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ISSN : 1225-6692(Print)
ISSN : 2287-4518(Online)
Journal of the Korean earth science society Vol.41 No.4 pp.415-434
DOI : https://doi.org/10.5467/JKESS.2020.41.4.415

The Analysis of Computational Thinking Practices in STEAM Program and its Implication for Creative Problem Solvers in the 21
st Century

Young-Shin Park, James Green*
Science Culture Education Center, Chosun University, Gwangju 61542, Korea
*Corresponding author: jameswgreen82@gmail.com Tel: +82-62-230-7379
August 1, 2020 August 28, 2020 August 28, 2020

Abstract


The purpose of this study was to explore if, what kinds of, how much computational thinking (CT after this) practices could be included in STEAM programs, and what kinds of CT practices could be improved to make STEAM revitalized. The CT analyzing tool with operational definitions and its examples in science education was modified and employed for 5 science-focused and 5 engineering-focused STEAM programs. There was no discerning pattern of CT practices uses between science and engineering STEAM programs but CT practices were displayed depending on their topics. The patterns of CT practices uses from each STEAM program could be used to describe what CT practices were more explored, weakly exposed, or missing. On the basis of these prescription of CT practices from each STEAM program, the researchers could develop the weakly exposed or missing CT practices to be improved for the rich experience in CT practices during STEAM programs.



초록


    National Research Foundation of Korea
    NRF-2016S1A5A2A01023698

    Introduction

    The goal of science education is scientific literacy, which is extended in its meaning in the 21st century. Students must be equipped with the skills necessary to solve problems from the community beyond obtaining the knowledge from curiosity, and ‘the skills necessary’ are considered recently as ‘computational thinking (CT)’ (Lamprou & Repenning, 2018;Park & Green, 2019;Sengupta et al., 2012), which is one of competencies required in science education. CT has been researched as the subject of intense and long discussions within the community of computer science and technology education (ISTE & CSTA, 2011;NRC, 2010;2011, Wing 2006;2008), where CT include the following practices; problem representation, abstraction, decomposition, simulation, verification, and prediction. Wing (2006;2008) argues that CT is a basic skill for all humans, not just computer scientists and she also address that CT should be taught everywhere including schools. Nine different skills of CT, defined by computer related associations (ISTE & CSTA, 2011), have been emphasized to be mastered by students at schools, which are data collection, data analysis, data representation, problem decomposition, abstraction, algorithms and procedures, automation, simulation, and parallelization.

    Recently, there has been a lot research and education to apply CT to the domain of science education. Next Generation Science Standards (NGSS) places a new emphasis on authentic scientific investigation with 8 distinct practices (NGSS Lead States, 2013), and computational thinking as the 5th practice (with mathematical thinking) is less familiar even to veteran teachers (Weintrop et al., 2016;Park & Green, 2019) which, in turn, reflects the growing importance of computation and digital technologies over the scientific disciplines. However, the inclusion of this practice offers little information and guideline for teachers who need to realize and apply it to the classroom. To make this gap connected from the perception of computational thinking from technology to science education, there have been trials in research and education as follows.

    Weintrop et al. (2016) addressed that they introduced those practices of CT from the perspective of technology transferred to those in science education and they implied that people in science education feel more comfortable in including CT into NGSS now such as teachers feel comfortable in their teaching, policymakers prioritize CT as a part of science education and curriculum developers produce CT materials targeted for science classrooms. Park & Green (2019) addressed that CT is a critical practice if students need to be equipped with competencies to be problem solvers for the 21st century. For scientific literacy, students need to have another chance to apply scientific concepts after forming them. Students form concepts by abstracting the problems they face and students apply those concepts to their daily lives by automating the solutions. The researchers also stated that CT can be observable and measurable by comparing and introducing CT components/practices and CT can be the catalyst for STEAM education. Sengupta et al. (2012) agreed that computational thinking is phrased to indicate a thought process involved in formulating problems and their solutions so that the solutions are represented in concrete form carried out by an information-processing agent (Wing, 2006). CT becomes evidence only in particular form of epistemic practice involving the generalization or use of external representations. The comparison between abstractions in CT and scientific inquiry were studied; for example, encapsulation practice in CT corresponds to that of creating coherent formal representation of scientific processes and phenomena; algorithm design and complexity analysis in CT correspond to mechanistic reasoning and explanation and etc. Barr & Stephenson (2011) released that there are multiple definitions of CT in different disciplines and it is imperative to create a definition for CT in K-12 by considering the followings; what CT looks like in the classroom; what skills students would demonstrate, what teachers need in order to put CT into practice; what skills teachers do to be modified and extended. To make this definition useful in the classroom, it is suggested that a definition with concrete examples demonstrating how CT can be incorporated in the classroom should be developed. CT concepts were shown in how they are embedded in activates across multiple disciplines. In science disciplines, data practices were matched with each other between CT capabilities and science practices; such as collecting data from an experiment (science) from data collection (CT); summarize data from an experiment (science) from data representation and analysis (CT); build a model of a physical entity (science) from abstraction (CT); do an experimental procedure (science) from algorithms & procedure (CT) etc. A few CT concepts like automation and parallelization were not matched with science practice appropriately but it is meaningful for the researchers to try to make links CT with other disciplines (computer science, math, science, social studies, and language art) by transforming CT to be practical needing skills in classroom culture.

    CT cannot be taught in a traditional way and has to overcome both a pedagogical and systemic challenge (Lamprou & Repenning, 2018;Park & Green, 2019). More concrete descriptions and connections between CT and science were explored on the basis of discussions and theories about CT. When high school students use an interactive simulation to explore the relationship between macroscopic properties of gasespressure, volume, and temperature-to see how these properties emerge from microscopic interactions. The tool of simulation has been considered to help students build conceptual understandings of the concepts being modeled, which is described as ‘using computational models to understand a concept’ in CT taxonomy (Weintrop et al., 2016). Park & Hwang (2018) worked with one teacher, Mr. Son, and he revised the curriculum to give students (6th grade) the chance of building and producing a traditional Korean traditional village with the function of reducing roof angles depending on the incidence of sunlight during different seasons. Mr. Son encouraged students to work on building algorithms to be automated with critical points be considered. Students needed to calculate the exact angle of the roof according to the Sun’s altitude at different seasons. This process was described as automation, representation of a solution (Lamprou & Repenning, 2018). There has been also connections between CT and STEM/STEAM programs and researches reported those connections in a beneficial ways such as CT is a catalyst for STEAM program, the pedagogy of science learning with integrating CT is effective, and it provides a justification for the choice of programming in order to facilitate scientific modeling with CT. In most science textbooks, there is curriculum indicating what to learn with the focus of scientific thinking. However if teachers revise the curriculum where students apply those learned concepts with the focus of CT as well, it is not hard work to develop and implement STEM/STEAM program into the classroom anymore. In this study, we would like to explore how much we can find CT practices in STEAM programs, which are not developed with CT inclusion at first, and what kinds of CT practices we can suggest from the analyzed results of CT. Teachers can use this CT analyzing tool as prescription for STEAM programs, or self-evaluation. The research questions can be as follows.

    • 1. What kinds of CT practices can be found in STEAM programs and what description of CT in STEAM can be illustrated? Here, if the question is extended, what kinds of CT practices and what description of CT in STEAM can be illustrated in Science-Focused STEAM programs, and Engineering- Focused STEAM programs?

    • 2. What kind of CT practices can be suggested to revitalize STEAM programs on the basis of descriptions made above?

    • The significance of this study can provide the practical strategies of how to analyze STEAM program if there is CT practice and how to promote STEAM program by adding concrete CT practices. This study will bridge the gap between theory and practice about CT introduced in science curriculum (MOE, 2015;NRC, 2012).

    Methodology

    Selecting subjects (STEAM programs) in this study

    Two areas (science-focused, engineering-focused STEAM programs) were selected for this research. The reason why two areas were selected is that it was assumed there would be different patterns of description in using CT practices between sciencefocused and engineering-focused STEAM programs separately. The reason why the researchers selected two areas of STEAM is to compare CT practices use patterns in a variety of STEAM programs if any. The criteria which were used in selecting STEAM program are as follows. First, the researchers collected the data from the products of STEAM programs by KOFAC (Korean Foundation for the Advancement of Science and Creativity) website where various types of STEAM programs are found to be downloaded. KOFAC spends a considerable amount of funds on developing STEAM programs ever year. The recent products of STEAM programs were selected from 2019 and five science-focused STEAM and five engineering-focused STEAM programs were selected from middle school and high school levels by considering the researchers’ background. The selected STEAM programs (Table 1; Table 2) are introduced with lessons in each module and titles with overviews.

    Modifying the CT analyzing tool

    Two frames were used to produce one reasonable CT analyzing tool in this study; one is from Park & Hwang (2017) and the other is from Weintrop et al. (2016). The protocols of CT practice by Park & Hwang (2017) were derived from three CT resources; Advanced Placement Computer Science Principles Curriculum Framework (2013;2017), 9 CT components by ISTA & CSTA (2011), and CT practice by NGSS (NGSS Lead States, 2013). The researchers compared those two frames (Park & Hwang, 2017;Weintrop et al., 2016), combined, modified, and redefined CT practices/protocols with examples. Major categories in CT were from the Weintrop et al. (2016) frame and sub practices were compared and finalized with operational definitions with examples (Appendix). The modified CT analyzing tool includes 5 protocols in Data Practice (DP), another 5 in Modeling and Simulation Practice (MS), and another 6 in Computational Problem-Solving Practice (PS). The main categories were rooted from Weintrop et al. (2016) and concrete definitions and examples were from the frame (Park & Hwang, 2017) with the consideration of the researchers’ background. The samples of CT analyzing tool in this study are as follows (Table 3).

    The researchers discussed each item from theories and practical experience to be consensus in its definition for the content validity and its reliability.

    Collecting and analyzing the data

    From the STEAM programs, the researchers used the CT analyzing tool (modified for this study) and counted the most exposed CT practices in each lesson of each module. There could be a few or a several CT practices in each section of lessons but there were agreement between the researchers to count the most exposed practice of CT for collecting data. The following sample of collecting data shows how the researchers analyzed the content of each lesson to see what protocols of CT practices found (Table 4).

    All counted protocols of CT practice from CT analyzing tool in this study were transferred to the bar graph so that the pattern of CT uses could be read easily in order to describing the uses of CT in each module.

    Results

    Two results from research questions are introduced in order; (1) how to analyze CT practices in science focus STEAM programs and engineering focus STEAM programs; (2) what CT practices can be suggested to STEAM programs on the basis of their analyzed results.

    The characteristics of CT practices in STEAM lessons

    In the results, the characteristics of CT practices in STEAM (in science and in engineering-focused) will be described by tables including one graph and its description withdrawn from each program (total 10 tables from 5 science and 5 engineering STEAM programs) and one graph from 5 science and the other graph from 5 engineering STEAM programs will be shown with general descriptions of CT practices.

    Science-focused STEAM program

    The researchers analyzed the selected 5 science focus STEAM programs to see if CT can be included, how much CT practice could be found and what kinds of CT could be found. The results are as follows. Each table includes the graph showing the pattern of CT practices uses in STEAM program and its interpretation of CT uses. Table 5 shows the pattern of CT practices in science 1 STEAM program, whose title is ‘we will tell you the weather of the universe’.

    The most commonly found skill was PS5 (Creating Computational Abstractions) with 23.1% or 6/26 skills found in the module. All 6 instances were found in the first lesson. This is due to abstraction being a necessary step for students to come to understand a topic. They have to decide what are the important factors to consider and what can be confidently be ignored. An example from this module would be the exercise where the students are asked to consider what might be the cause of a communication failure with wireless equipment and what might be some possible damages. The joint second most found were DP1 (collecting data), MS3 (Assessing Computational Models), and MS4 (Designing Computational Models) all with 15.4% or 4/26. An instance of DP1 found in this module is the designing of a space weather warning app. On paper, the students have to make decisions such as the app name, who is the app aimed at, and the contents. There 7 examples of skills that recorded no instances. One of those PS1 (Preparing Problems for Computational Solutions) is in many cases found before MS4 (Designing a Computational Model) as it is considered a necessary step to prepare the problem before design the model.

    The pattern of CT practices in science 2 STEAM program is displayed, whose title is ‘burning ice, gas hydrate’ (Table 6).

    The most commonly found skill was DP1 (Collecting Data) with 40.9% or 9/22 practices found in the module. 8 of the 9 instances were found in the first lesson. This is because the students were presented with a great deal of data by the teacher to give them an introduction into what is probably an unfamiliar topic. An example of this is the students had to read the information about gas hydrate to answer questions, such as, “How is gas hydrate made?” and “What are the benefits and disadvantages or gas hydrate?” The second practice most found was MS1 (Using Computational Models to Understand a Concept) with 22.7% or 5/22. This is due to the fact that the students perform 5 different activities aimed at helping the students to grasp the concepts. One of those activities was to make a molecular model (ball and stick model) of a gas hydrate molecule surrounded by 20 water molecules. This activity gives the students an understanding of the concept of a stable gas hydrate molecule. There 9 examples of practices that recorded no instances. One of those was DP4 (Analyzing Data) was expected as the students collect so much data, but do not make any analysis, they just answer the given questions.

    The pattern of CT practices in science 3 STEAM program is displayed, whose title is ‘Dokdo, lonely stone island’ (Table 7).

    The joint most commonly found skills were PS1 (Preparing Problems for Computational Solutions) and PS5 (Creating Computational Abstractions) with 28.6% or 4/14 practices found in the module. 3 of the PS1 and 2 of the PS5 practices were in lesson 1 and the others were in lesson 3. None were found in lesson 2 as this is a very short lesson. There 10 examples of practices that recorded no instances. It is notable that there was no MS4 (Designing Computational Models) found in the module. In other modules the MS4 and MS5 (Creating Computational Models) were generally found together. This result shows that it is not necessarily true that the two practices must always be present together.

    The pattern of CT practices in science 4 STEAM program is displayed, whose title is ‘silver care expert’ (Table 8).

    The most frequently found practice was DP1 (Collecting Data) 8/50 (16%). The practices were found though-out the module but mostly in lesson 1 with 5/8 (62.5%), 1/8 (12.5%) in lesson 2, and 2/8 (25%) in lesson 3. An example of the DP1 practice found in this module was the activity where the students have to read two extracts and find the symptoms of aging. The second most common practice was PS5 (Creating Computational Abstractions) 7/50 (14%). 4/ 7 (57.1%) were found in lesson 1 and 3/7 (42.9%) were found in lesson 3. There were no PS5 practices found in lesson 2. An example of PS5 found in this module was the activity where the students have to create a questionnaire to ask elderly people about their problems and issues. The students have to consider what questions they want to include in the questionnaire. They need to know what would be the important information to ask. A total of 3 practices were found with 0 instances, MS2 (Using Computational Models to Find and Test Solutions), PS2 (Programming) and PS6 (Trouble-shooting and Debugging).

    The pattern of CT practices in science 5 STEAM program is displayed, whose title is ‘autonomous cars’ (Table 9).

    The module is led by four practices, PS5 (Creating Computational Abstractions), PS1 (Preparing Problems for Computational Solutions), DP1 (Collecting Data) and MS3 (Assessing Computational Models). There were 8/30 (26.7%) PS5 practices, 7/30 (23.3%) PS1 practices, and 6/30 (20%) instances of DP1 and MS3. An interesting example from this module is the cases of MS3 found. All 6 MS3 practices found were questions of an ethical or moral nature. The students had to decide what action the autonomous car should make in a given situation, e.g. should the car hit an elderly or young person? There are 10 examples of practices that recorded no instances. It is notable that although there were 6 instances of data being collected there were no activities to have the students manipulate, analyze, or visual the data.

    There was a total of 142 practices found in the 5 science modules combined together, 47 (33.1%) Data Practices, 40 (28.2%) Modeling and Simulation Practices, and 55 (38.7%) Computational Solving Practices. Although there is a significant number of practices from all 3 groups there were greater numbers of Computational Problem-Solving Practices found. The results also show that the Data Practices were dominated by DP1 with 29/47 (61.7) and Computational Problem- Solving Practices were dominated by PS5 27/55 (49.1%) and PS1 15/55 (27.3%). The Modeling and Simulation Practices group, however, had a more even spread amongst the five practices.

    The most commonly found skill was DP1 (Collecting Data) with 29/142 or 20.4% of the found practices. Of these, 19/29 (65.5%) were in the first lessons of the various modules. This matches the researchers’ expectations as in our experience a popular introduction to topics/issues is to present the students with established information and asking them to find the required data points. The second most common was PS5 (Creating Computational Abstractions) with 27/142 (19%). 16/27 (59.3%) of these were found in the first lesson of the five modules. As discussed when reviewing the results from Science 1: We Will Tell You the Weather of the Universe, this is due to the possibility that PS5 is a common first step in getting students to begin to contemplate the issue at hand.

    There were two practices that registered 0 occurrences, MS2 (Using Computational Models to Find and Test Solutions) and PS2 (Programming). Having no instances of MS2 is the most surprising to the authors but no PS2 is less surprising. PS2 is called programming but it is the opinion of the authors that this also includes algorithms. Actual computer programming maybe considered by some as too difficult for middle school students but it is possible and thinking in algorithms is certainly possible.

    Engineering-focused STEAM program

    The pattern of CT practices in engineering 1 STEAM program is displayed, whose title is ‘create automated devices for safe living from disasters’ (Table 10).

    The most frequently found practice was PS5 20/66 or 30.3%. There were spread evenly between the four lessons, 6/20 (30%) in the first lesson, 5/20 (25%) in both lessons 2 and 3, and 4/20 (20%) in lesson 4. An example of PS5 found in the third lesson of this module is when the students are asked to make a presentation about comparing sensors and actuators to our bodies. While the making of a presentation isn’t PS5, considering what similarities and differences to include in the presentation is abstraction. The second most common practice was MS3 10/66 or 15.2%. 7/ 10 (70%) of those were found in the final lesson. The final lesson is about exhibition hall and showing your work to classmates. There is an example of MS3 here in that the students are asked to review the pros and cons of their classmates’ automation device. There are 3 practices that were not found in this module, DP2 (Creating Data), MS1 (Using Computational Models to Understand a Concept), and MS2 (Using Computational Models to Find and Test Solutions). The fact that there was no MS1 and MS2 shows the students did no using of models to try and understand a concept or test their understanding of a concept.

    The pattern of CT practices in engineering 2 STEAM program is displayed, whose title is ‘science story at the airport and airplanes’ (Table 11).

    The practice found the greatest number of times was PS5 (Creating Computational Abstractions) with 15/69 or 21.7%. Instances were found in all three of the lessons, but they were mainly found in lesson 1 and 2. There were 8/15 or 53.3% in lesson 1 and 5/ 15 (33.3%) in lesson 2. However, there were only 2/ 15 (13.3%) in lesson 3. The second most common practice found was DP1 (Collecting Data) with 9/69 (13%). They were evenly distributed between the three lessons with 3/9 (33%) in lesson 1, 4/9 (44.4%) in lesson 2, and 2/9 (22.2%) in lesson 3. Five of the practice recorded zero instances. They were DP2 (Creating Data), DP3 (Manipulating Data), PS2 (Programming), PS3 (Choosing Effective Computational Tools), and PS6 (Troubleshooting and Debugging).

    The pattern of CT practices in engineering 3 STEAM program is displayed, whose title is ‘where is the fine dust’ (Table 12).

    The two joint most frequently found practices were DP1 (Data Collection) and DP4 (Data Analysis) both with 10/44 or 22.7% of the total. 7/10 (70%) of DP1 and 9/10 (90%) of DP4 were found in the first of the two lesson of this module. For an example of DP1 found in this module is the videos that the students are asked to watch to collect information about fine dust, such as, what is fine dust and how dangerous is it? One of the DP4 practices that was found was when the students are analyzing data from the airkorea website to see if there is a difference in the fine dust concentration in the different regions of Korea. There were 8 practices that recorded 0 instances in this module. The missing practices were DP2 (Creating Data), DP3 (Manipulating Data), MS1 (Using Computational Models to Understand a Concept), MS4 (Designing Computational Models), PS2 (Programming), PS3 (Choosing Effective Computational Tools), PS4 (Assessing Different Approaches/Solutions to Problem), and PS6 (Troubleshooting and Debugging).

    The pattern of CT practices in engineering 4 STEAM program is displayed, whose title is ‘ecological drone use’ (Table 13).

    The joint most common practice in this module was PS5 (Creating Computational Abstraction) with 8/39 or 20.5%. The practices were mostly found in lesson 1, with 7/8 PS5 practices found in that lesson. An example of PS5 found in this module was when the students are asked to think about what uses drones could be used for to help restore ecosystems. The other joint most found practice was DP1 (Data Collection) with 8/39 or 20.5%. The first activity the students were asked to perform was to watch a video to find out which plants are endangered and also about how environmental pollution can affect peoples’ lives. There were 4 practices in this module that were not found, DP2 (Creating Data), DP4 (Analyzing Data), MS1 (Using Computational Models to Understand a Concept), and PS4 (Assessing Different Approaches/ Solutions to a Problem).

    The pattern of CT practices in engineering 5 STEAM program is displayed, whose title is ‘automata bearing safety’ (Table 14).

    The most commonly found skill was PS5 (Creating Computational Abstractions) with 31.6% or 18/57 skills found in the module. The 18 practices were found through-out the three modules, but half (9/18 50%) were found in lesson 1. An example of the PS5 found in this module was when the students were asked to consider what safety problems there are in their school and how they can be solved. The second most found was DP1 (collecting data) with 14% or 8/ 57. An instance of DP1 found in this module is the activity the students did where they watched a video to learn the names of the different types of gears. There are 4 examples of skills that recorded no instances. With there being 8 instances of DP1 then DP4 (Analyzing Data) is normally found as the students analyze the data.

    There was a total of 276 practices found in the 5 engineering modules combined together, 78 (28.3%) Data Practices, 64 (23.2%) Modeling and Simulation Practices, and 134 (48.5%) Computational Solving Practices. Although there is a significant number of practices from all 3 groups there were greater numbers of Computational Problem-Solving Practices found. The results also show that the Data Practices were dominated by DP1 with 38/78 (48.7%) and Computational Problem-Solving Practices was dominated by PS5 with 67/134 (50%). The Modeling and Simulation Practices group, however, had a more even spread amongst the five practices. The most commonly found skill was PS5 (Creating Computational Abstractions) with 67/276 or 24.3% of the found practices. The second most common was DP1 (Collecting Data) with 38/276 or 13.8%. There was one practice that registered 0 occurrences, DP2 (Creating Data). We might speculate that DP2 is too difficult for middle school age students. It is for the times when collection of data is infeasible, i.e. galaxy formation or plate tectonics.

    Improving the missing/weakly exposed CT practices

    On the basis of the patterns of CT uses in each STEAM program, how the missing CT or weakly exposed CT practices can be improved has been discussed among the researchers. In this study, the relative weakly exposed or missing CT practices of each module (5 science and 5 engineering) was developed and introduced. This result can imply that CT practices can be brought into the classroom by showing that we can improve CT practices as missing or weakly exposed ones in STEAM programs. We developed the possible improved CT on the basis of the analyzed pattern of CT as follows.

    There are 10 different STEAM programs in this study and each module consists of a few or more lessons. Two samples, one from a science, and one from an engineering focused STEAM program are introduced to show how more CT practices can be improved. The sample science program (Table 15) is based on Science 1: We Will Tell You the Weather of the Universe. This module is composed of three lessons, “When the Sun goes Boom, the Earth is Bruised”, “Space Weather Forecast and Special Report Through Observation of Solar Activity”, and “Creating a Space Environment Forecast Application”. This suggested additional activity is to be added to the second lesson. In order to add some context the following is a brief summary of the module up to where the suggested activity would be added.

    The first lesson starts with the students investigating the damage that can be caused by solar flares. They then look at the solar activity over the last 100 years and see how common instances of increased sunspots are. Next, and final activity of lesson 1, is to look at the phenomenon caused by solar activity and the effects they can have. Lesson 2 starts with a look at how space weather can affect live on Earth, such as GPS, wireless communication, and flight. The activity that it is suggested to add would be inserted here. The original activity, shown in Table 15, involves the students looking at the Space Weather Center and Space Weather Forecast service site to see the information the website gives about space weather elements and what forecast they provide.

    The above science focus STEAM program has the pattern of highly used CT practices in DP1, DP4, MS3, MS4, and PS5. More CT practices of MS5 (missing), PS2 (missing), and PS6 (weakly exposed) were developed and added and this improved STEAM program could give students chances to experience more extended CT practices as envisioned ones. The purpose of the suggested activity (Table 15) is to give the students the chance to experience programming. As shown by Clements & Gullo (1984), programming provides a situation for students to practice effective thinking processes. As was argued by Margolis et al. (2008) programming is often seen as something that can only be done by the “best and the brightest”. This activity can therefore be used to introduce programming to every student regardless of ability, gender, or ethnicity and show them that it can be done by anyone.

    Another sample is from engineering focus STEAM program. The following sample of suggesting how CT practices could be improved from the weak to the strong exposed ones in engineering focus STEAM program, whose title is ‘create automated devices for safe living from natural disasters and man-made disasters’ (Table 16). This module is composed of eight lessons, “What are Disasters and How Can We Overcome Them” is lesson 1. Lessons 2 and 3 are grouped and are called “Science and Technology Challenge for Disaster and Disaster Alarm”. Lessons 4 to 7 are called “Creating an Automated Disaster Alarm”, and lesson 8 is called “An Exhibition Hall of the Classes Work”. This is how the lessons were designated in the original module. This suggested additional activity is to be added to the first lesson. In order to add some context the following is a brief summary of the module up to where the suggested activity would be added.

    The module starts with an introduction of the students reading about the damage caused by an earthquake in Japan. They then read a passage that introduces the idea of natural and man-made disasters and how technology might be used to to reduce the damage caused by disasters. Lesson 1 starts with the students watching some disaster movie trailers and considering some questions about what a disaster is how disasters affect human life and what the relationship is between technology and disasters. The lesson then continues with a more indepth look at the different ways that technology can be used to help during a disaster.

    The above engineering focus STEAM program has the pattern of highly used CT practices in DP1 and PS5 mainly. More CT practices of MS1(very weakly exposed) and MS2 (missing) were developed and added and this improved STEAM program could give students chances to experience more extended CT practices as envisioned ones. In this way, the researchers could improve CT practices to be more exposed with the use of the CT analyzing tool developed in this study. This result demonstrated that current STEAM programs can be improved with more explicit CT practices included so that STEAM education could meet the vision of creative problem solvers needed in the 21st century. The purpose of the suggested activity is to give the students the chance to use some computational models to understand how different strategies and technology can be used to protect and reduce the damage from disasters. The desktop game is from www.stopdisastersgame.org, which was developed by The United Nations for Disaster Risk Reduction (UNDRR). From the UNDRR’s website, “It enables players to experience 5 natural environmental hazards (wildfires, earthquakes, floods, tsunamis, and hurricanes). Learn of the risk posed by natural hazards and manage your resources. Build schools, hospitals, housing and defenses to protect the local population.” (“Stop disasters!” 2020).

    Conclusion and Implication

    The possibilities of CT practices inclusion in sciencefocused STEAM programs and engineering-focused ones were analyzed, their patterns of CT practices were described, and the possibilities of making weakly exposed or missing CT practices stronger were explored by adding new activities to make the following conclusions.

    First, notable CT practices of Data Practices (DP1 to DP5), Modeling and Simulation Practices (MS1 to MS5), and Computational Problem-Solving Practices (PS1 to PS6) were differently used in STEAM programs according to their topics. In each pattern of CT practices inclusions in the results, the weakly or strongly exposed CT practices and the missing ones were noticeable. There was not any certain pattern differentiating CT practices in science-focused STEAM programs from engineering ones, which implies that CT practices can be applied to any subject even in daily lives as Wing (2006;2008) indicated. CT practices of STEAM programs are not dependent on various disciplines but topics or themes. According to the theme or topics, different types of CT practices of STEAM programs were remarkably displayed by the use of CT analyzing tool in this study. We expect students to learn science concepts and its core competencies by STEAM programs (Park & Park, 2018) to be creative problem solvers and CT practices as this study indicated are found in STEAM programs even though those are weakly exposed in the program. CT practices CT practices as this study indicated are found are not new but implicitly embedded in STEAM programs. It is necessary for CT to be exposed explicitly by the teachers when developing or teaching STEAM programs in the classroom. The CT analyzing tool developed and employed in this study can provide the needed guidance to teachers.

    Second, on the basis of analyzed CT practices in STEAM programs, the pattern of CT practices showed which CT practices were more exposed than other ones relatively or which one was missing. With these results, the researchers made weakly exposed or missing CT practices to be improved by adding some activities into original ones so that students could have more chance to experience various types of CT practices. The validity about new CT practices added or modified to the original activities had been constructed among the researchers through discussion to be consensus but there is still room for further discussion of how to improve CT practices and how to construct those validities to be generalized into the classroom. Nevertheless, this study implies at least that CT practices in any STEAM program which had been analyzed with weak or missing CT practices can be improved for more experiences by rich CT practices, required to be creative problem solvers envisioned in the 21st century.

    Third, the CT analyzing tool in this study with operating definitions and its examples in science education can provide the instructions of how to include CT practices into the classroom by STEAM programs. The researchers have released that there is a gap between theory and practice from new policy and new curriculum, most educators and teachers have struggled to capture the way of how to implement ‘computational thinking’ practice out of the 8 ones envisioned in NGSS (2013) and ‘computer use’ in Korean revised science curriculum (2015). To bridge the theory and practice in ‘computational thinking’ competency envisioned in the 21st century, this study can make a step forward to make STEAM programs to be revitalized in Technology and Engineering more than before. The CT analyzing tool in this study should be developed in detail with more forth coming researches and its validity should be further established.

    Acknowledgment

    This work was supported by the Ministry of Education of the Republic of Korea and the National Research Foundation of Korea (NRF-2016S1A5A2 A01023698)

    Figure

    JKESS-41-4-415_F1.gif

    The pattern of CT uses in Science Focus STEAM programs

    JKESS-41-4-415_F2.gif

    The pattern of CT uses in Engineering Focus STEA grams. *DP: Data Practices MS: Modeling and Simulation PS: Computational Problem-Solving Practices

    Table

    The modules of STEAM program (science focused) selected in this study

    The modules of STEAM program (Engineering focused) selected in this study

    The sample practice of CT analyzing tool with definition and example

    The sample of collecting the data for this study

    The CT pattern of the 1st Science STEAM program and its description

    The CT pattern of the 2nd Science STEAM program and its description

    The CT pattern of the 3rd Science STEAM program and its description

    The CT pattern of the 4th Science STEAM program and its description

    The CT pattern of the 5th Science STEAM program and its description

    The CT pattern of the 1st Engineering STEAM program and its description

    The CT pattern of the 2nd Engineering STEAM program and its description

    The CT pattern of the 3th Engineering STEAM program and its description

    The CT pattern of the 4th Engineering STEAM program and its description

    The CT pattern of the 5th Engineering STEAM program and its description

    The improvement of CT practices by adding the activity in science STEAM program

    The improvement of CT practices by adding the activity in engineering STEAM program

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