), 44–58. https://doi.org/10.7771/1541-5015.1046
Torp, L., & Sage, S. (1998). Problems as Possibilities: Problem-Based Learning for K-12 Education (2nd Ed.). Association for Supervision and Curriculum Development.
Verschaffel, L., Greer, B., & de Corte, E. (2000). Making sense of word problems. Swetz and Zeitlinger Publishers.
Voskoglou, M. G., & Buckley, S. (2012). Problem Solving and Computational Thinking in a Learning Environment. Egyptian Computer Science Journal, 36(4), 28–46. http://arxiv.org/ftp/arxiv/papers/1212/1212.0750.pdf
Wilkerson, L., & Gijselaers, W. H. (1996). Concluding Comments. New Directions for Teaching and Learning, 68, 101–104. https://doi.org/10.1002/tl.37219966814
Wing, J. M. (2006). Computational Thinking. Communications of the ACM, 49(3), 33–35. https://doi.org/10.1145/1118178.1118215
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
Woods, D. R. (1996). Problem-based learning for large classes in chemical engineering. New Directions for Teaching and Learning, 1996(68), 91–99. https://doi.org/10.1002/tl.37219966813
In-Text Citation: (Ting et al., 2023)
To Cite this Article: Ting, K. S., Talib, O., Ayub, A. F. M., Zolkepli, M., Yee, C. C., & Hoong, T. C. (2023). Word Problems as a Vehicle for Teaching Computational Thinking. International Journal of Academic Research in Progressive Education and Development, 12(1), 1375–1390.
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