[논문]Exploring Students Competencies to be Creative Problem Solvers With Computational Thinking Practices

Park, Young-Shin; Park, Miso

doi:10.5467/jkess.2018.39.4.388

Exploring Students Competencies to be Creative Problem Solvers With Computational Thinking Practices 원문보기

한국지구과학회지 = Journal of the Korean Earth Science Society, v.39 no.4, 2018년, pp.388 - 400

Park, Young-Shin (Department of Earth Science Education, Chosun University) , Park, Miso (Science and Culture Exhibition Department, National Science Museum)

Abstract ▼ AI-Helper

The purpose of this study was to explore the nine components of computational thinking (CT) practices and their operational definitions from the view of science education and to develop a CT practice framework that is going to be used as a planning and assessing tool for CT practice, as it is required for students to equip with in order to become creative problem solvers in $21^{st}$ century. We employed this framework into the earlier developed STEAM programs to see how it was valid and reliable. We first reviewed theoretical articles about CT from computer science and technology education field. We then proposed 9 components of CT as defined in technology education but modified operational definitions in each component from the perspective of science education. This preliminary CTPF (computational thinking practice framework) from the viewpoint of science education consisting of 9 components including data collection, data analysis, data representation, decomposing, abstraction, algorithm and procedures, automation, simulation, and parallelization. We discussed each component with operational definition to check if those components were useful in and applicable for science programs. We employed this CTPF into two different topics of STEAM programs to see if those components were observable with operational definitions. The profile of CT components within the selected STEAM programs for this study showed one sequential spectrum covering from data collection to simulation as the grade level went higher. The first three data related CT components were dominating at elementary level, all components of CT except parallelization were found at middle school level, and finally more frequencies in every component of CT except parallelization were also found at high school level than middle school level. On the basis of the result of CT usage in STEAM programs, we included 'generalization' in CTPF of science education instead of 'parallelization' which was not found. The implication about teacher education was made based on the CTPF in terms of science education.

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문제 정의

In NGSS, computational thinking as one of important 8 practices is emphasized to be included into science program so that students have opportunities to experience those practices to be creative problem solvers. This research can give any hint or guideline to people who want to develop/teach STEAM program with computational thinking practices.
If students had chances to develop the equipment by their own purpose variously, then DCA and APA might have been promoted in that students evaluate the pros and cons of each their developed equipment and select the best solution on the basis of various views. This study described CT by observable practices in the classroom or program qualitatively so it is not useful to compare which program, which classroom, or who has more CT practices in teaching among them. Therefore, this study can provide another way of describing CT inclusion quantitatively (Park and Lawrence, 2015; Park, 2018).
In this study, the researchers would introduce how one framework (CTPF) including 9 components of computational thinking and their definitions from the perspectives of science education has been developed, and how this tool, CTPF, has been validated by applying it into science program (the selected STEAM program in this study). This study has its significance in STEAM education. In Korea, many teachers are still struggling in how to implement T and E into STEAM program into science class and CT practice can bridge T and E into STEAM context more easily.

제안 방법

The reason why the researchers selected STEAM program for its validation was that the goal of using CT and STEAM is the same; train students to be more creative problem solvers. After this validation, more modification or addition were taken to finalize the CTPF.
The team employed this framework into science program to see if each component of CT was applicable and implemented in any science content (10 different programs, each program consists of 3 or 4 blocks of lessons) and the research team confirmed or validated the former proposed CTPF components with definitions. If any CT component was not applicable with definition, the team discussed and add or modified operational definitions, which produced the first version of CTPF.
In this study, the researchers introduce another framework consisting of 9 components of CT defined by ISTE and CSTA (2011); however, those 9 components of CT had been modified with the view of science education at the 1st step then we employed this framework into STEAM program to construct its validation at the 2^nd step. This CTPF (component of CT practice framework) can be used as evidences to describe what kinds of CT component are included in science program.
However, the inclusion of CT offers little guideline for teachers who would train students to be creative problem solvers. In this study, the researchers would introduce how one framework (CTPF) including 9 components of computational thinking and their definitions from the perspectives of science education has been developed, and how this tool, CTPF, has been validated by applying it into science program (the selected STEAM program in this study). This study has its significance in STEAM education.
Students also tested some natural materials efficient for making rainfall or dirty one cleaner and they chose most efficient materials in order to get the clean water, which is ‘simulation’ CT component.
Students investigated what factors cause Korea into water shortage (abstraction). Students investigated all possible factors to the solution (precipitation pattern, total amount of water resources /volume and usage), characteristics of water supply and causes of water shortage, and available water resources. Then students evaluated water status of individual home and school as well as community and they learned all new and old technology helpful for water shortage according to situation (problem decomposition, abstraction, algorithms and procedures CT components were possible).
Students learned about green algae and they discussed about photo bioreactor’s efficiency in their resident area and they made it in a group (algorithms and procedure).
Students learned about green algae and they discussed about photo bioreactor’s efficiency in their resident area and they made it in a group (algorithms and procedure). Students provided green algae for two weeks and checked its photosynthesis by color in the tank (simulation). However, students did not have a chance to produce oil from green algae after drying them.
In conclusion, first, CT in science education has 9 different components including generalization instead of parallelization. The first three CT components are data related one, which students were familiar with and they employed these three CT components while forming scientific concepts. On the other hand, the rest of CT components were used when students faced problem, built certain model by considering all possible factors influential to the solution.
The researchers imported 9 terms of CT used in computer science discipline (CSTA, 2011); data collection, data analysis, data representation, problem decomposition, abstraction, algorithms & procedure, automation, simulation, and parallelization. The researcher team consisting of science educators and teachers as experts discussed several times if those components of CT can be applicable into science program for the purpose of equipping students with abilities needed as problem solvers and produced one framework consisting of 9 computational thinking terms with the modified operational definition from the perspectives of science education. The team employed this framework into science program to see if each component of CT was applicable and implemented in any science content (10 different programs, each program consists of 3 or 4 blocks of lessons) and the research team confirmed or validated the former proposed CTPF components with definitions.
The researchers imported 9 terms of CT used in computer science discipline (CSTA, 2011); data collection, data analysis, data representation, problem decomposition, abstraction, algorithms & procedure, automation, simulation, and parallelization.
The researchers picked one dominating/outstanding CT component from each lesson and marked each shape to the applicable description of CT practices in NGSS (2013). The pattern of CT inclusion in STEAM from elementary to high school is changing more and more in its frequently usage from K to 12 in NGSS.
The researcher team consisting of science educators and teachers as experts discussed several times if those components of CT can be applicable into science program for the purpose of equipping students with abilities needed as problem solvers and produced one framework consisting of 9 computational thinking terms with the modified operational definition from the perspectives of science education. The team employed this framework into science program to see if each component of CT was applicable and implemented in any science content (10 different programs, each program consists of 3 or 4 blocks of lessons) and the research team confirmed or validated the former proposed CTPF components with definitions. If any CT component was not applicable with definition, the team discussed and add or modified operational definitions, which produced the first version of CTPF.
For water purification, students picked 5 from 8 materials which were good in efficiency and economics for water purification on the basis of students’ decision. Then students carried out the experiment consisting of 5 different material effective for water purification from rainfall. All of these processes, students experienced algorithm, automation, and simulation but parallelization.
Students investigated all possible factors to the solution (precipitation pattern, total amount of water resources /volume and usage), characteristics of water supply and causes of water shortage, and available water resources. Then students evaluated water status of individual home and school as well as community and they learned all new and old technology helpful for water shortage according to situation (problem decomposition, abstraction, algorithms and procedures CT components were possible). In this case, Students created plan or scenario of how our home/school/community could save the water (automation is possible, since they did not run the output but designed it with all consideration).
Students learned some technologies of saving water or storing water from rainfall through the examples of Japan and Germany, and they discussed how they could save water individually or by the unit of community (house or school unit here). Then students tried to make a equipment of saving water by considering concepts they learned, which is algorithm and procedure. Students also tested some natural materials efficient for making rainfall or dirty one cleaner and they chose most efficient materials in order to get the clean water, which is ‘simulation’ CT component.
To construct the validity of CTPF, the researchers implemented this framework to STEAM program (climate change and water shortage) to see if all components of CT could be applied into the context of STEAM program. The reason why the researchers selected STEAM program for its validation was that the goal of using CT and STEAM is the same; train students to be more creative problem solvers.

대상 데이터

This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science, ICT & Future Planning (2016R1A2B4013063). The only data in this study are on the basis of the 2nd author's master thesis in Chosun University.

성능/효과

In conclusion, first, CT in science education has 9 different components including generalization instead of parallelization. The first three CT components are data related one, which students were familiar with and they employed these three CT components while forming scientific concepts.
In general, the profile of CT components in STEAM program showed the CT spectrum covering from data collection to parallelization as levels went higher. The first three data related CT components were dominating at elementary levels, all components of CT except parallelization were found at middle school, and finally more frequencies in each component of CT were found at high school level than middle school one. Therefore, CTPF_v1 (without parallelization) was well defined practice framework which was applicable to decide if any program include components of CT, how much it includes, and how it can be promoted.

참고문헌 (18)

Barr, V. and 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 p.

상세보기
Computer Science Teacher Association, 2011, Computational Thinking Leadership Toolkit.
Ministry of Education, 2015, 2015 revised information curriculum
National Research Council, 2012, A framework for K-12 Science education, Washington. D.C: The National Academies Press.
NGSS Lead States, 2013, Next Generation Science Standards: For states, by states. Washington, DC: The National Academy Press.
Park, Y-S., 2010, Secondary beginning teachers' views of scientific inquiry: With the view of hands-on, minds-on, and hearts-on. Journal of the Korean Earth Science Society, 31(7), 798-812 p.

원문보기 상세보기
Park, Y-S. and Hwang, J., 2017, The preliminary study of developing computational thinking practice analysis tool and its implementation. Journal of Korean Society of Earth Science Education, 19(2), 140-160 p.
Park, Y-S., May, 2018, The study of elementary science program with computational thinking practices and its understandings by elementary teachers, The 1st Korean Geoscience Union conference 2018, Hong-Cheon, Kangwon-Do, Korea.
Park, M., Park, G., Green, J., and Park, Y-S., September, 2017, Development of computational thinking practice tool and its application in science education, The biannual KESS conference 2017, G & R Hub, Chonnam National University, Korea.
Park, Y-S. and Park, M., July, 2017, Exploring the characteristics of computational thinking included in STEAM program, The 72nd KASE conference 2017, Gyungsang National University, Tong-Young campus, Tong-Young, Korea.
Park, Y-S. and Lawrence, B. F., October, 2015, The development of computational thinking practice observational protocol and its application, The 8th International EASE (East-Asian Association for Science Education) Conference 2015, Beijing Normal University, Beijing, China Mainland.
Song, J. and Na, J., 2014, The meaning of science fusion of window and direction of science classroom culture. Journal of Research in Curriculum Instruction, 18(3), 827-845 p.

상세보기
Stohlmann, M., Moore, T. J., and Roehrig, G. H., 2012, Considerations for teaching integrated STEM education. Journal of Pre-College Engineering Education Research, 2(1), 28-34 p.
The International Society for Technology in Education, 2011, Computational Thinking teacher resources second edition.
The International Society for Technology in Education & Computer Science Teacher Association, 2011, Computational Thinking leadership toolkit.
Weintrop, D., Beheshti, E., Horn, M., Orton, K., Jona, K., Trouille, L., and Wilensky, U., 2016, Defining computational thinking for mathematics and science classrooms. Journal of Science Education and Technology, 21(1), 127-147 p.
Wing, J. M., 2006, Computational thinking, Communication of the ACM, 49(3), 33-35 p.
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 p.

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