Review of the Literature
Introduction
Calls to change education are prevalent throughout society. The majority of the conversation centers on changing workforce needs. As global economies shift from primarily industrial to technological economies, all parties can agree that students need to be well prepared to perform in these workplaces. Many educators promote a transition from a largely lecture-based, information-transfer educational model to a more experiential model that more reflects the types of work that will be experienced now and in the future. The push to move to a more constructivist educational model has been discussed for over 100 years. The objective of this review is to explore the foundations of constructivist models of education, its impact on computational thinking, and how it applies to twenty-first century needs. |
|
Constructivist Theory in the 20th Century
John Dewey (1916)proposed that this an adherence to very specific goals and outcomes created a separation between the activity and student engagement. He further explained that the attainment of the goal by the student becomes more important than the learning gained in reaching that goal. Instead, Dewey claimed, students should have an active role in their learning. Aliya Sikandar(2015) explained that Dewey’s philosophy linked a student’s life and experiences closely together. When education was tied to life experiences, learning was more meaningful and sustaining.He believed that learning was social, and children learn best in the social environments where they could take part in their learning. In this manner, children would develop educationally by building upon prior knowledge through activities and experiences. (Dewey, 1916; Sikandar,2015).
Glassmanet al (2000) explained thatFin order for students to build, or construct, their learning Dewey maintained that students must engage in activities more consistent with adult life. This construction of knowledge through practical applications in real-world activities would better teach children than traditional methods. (Glassman & Whaley, 2000). Wells (2010) furtherexplained that Rrote memorization created a disdain for learning and encouraged shallow thinking. In light of the philosophy that learning was a Dewey believed that learning was a social activity, Dewey believed learning and was enhanced when students collaborated in the process (Wells, 2010)(Glassman & Whaley, 2000; Wells, 2010).
By the mid-1900s, researcher Ralph Tyler’s work in cognition, education, and literacy, supported Dewey’s belief in learning through inquiry (Wells, 2010). Further work by Lev Vygotsky, expanded on the importance of the social aspect of learning(Glassman & Whaley, 2000; Wells, 2010). The work of both researchers demonstrated the impact that collaboration and social interaction had in the learning process. As students worked together to solve a challenge, they internalized what they were learning and gave it more meaning. This meaning was then able to be transferred to new situations (Powell & Kalina, 2009).(Wells, 2010; Wing, 2010).
In the middle of the 20thCentury Jean Piaget developed the concept of schema and cognitive development(McLeod, 2018).He did not believe that intelligence was a fixed value but rather it was flexible and that all children could learn. In support of this introduced the concept of schemas. He defined schema as, “a cohesive, repeatable action sequence possessing component actions that are tightly interconnected and governed by a core meaning”(McLeod, 2018)(McLeod, 2018). In essence, this was a way of building knowledge. Schemas were building blocks of knowledge that students could use in constructing different ways to make meaning of the world around them. According to Piaget, learning was either when a student developed a new schema or used the schema in a different context. He referred to this use of a schema in a different context as adaptation (Huitt & Hummel, 2003). Adaptation takes place in different phases: assimilation, accommodation, and equilibrium..It is Assimilation is when the student uses a schema in a new situation. If the schema does not fit the new situation then accommodation will take place. Accommodation is the changing of the schema to fit to the new situation or finding a new schema for the original situation. If the existing schemas can accommodate most new situations, then the child is said to be at equilibrium. If the child is not at equilibrium, then a frustration can grow it will cause the child to find and restore balance and develop new learning and adjust the schemas to fit. theprocessof moving through these that will drive accommodation adaptationand develop new learning in the student (McLeod, 2018).
Application of Technology in Constructivist Models
Seymoure Papert was a mathematician and MIT professor who collaborated with Piaget (Seymore papert, 1999). (Stager, 2016). In the 1960s he proposed the concept of a one-to-one educational system where every child had an inexpensive computer to use for their education. Building upon constructivist educational theory and his belief that leveraging technology can improve student outcomes, Papert invented the Logo programming language (Logo Foundation, 2015). Logo was a language that controlled a robot called a turtle by giving it simple commands of distance and direction. In Papert’s research, he would work with children in math using Logo and the turtle.(Papert & Harel, 1991). For example, he would mark a triangle on the floor with tape. The children were instructed to walk along the tape and count their steps. The children within go and program that number of steps to make the turtle move an equivalent distance. At the vertices, the children would turn. They would program the turtle to make a similar turn. Through this type of discovery, the children learned about the concept of complementary anglesthrough experimentationand exploration. This method of instruction was very successfuland.Logo was used in schools through the 1980s(Logo Foundation, 2015).
While at MIT, Papert helped found the MIT Media Lab. Mitch Resnick joined worked with Papert at thetheMIT Media Lab later and worked with Pappert and (Mitchel resnick, 2018). Resnickeventually became the LEGO Papert Professor of Learning Research (MIT, 2018)at the MIT Media Lab. While at MIT, Resnick created the Lifelong Kindergarten research group and developed the Scratch programming language. Resnick’s work with Scratch continued what Papert began with Logo.
Scratch is a block style programming languagethat can be used by students as early as kindergarten and well into college(Merrill, 2017). Users connect blocks of code in a logical sequence to achieve a goal or solve a problem. Scratch can be used by students as early as kindergarten and well into college.One of the original goals of Scratch was to help people learn about coding and problem-solving in a friendlier manner than traditional text-based programming languages(Kalelioglu & Gulbahar, 2014).The aim of teaching people programming is not to turn everybody into a professional programmer, just as the aim of teaching English-Language Arts (ELA) to everyone is not to make all people professional writers. ELA teaches communication it teaches people to organize their thoughts into better express themselves. Similarly, programming teaches people how to analyze and solve problems. Programming also allows people to communicate and share their ideas in a new and creative fashion.
Scratch is a way for teachers to implement coding and computer science principles along with computational thinking into the classroom to allow students to work on projects in a new way. It allows students to construct new learning, to rearrange it, and to use it in new ways. Through coding students have been able to spend more time working on a problem so that they can make deeper connections. This time allotment has come from students being passionate about the problems they are solving and excited to overcome the challenges (Merrill, 2017)( Kalelioglu & Gulbahar, 2014; Merrill, 2017).
Challenges from Technological Advances
In the 90s Richard Paul and Dillon Beach (1995) made observations about the accelerating pace of change and the increasing complexity of the world we were living in. They noted that these facts created new demands for adaptation and quick problem-solving. Furthermore, they observed that the problems being solved were increasingly requiring new methods for solving the problems (Paul & Beach, 1995). Twenty years later, Charles Vest (2007), President of the National Academy of Engineering, addressed this same situation at his annual conference:
John Dewey (1916)proposed that this an adherence to very specific goals and outcomes created a separation between the activity and student engagement. He further explained that the attainment of the goal by the student becomes more important than the learning gained in reaching that goal. Instead, Dewey claimed, students should have an active role in their learning. Aliya Sikandar(2015) explained that Dewey’s philosophy linked a student’s life and experiences closely together. When education was tied to life experiences, learning was more meaningful and sustaining.He believed that learning was social, and children learn best in the social environments where they could take part in their learning. In this manner, children would develop educationally by building upon prior knowledge through activities and experiences. (Dewey, 1916; Sikandar,2015).
Glassmanet al (2000) explained thatFin order for students to build, or construct, their learning Dewey maintained that students must engage in activities more consistent with adult life. This construction of knowledge through practical applications in real-world activities would better teach children than traditional methods. (Glassman & Whaley, 2000). Wells (2010) furtherexplained that Rrote memorization created a disdain for learning and encouraged shallow thinking. In light of the philosophy that learning was a Dewey believed that learning was a social activity, Dewey believed learning and was enhanced when students collaborated in the process (Wells, 2010)(Glassman & Whaley, 2000; Wells, 2010).
By the mid-1900s, researcher Ralph Tyler’s work in cognition, education, and literacy, supported Dewey’s belief in learning through inquiry (Wells, 2010). Further work by Lev Vygotsky, expanded on the importance of the social aspect of learning(Glassman & Whaley, 2000; Wells, 2010). The work of both researchers demonstrated the impact that collaboration and social interaction had in the learning process. As students worked together to solve a challenge, they internalized what they were learning and gave it more meaning. This meaning was then able to be transferred to new situations (Powell & Kalina, 2009).(Wells, 2010; Wing, 2010).
In the middle of the 20thCentury Jean Piaget developed the concept of schema and cognitive development(McLeod, 2018).He did not believe that intelligence was a fixed value but rather it was flexible and that all children could learn. In support of this introduced the concept of schemas. He defined schema as, “a cohesive, repeatable action sequence possessing component actions that are tightly interconnected and governed by a core meaning”(McLeod, 2018)(McLeod, 2018). In essence, this was a way of building knowledge. Schemas were building blocks of knowledge that students could use in constructing different ways to make meaning of the world around them. According to Piaget, learning was either when a student developed a new schema or used the schema in a different context. He referred to this use of a schema in a different context as adaptation (Huitt & Hummel, 2003). Adaptation takes place in different phases: assimilation, accommodation, and equilibrium..It is Assimilation is when the student uses a schema in a new situation. If the schema does not fit the new situation then accommodation will take place. Accommodation is the changing of the schema to fit to the new situation or finding a new schema for the original situation. If the existing schemas can accommodate most new situations, then the child is said to be at equilibrium. If the child is not at equilibrium, then a frustration can grow it will cause the child to find and restore balance and develop new learning and adjust the schemas to fit. theprocessof moving through these that will drive accommodation adaptationand develop new learning in the student (McLeod, 2018).
Application of Technology in Constructivist Models
Seymoure Papert was a mathematician and MIT professor who collaborated with Piaget (Seymore papert, 1999). (Stager, 2016). In the 1960s he proposed the concept of a one-to-one educational system where every child had an inexpensive computer to use for their education. Building upon constructivist educational theory and his belief that leveraging technology can improve student outcomes, Papert invented the Logo programming language (Logo Foundation, 2015). Logo was a language that controlled a robot called a turtle by giving it simple commands of distance and direction. In Papert’s research, he would work with children in math using Logo and the turtle.(Papert & Harel, 1991). For example, he would mark a triangle on the floor with tape. The children were instructed to walk along the tape and count their steps. The children within go and program that number of steps to make the turtle move an equivalent distance. At the vertices, the children would turn. They would program the turtle to make a similar turn. Through this type of discovery, the children learned about the concept of complementary anglesthrough experimentationand exploration. This method of instruction was very successfuland.Logo was used in schools through the 1980s(Logo Foundation, 2015).
While at MIT, Papert helped found the MIT Media Lab. Mitch Resnick joined worked with Papert at thetheMIT Media Lab later and worked with Pappert and (Mitchel resnick, 2018). Resnickeventually became the LEGO Papert Professor of Learning Research (MIT, 2018)at the MIT Media Lab. While at MIT, Resnick created the Lifelong Kindergarten research group and developed the Scratch programming language. Resnick’s work with Scratch continued what Papert began with Logo.
Scratch is a block style programming languagethat can be used by students as early as kindergarten and well into college(Merrill, 2017). Users connect blocks of code in a logical sequence to achieve a goal or solve a problem. Scratch can be used by students as early as kindergarten and well into college.One of the original goals of Scratch was to help people learn about coding and problem-solving in a friendlier manner than traditional text-based programming languages(Kalelioglu & Gulbahar, 2014).The aim of teaching people programming is not to turn everybody into a professional programmer, just as the aim of teaching English-Language Arts (ELA) to everyone is not to make all people professional writers. ELA teaches communication it teaches people to organize their thoughts into better express themselves. Similarly, programming teaches people how to analyze and solve problems. Programming also allows people to communicate and share their ideas in a new and creative fashion.
Scratch is a way for teachers to implement coding and computer science principles along with computational thinking into the classroom to allow students to work on projects in a new way. It allows students to construct new learning, to rearrange it, and to use it in new ways. Through coding students have been able to spend more time working on a problem so that they can make deeper connections. This time allotment has come from students being passionate about the problems they are solving and excited to overcome the challenges (Merrill, 2017)( Kalelioglu & Gulbahar, 2014; Merrill, 2017).
Challenges from Technological Advances
In the 90s Richard Paul and Dillon Beach (1995) made observations about the accelerating pace of change and the increasing complexity of the world we were living in. They noted that these facts created new demands for adaptation and quick problem-solving. Furthermore, they observed that the problems being solved were increasingly requiring new methods for solving the problems (Paul & Beach, 1995). Twenty years later, Charles Vest (2007), President of the National Academy of Engineering, addressed this same situation at his annual conference:
As we think about the challenges ahead, it is important to remember that students are driven by passion, curiosity, engagement, and dreams. Although we cannot know exactly what they should be taught, we can focus on the environment in which they learn and the forces, ideas, inspirations, and empowering situations to which they are exposed. Despite our best efforts to plan their education, however, to a large extent we simply wind them up, step back, and watch the amazing things they do. In the long run, making universities and engineering schools exciting, creative, adventurous, rigorous, demanding, and empowering milieus is more important than specifying curricular details. (Vest, 2007)
|
Computational Thinking
A primary component of programming is Computational Thinking. These are thought processes iInherent to problem-solving methods (Wing, 2010)(Wing, 2006; Wing, 2010). This particular methodology is very effective for working with the information and processing that for a solution. Computational thinking has been broken down into four basic components: abstraction, decomposition, pattern recognition, and algorithm design (Shute, Sun, & Asbell-Clark, 2017; What is Computational Thinking, 2012). Abstraction is seen as one of the key components for enhancing learning (Lockwood & Mooney, 2017).Abstractions are a way to let one item stand in place for something more complex such as an algorithm or a sequence. It allows the problem solver to deal with larger problems by not worrying about the details or minutia (Wing, 2010).
James Lockwood and Aiden Moody (2017) researched the effects of computational thinking in education. They found that teachers who integrate computational thinking into their curriculum better developed students’ analytical skills. As students’ analytical skills increased there was a corresponding increase in attitudes and confidence. The researchers also found a correlation between computational thinking scores and academic success (Lockwood & Mooney, 2017). Very recent research by Chris Penny (2018) from Westchester University showed a strong correlation between reading and writing scores on the SAT and coding. This correlation was stronger than the relationship between math scores and coding ability. The researchers found that as students got better at programming, their reading and writing skills increased at the same time. The reverse relationship was shown to be true. Increases in reading and writing skills improved coding ability (Foutty, 2018; Foutty, 2018; Wing, 2010).
Applications of Computational Thinking
Research into implementations of Computational Thinking in classroom curriculum starts with providing teachers the requisite knowledge about computational thinking (Voogt, Fisser, Good, Mishra, & Yadav, 2015; Yadav, Mayfield, Zhou, Korb, & Hambrusch, 2014). Difficulties arise in arriving at a common definition. Sources break computational thinking down into four (What is computational thinking, 2012), six (Shute, Sun, & Asbell-Clark, 2017), or as many as nine categories (Voogt, Fisser, Good, Mishra, & Yadav, 2015). Contributing to the difficulty in defining computational thinking is attributed to the trend is that, in most schools, computational thinking is only taught in computer science courses (Yadav, Mayfield, Zhou, Korb, & Hambrusch, 2014).
A basic definition of Computational Thinking is provided and supporting documentation is found in research by Voogt et al. (2015),
A primary component of programming is Computational Thinking. These are thought processes iInherent to problem-solving methods (Wing, 2010)(Wing, 2006; Wing, 2010). This particular methodology is very effective for working with the information and processing that for a solution. Computational thinking has been broken down into four basic components: abstraction, decomposition, pattern recognition, and algorithm design (Shute, Sun, & Asbell-Clark, 2017; What is Computational Thinking, 2012). Abstraction is seen as one of the key components for enhancing learning (Lockwood & Mooney, 2017).Abstractions are a way to let one item stand in place for something more complex such as an algorithm or a sequence. It allows the problem solver to deal with larger problems by not worrying about the details or minutia (Wing, 2010).
James Lockwood and Aiden Moody (2017) researched the effects of computational thinking in education. They found that teachers who integrate computational thinking into their curriculum better developed students’ analytical skills. As students’ analytical skills increased there was a corresponding increase in attitudes and confidence. The researchers also found a correlation between computational thinking scores and academic success (Lockwood & Mooney, 2017). Very recent research by Chris Penny (2018) from Westchester University showed a strong correlation between reading and writing scores on the SAT and coding. This correlation was stronger than the relationship between math scores and coding ability. The researchers found that as students got better at programming, their reading and writing skills increased at the same time. The reverse relationship was shown to be true. Increases in reading and writing skills improved coding ability (Foutty, 2018; Foutty, 2018; Wing, 2010).
Applications of Computational Thinking
Research into implementations of Computational Thinking in classroom curriculum starts with providing teachers the requisite knowledge about computational thinking (Voogt, Fisser, Good, Mishra, & Yadav, 2015; Yadav, Mayfield, Zhou, Korb, & Hambrusch, 2014). Difficulties arise in arriving at a common definition. Sources break computational thinking down into four (What is computational thinking, 2012), six (Shute, Sun, & Asbell-Clark, 2017), or as many as nine categories (Voogt, Fisser, Good, Mishra, & Yadav, 2015). Contributing to the difficulty in defining computational thinking is attributed to the trend is that, in most schools, computational thinking is only taught in computer science courses (Yadav, Mayfield, Zhou, Korb, & Hambrusch, 2014).
A basic definition of Computational Thinking is provided and supporting documentation is found in research by Voogt et al. (2015),
Based on the definitions and core concepts of CT as provided by computer scientists, several definitions have emerged for what CT is in the domain of compulsory schooling (K-12). Key in all these definitions is the focus on the skills, habits and dispositions needed to solve complex problems (e.g. Barr and Stephenson 2011; Grover and Pea 2013; Lee et al. 2011; Sengupta et al. 2013; Wolz et al. 2011) with the help of computing (Wolz et al. 2011) and computers (Grover and Pea 2013; Lee et al. 2011). CT encompasses being able to distinguish several levels of abstraction and apply mathematical reasoning and design-based thinking (Sengupta et al. 2013). Mishra and Yadav (2013) have argued that CT goes beyond typical human computer interactions; instead, they argued that human creativity can be augmented by computational thinking, in particular with the use of automation and algorithmic thinking. Specifically, Mishra and Yadav suggested that computational thinking could move students from being consumers of technology to create new forms of expression build tools and foster creativity. (Voogt, Fisser, Good, Mishra, & Yadav, 2015)
|
Finding a consistent definition for Computational Thinking is key to implementation in core curricula (Cooper, Perez, & Rainey, 2010).
In 2010, The National Council for Research (2010) published the idea that all people are expected to be able to employ computational thinking (National Research Council, 2010). The proceedings from the conference went on to state that computational thinking can be employed in lessons just as any other thinking strategy. To be useful, computational thinking strategies must be modeled by both teachers and curriculum. With proper use in the curriculum and through modeling students can learn to use the strategies in situations outside of the classroom. Optimal employment of computational thinking strategies outside of class exist when students are introduced to the strategies early in their education. The council further stated that computational thinking should be a part of K-12 education. Numerous studies and have been undertaken to research the implementation of computational thinking in the classroom (Bundy, 2007; Cooper, Perez, & Rainey, 2010; Doleck, Bazelais, Lemay, Saxena, & Basnet, 2017; Gretter & Yadav, 2016; Kules, 2016; Lu & Fletcher, 2009; National Research Council, 2010; Wing, 2010; Yadav, Mayfield, Zhou, Korb, & Hambrusch, 2014).
Through the lens of a life-long skill, computational thinking has applications in all disciplines (Yadav, Mayfield, Zhou, Korb, & Hambrusch, 2014). When a social studies class is studying trends and population data (Shapiro, 1972), they are performing analysis. When that class is using deductive reasoning to determine the general principles of a culture, the class is practicing abstraction. The analysis of sentence structures found in English Language Arts classes is pattern recognition. And when the ELA class does a character study from a novel, they are practicing decomposition (Yadav, Mayfield, Zhou, Korb, & Hambrusch, 2014).
Conclusion
Technological advances have not changed the direction that educational leaders have been working on for the last 100 years. The student-centered, practical application approach to education promoted by Dewey in the early 1900s has been extensively researched and investigated through the present. Changes in economies and technology have strengthened the argument for constructivist methods of education pioneered by Papert. The information shown in this review provides support and foundations for implementing computational thinking into core curricula. Embedding coding and computational thinking into core curricula will provide deeper and more authentic learning for students preparing them for the challenges of the 21st century.
References
Brookhard, S. (2010). How to assess higher-order thinking skills in your classroom. Alexandria, VA: ASCD.
Bundy, A. (2007). Computational thinking is pervasive. Journal of Scientific and Practical Computing, 1(2), 1-3. https://pdfs.semanticscholar.org/d3b5/562aa8399ecbdcc40b98108229aa54e12449.pdf
Cooper, S., Perez, L., & Rainey, D. (2010, November). K-12 computational learning. Communications of the ACM, 53(11), pp. 27-29. https://doi.org/10.1145/1839676.1839686
Dewey, J. (1916). Democracy and education. New York, NY: Macmillan.
Doleck, T., Bazelais, P., Lemay, D., Saxena, A., & Basnet, R. (2017). Algorithmic thinking, cooperativity, creativity, critical thinking, and problem solving: exploring the relationship between computational thinking skills and academic performance. Journal of Computers in Education, 4(4), 355-369. https://doi.org/10.1007/s40692-017-0090-9
Foutty, B. (2018, March 13). There's a negation of failure in this beautiful, utopian space, Swift Teacher Podcast. Podcast retrieved from: https://www.swiftteacher.org/podcast/2018/3/12/21-theres-a-negation-of-failure-in-this-beautiful-utopian-space-with-west-chester-university-swift-playgrounds-study-group
Glassman, M., & Whaley, K. (2000). Dynamic aims: The use of long-term projects in early childhood classrooms in light of dewey's educational philosophy. Early Childhood Research & Practice, 2(1), 19. http://ecrp.illinois.edu/v2n1/glassman.html
Gretter, S., & Yadav, A. (2016, September). Computational thinking and media & information literacy: An integrated approach to teaching twenty-first century skills. TechTrends, pp. 510-516. https://doi.org/10.1007/s11528-016-0098-4
Huitt, W., & Hummel, J. (2003). Piaget's theory of cognitive development. Educational Psychology Interactive. http://www.edpsycinteractive.org/topics/cognition/piaget.html
Kalelioglu, F., & Gulbahar, Y. (2014, January). The effects of teaching programming via scratch on problem solving skills: A discussion from learners’ perspective. Informatics in Education, 13(1), 33-50. https://www.mii.lt/informatics_in_education/pdf/INFE232.pdf
Kules, B. (2016, December 27). Computational thinking is critical thinking: Connecting to university discourse, goals, and learning outcomes. Proceedings of the Association for Information Science and Technology, 53(1), 1-6. https://doi.org/10.1002/pra2.2016.14505301092
Lockwood, J., & Mooney, A. (2017). Computational thinking in education: Where does it fit? Maynooth University, Department of Computer Science. Maynooth, Co. Kildare: Maynooth University. https://arxiv.org/pdf/1703.07659.pdf
Logo Foundation. (2015). Logo history. Retrieved from Logo Foundation: http://el.media.mit.edu/logo-foundation/what_is_logo/history.html
Lu, J., & Fletcher, G. (2009). Thinking about computational thinking. Proceedings of the 40th SIGCSE Technical Symposium on Computer Science Education (pp. 260-264). Chattanooga, TN: ACM SIGCSE Bulletin. https://doi.org/10.1145/ pra2.1539024.1508959
McLeod, S. (2018, June 6). Jean piaget's theory of cognitive development. Retrieved from SimplyPsychology: https://www.simplypsychology.org/piaget.html
Merrill, S. (2017, December 7). The future of coding in schools. Retrieved from Edutopia: https://www.edutopia.org/article/future-coding-schools
MIT. (2018). Mitchel resnick. Retrieved from Mit Media Lab: https://www.media.mit.edu/people/mres/overview/
National Research Council. (2010). Report of a workshop on the scope and nature of computational thinking (pp. 1-114). Washington, DC: The National Academies Press. https://www.nap.edu/catalog/12840/report-of-a-workshop-on-the-scope-and-nature-of-computational-thinking
Papert, S. (1999, June 22). Retrieved from papert.org: http://papert.org
Papert, S., & Harel, I. (1991). Situating constructionism. In S. Papert, & I. Harel, Constructionism (p. 518). New York, NY: Ablex Publishing Corporation. http://www.papert.org/articles/SituatingConstructionism.html
Paul, R., & Beach, D. (1995). Accelerating change. Retrieved from The Foundation for Critical Thinking: https://www.criticalthinking.org/pages/accelerating-change/474
Powell, K., & Kalina, C. (2009). Cognitive and social constructivism: Developing tools for an effective classroom. Education, 130(2), 241-250. http://go.galegroup.com/ps/i.do?id=GALE%7CA216181184&v=2.1&u=monash&it=r&p=EAIM&sw=w&asid=28e2938c957e0b4e3191ff89e7607558
Shapiro, P. (1972). After data collection: Coding - an educational research tool. Stanford: Stanford University. https://eric.ed.gov/?id=ED061770
Shute, V., Sun, C., & Asbell-Clark, J. (2017). Demystifying computational thinking. Amsterdam: Educational Research Review. http://dx.doi.org/10.1016/j.edurev.2017.09.003
Sikandar, A. (2015, December). John dewey and his philoisophy of education. Journal of Education and Educational Development, 2(2), 191-203. http://dx.doi.org/10.22555/joeed.v2i2.446
Stager, G. (2016, September 15). Seymour papert (1928–2016): Father of educational computing. Nature, 308. https://doi.org/10.1038%2F537308a
Vest, C. (2007, January 9). Educating engineers for 2020 and beyond. Retrieved from NAE Grand Challenges for Engineering: http://www.engineeringchallenges.org/cms/7126/7639.aspx
Voogt, J., Fisser, P., Good, J., Mishra, P., & Yadav, A. (2015, December). Computational thinking in compulsory education: Towards an agenda for research and practice. Education and Information Technologies, 20(4), 715-725. http://dx.doi.org/10.1007/ j.edurev.s10639-015-9412-6
Wells, A. (2010). An investigation of inquiry-based learning in the inclusive classroom. Manitoba: University of Manitoba. https://umanitoba.ca/faculties/education/media/Wells-10.pdf
What is computational thinking. (2012). Retrieved from Center for Computational Thinking: http://www.cs.cmu.edu/%7ECompThink/
Wheeler, S. (2016, January 23). The pedagogy of john dewey: A summary. Retrieved October 2018, from TeachThought: https://teachthought.com/learning/pedagogy-john-dewey-summary/
Wing, J. (2006, March). Computational thinking. Communications of the ACM, 49(3), 33-35. https://doi.org/10.1145/1118178.1118215
Wing, J. M. (2010). Research notebook: Computational thinking--what and why? Retrieved from The Link: https://www.cs.cmu.edu/link/research-notebook-computational-thinking-what-and-why
Yadav, A., Mayfield, C., Zhou, N., Korb, J., & Hambrusch, S. (2014, March). Computational thinking in elementary and secondary teacher education. ACM Transactions on Computing Education, 14(1), 5:1-5:16. http://dx.doi.org/10.1145/2576872
In 2010, The National Council for Research (2010) published the idea that all people are expected to be able to employ computational thinking (National Research Council, 2010). The proceedings from the conference went on to state that computational thinking can be employed in lessons just as any other thinking strategy. To be useful, computational thinking strategies must be modeled by both teachers and curriculum. With proper use in the curriculum and through modeling students can learn to use the strategies in situations outside of the classroom. Optimal employment of computational thinking strategies outside of class exist when students are introduced to the strategies early in their education. The council further stated that computational thinking should be a part of K-12 education. Numerous studies and have been undertaken to research the implementation of computational thinking in the classroom (Bundy, 2007; Cooper, Perez, & Rainey, 2010; Doleck, Bazelais, Lemay, Saxena, & Basnet, 2017; Gretter & Yadav, 2016; Kules, 2016; Lu & Fletcher, 2009; National Research Council, 2010; Wing, 2010; Yadav, Mayfield, Zhou, Korb, & Hambrusch, 2014).
Through the lens of a life-long skill, computational thinking has applications in all disciplines (Yadav, Mayfield, Zhou, Korb, & Hambrusch, 2014). When a social studies class is studying trends and population data (Shapiro, 1972), they are performing analysis. When that class is using deductive reasoning to determine the general principles of a culture, the class is practicing abstraction. The analysis of sentence structures found in English Language Arts classes is pattern recognition. And when the ELA class does a character study from a novel, they are practicing decomposition (Yadav, Mayfield, Zhou, Korb, & Hambrusch, 2014).
Conclusion
Technological advances have not changed the direction that educational leaders have been working on for the last 100 years. The student-centered, practical application approach to education promoted by Dewey in the early 1900s has been extensively researched and investigated through the present. Changes in economies and technology have strengthened the argument for constructivist methods of education pioneered by Papert. The information shown in this review provides support and foundations for implementing computational thinking into core curricula. Embedding coding and computational thinking into core curricula will provide deeper and more authentic learning for students preparing them for the challenges of the 21st century.
References
Brookhard, S. (2010). How to assess higher-order thinking skills in your classroom. Alexandria, VA: ASCD.
Bundy, A. (2007). Computational thinking is pervasive. Journal of Scientific and Practical Computing, 1(2), 1-3. https://pdfs.semanticscholar.org/d3b5/562aa8399ecbdcc40b98108229aa54e12449.pdf
Cooper, S., Perez, L., & Rainey, D. (2010, November). K-12 computational learning. Communications of the ACM, 53(11), pp. 27-29. https://doi.org/10.1145/1839676.1839686
Dewey, J. (1916). Democracy and education. New York, NY: Macmillan.
Doleck, T., Bazelais, P., Lemay, D., Saxena, A., & Basnet, R. (2017). Algorithmic thinking, cooperativity, creativity, critical thinking, and problem solving: exploring the relationship between computational thinking skills and academic performance. Journal of Computers in Education, 4(4), 355-369. https://doi.org/10.1007/s40692-017-0090-9
Foutty, B. (2018, March 13). There's a negation of failure in this beautiful, utopian space, Swift Teacher Podcast. Podcast retrieved from: https://www.swiftteacher.org/podcast/2018/3/12/21-theres-a-negation-of-failure-in-this-beautiful-utopian-space-with-west-chester-university-swift-playgrounds-study-group
Glassman, M., & Whaley, K. (2000). Dynamic aims: The use of long-term projects in early childhood classrooms in light of dewey's educational philosophy. Early Childhood Research & Practice, 2(1), 19. http://ecrp.illinois.edu/v2n1/glassman.html
Gretter, S., & Yadav, A. (2016, September). Computational thinking and media & information literacy: An integrated approach to teaching twenty-first century skills. TechTrends, pp. 510-516. https://doi.org/10.1007/s11528-016-0098-4
Huitt, W., & Hummel, J. (2003). Piaget's theory of cognitive development. Educational Psychology Interactive. http://www.edpsycinteractive.org/topics/cognition/piaget.html
Kalelioglu, F., & Gulbahar, Y. (2014, January). The effects of teaching programming via scratch on problem solving skills: A discussion from learners’ perspective. Informatics in Education, 13(1), 33-50. https://www.mii.lt/informatics_in_education/pdf/INFE232.pdf
Kules, B. (2016, December 27). Computational thinking is critical thinking: Connecting to university discourse, goals, and learning outcomes. Proceedings of the Association for Information Science and Technology, 53(1), 1-6. https://doi.org/10.1002/pra2.2016.14505301092
Lockwood, J., & Mooney, A. (2017). Computational thinking in education: Where does it fit? Maynooth University, Department of Computer Science. Maynooth, Co. Kildare: Maynooth University. https://arxiv.org/pdf/1703.07659.pdf
Logo Foundation. (2015). Logo history. Retrieved from Logo Foundation: http://el.media.mit.edu/logo-foundation/what_is_logo/history.html
Lu, J., & Fletcher, G. (2009). Thinking about computational thinking. Proceedings of the 40th SIGCSE Technical Symposium on Computer Science Education (pp. 260-264). Chattanooga, TN: ACM SIGCSE Bulletin. https://doi.org/10.1145/ pra2.1539024.1508959
McLeod, S. (2018, June 6). Jean piaget's theory of cognitive development. Retrieved from SimplyPsychology: https://www.simplypsychology.org/piaget.html
Merrill, S. (2017, December 7). The future of coding in schools. Retrieved from Edutopia: https://www.edutopia.org/article/future-coding-schools
MIT. (2018). Mitchel resnick. Retrieved from Mit Media Lab: https://www.media.mit.edu/people/mres/overview/
National Research Council. (2010). Report of a workshop on the scope and nature of computational thinking (pp. 1-114). Washington, DC: The National Academies Press. https://www.nap.edu/catalog/12840/report-of-a-workshop-on-the-scope-and-nature-of-computational-thinking
Papert, S. (1999, June 22). Retrieved from papert.org: http://papert.org
Papert, S., & Harel, I. (1991). Situating constructionism. In S. Papert, & I. Harel, Constructionism (p. 518). New York, NY: Ablex Publishing Corporation. http://www.papert.org/articles/SituatingConstructionism.html
Paul, R., & Beach, D. (1995). Accelerating change. Retrieved from The Foundation for Critical Thinking: https://www.criticalthinking.org/pages/accelerating-change/474
Powell, K., & Kalina, C. (2009). Cognitive and social constructivism: Developing tools for an effective classroom. Education, 130(2), 241-250. http://go.galegroup.com/ps/i.do?id=GALE%7CA216181184&v=2.1&u=monash&it=r&p=EAIM&sw=w&asid=28e2938c957e0b4e3191ff89e7607558
Shapiro, P. (1972). After data collection: Coding - an educational research tool. Stanford: Stanford University. https://eric.ed.gov/?id=ED061770
Shute, V., Sun, C., & Asbell-Clark, J. (2017). Demystifying computational thinking. Amsterdam: Educational Research Review. http://dx.doi.org/10.1016/j.edurev.2017.09.003
Sikandar, A. (2015, December). John dewey and his philoisophy of education. Journal of Education and Educational Development, 2(2), 191-203. http://dx.doi.org/10.22555/joeed.v2i2.446
Stager, G. (2016, September 15). Seymour papert (1928–2016): Father of educational computing. Nature, 308. https://doi.org/10.1038%2F537308a
Vest, C. (2007, January 9). Educating engineers for 2020 and beyond. Retrieved from NAE Grand Challenges for Engineering: http://www.engineeringchallenges.org/cms/7126/7639.aspx
Voogt, J., Fisser, P., Good, J., Mishra, P., & Yadav, A. (2015, December). Computational thinking in compulsory education: Towards an agenda for research and practice. Education and Information Technologies, 20(4), 715-725. http://dx.doi.org/10.1007/ j.edurev.s10639-015-9412-6
Wells, A. (2010). An investigation of inquiry-based learning in the inclusive classroom. Manitoba: University of Manitoba. https://umanitoba.ca/faculties/education/media/Wells-10.pdf
What is computational thinking. (2012). Retrieved from Center for Computational Thinking: http://www.cs.cmu.edu/%7ECompThink/
Wheeler, S. (2016, January 23). The pedagogy of john dewey: A summary. Retrieved October 2018, from TeachThought: https://teachthought.com/learning/pedagogy-john-dewey-summary/
Wing, J. (2006, March). Computational thinking. Communications of the ACM, 49(3), 33-35. https://doi.org/10.1145/1118178.1118215
Wing, J. M. (2010). Research notebook: Computational thinking--what and why? Retrieved from The Link: https://www.cs.cmu.edu/link/research-notebook-computational-thinking-what-and-why
Yadav, A., Mayfield, C., Zhou, N., Korb, J., & Hambrusch, S. (2014, March). Computational thinking in elementary and secondary teacher education. ACM Transactions on Computing Education, 14(1), 5:1-5:16. http://dx.doi.org/10.1145/2576872