How should computational thinking be integrated into K-12 classrooms?
Computational thinking (CT) is a set of problem-solving approaches used by computer scientists to break down complex challenges and offer solutions. While the origins of CT are in computer science (CS), the concepts involved in computational thinking can be used as a 21st century approach to problem-solving across all subject areas. Using four foundational competencies – abstraction, decomposition, pattern recognition, and testing – the “power and utility” of computational thinking can and should be used by students of all disciplines.[i] In other words, advocates of CT suggest that rather than using CT to only solve computational problems, students should use computational thinking to solve problems in science, art, sociology, economics, and more.[ii]
CT has recently become an important initiative in educational research, policy, and practice. Seen as “a new literacy of the 21st century”[iii] and “a critical skill for today’s world”[iv], broad consensus now exists that computational thinking is an ability that everyone, not just computer scientists, should develop to excel in a world and workforce that is increasingly integrated with technology.[v] Embracing CT can lead to an increase in creativity as students move from being users of technology to becoming producers of new tools and new technologies.[vi]
The sections below examine some key findings from the research on computational thinking, including its evolution and growth as a field, the challenges and importance of integration, and how to support teachers in implementing CT in the classroom.
Computational Thinking as an Evolving Field
Computational thinking has varying definitions and methods of assessment.
As the field deliberates definitions for computational thinking, practitioners must make decisions about how to define CT as a set of skills and knowledge, deciphering “what counts” in order for students to develop these competencies. Definitions and frameworks for CT continue to evolve as research and practitioners glean insights about its application across disciplines and developmental levels in K-12 learning environments. However, because the field continues to lack a consistent definition for CT, the work of defining CT for educators and students in particular contexts is left to educational leaders and/or practitioners themselves.
A related challenge is determining how to assess computational thinking skills. Researchers have used different methods to evaluate computational thinking including questionnaires and surveys[vii], interviews and observations with participants[viii], and project-related tasks.[ix] Without more effective and consistent assessment measures that are tied to articulated and grade-appropriate standards, practitioners are challenged to determine the effectiveness of CT interventions in their schools and communities.
Equitable research is crucial to ensure that all students benefit from learning computational thinking.
As a relatively new and emerging field of research, the opportunity to develop a body of knowledge around providing more equitable CS/CT learning opportunities to students is now and the need is urgent. Ensuring that historically underrepresented populations are included in research studies is one place to start.[x] Researchers, some scholars suggest, should be more diligent in comprehensively reporting their study methods, including information about student ages, demographics, and prior experience using digital technology. However, we should not stop at participant demographics because interventions, and the way that certain participants are served, can still be grossly inequitable. For example AP Computer Science is likely a unique experience for different students based on teacher, context, and curricula decisions. Therefore, demographic enrollment in AP CS may not depict a complete picture of a systemic problem. Researchers have an obligation to report information beyond demographics that are important to inform equity initiatives, such as recruitment efforts, curricular activities, pedagogical approaches, resources and tools, community involvement, and student self-efficacy.
As researchers work to define CT, and develop aligned assessment and learning progressions, they should also work to understand how some definitions may exclude certain populations of learners (e.g., English language learners or neurodiverse learners) by limiting access to those with certain background experiences, linguistic resources, abilities, or technological tools. For research to be valuable to and actionable in practice, contextual information that allows the reader to understand why the implementation and outcomes occurred as they did and how comparable the reader’s own context is to the study setting is necessary.
Computational Thinking Integration
Schools can integrate computational thinking into any subject area.
Educational systems need to rethink opportunities available to students to engage in CS/CT and to remove systemic barriers. One path forward is a focus on integrating CT into multiple subjects beginning in the early grades. Students need more consistent, cumulative, and competency-based approaches to opportunities to learn CT, not a reliance on just a few standardized courses. Traditional computer science classrooms include computational thinking skills; however, these courses are typically only offered as elective courses and only in the upper grades. Not all students have an opportunity to take them. Several researchers have proposed frameworks and activities for computational thinking in the younger grades.[xi]
Computational thinking must be accessible, especially to those students historically underrepresented in computer science.
Historically underrepresented students have disproportionately elected not to enroll in computer science courses even when they are available. In 2015, girls comprised only 22 percent of students that took the AP Computer Science exam and African American or Latino students comprised only 13 percent.[xii] This lack of diversity is not due to the lack of ability or interest in computing among women and people of color.[xiii] Rather, stereotypes about who should excel in computer science lead to inequitable tracking of young women and students of color out of computer science courses. Fewer and weaker opportunities for computer science learning are presented to students in under-resourced schools, compared to their peers in wealthier schools.
Schools and districts are leveraging many innovative strategies to increase access to CS learning opportunities for all students. Notably, these strategies not only aim to increase opportunities to engage historically underrepresented students in computer science, but also seek to change how and why students are engaging in CS. Most projects take advantage of the integrative nature of CT, leveraging computing as a means to engage in more familiar topics, such as music or storytelling, which may build students’ attitudes and confidence toward computer science.
Table 1. CS Integration Strategies
|CS/CT Integration Strategy||Description||Example(s)|
|Computing Across the Disciplines||Computational thinking is integrated in all subjects such as math, science, language arts and social studies.||Bootstrap
CS First: Storytelling with Scratch
|Broadening Participation and Labor Empowerment||CS initiatives recruit and serve student populations historically underrepresented in computer science.||Black Girls Code
Girls Who Code
The Knowledge House
|Partnership between K-12 and Higher Ed||One partnership between DC Public Schools, Howard University, Exploring Computer Science (ECS), and Google seeks to increase DC high school student exposure to CS and the number of DC teachers implementing the ECS course.||Partnership for Early Engagement in Computer Science|
Table 1. CS integration strategies adapted from Santo, Vogel & Ching (2019).[xiv]
Supporting Educators to Implement Computational Thinking
Computational thinking has a wide range of activities and curricula and educators may need help with selection and integration.
Educators seeking to integrate CT must deliberate between a number of curricula and resources available. Countless curricula with suggested activities are available to integrate CS/CT at different grade bands. Some curricula, such as Code.org and Codecademy, support students in computer science learning, while others, such as Bootstrap and Project Lead the Way, support students’ interdisciplinary learning with CS/CT. There is not much information to help educators determine which programs will work best in a particular context (e.g., grade band, community context, student background, different ability levels among students). Examining priorities and motivations for CT integration within communities is also an essential part of the process of selecting appropriate and relevant instructional materials.[xv]
Studies also have suggested that teachers are challenged to identify opportunities to integrate CT within existing curriculum and to find resources and support to integrate CT in the ways that they plan and imagine.[xvi] As policy documents increasingly require or expect teachers to integrate CT into core subjects, teachers need support to recognize and realize opportunities that integrate CT in ways that enhance disciplinary learning, as opposed to CT connections that do not consider its additional value to content learning.[xvii]
Educators need more training, support, and experience with computer science.
Many teachers do not have strong computer science backgrounds. Typically, professionals with computer science skills do not enter the teaching profession; perhaps because demand for these skills outside of schools yield greater financial compensation. Educational leaders and teacher educators are challenged to design and implement opportunities for teachers to learn computer science and support them to integrate it into their instruction. A lack of teacher credentialing pathways and professional development leaves teachers ill equipped to teach computer science, particularly in innovative ways that engage traditionally underrepresented students.[xviii]
That said, research has shown that with professional development, teachers are able to “improve the basic ideas of computational thinking…in a relatively short period of time” as well as develop increased confidence in their abilities to learn and teach computational thinking.[xix] Studies have illustrated effective learning of computational thinking by teachers through exercises within their teacher education program[xx] and an extended professional development program.[xxi] Experts suggest designing and implementing professional learning opportunities for pre-service and in-service teachers to integrate computational thinking in K-12 classrooms.[xxii] In other words, strengthening and supporting teachers’ skills and knowledge about computational thinking is a necessary and achievable goal.
[i] Yadav, A., Hong, H., & Stephenson, C. (2016). Computational thinking for all: Pedagogical approaches to embedding a 21st century problem solving in K-12 classrooms. TechTrends, 60, 565–568. https://doi.org/10.1007/s11528-016-0087-7.
[ii] Hemmendinger, D. A Plea for Modesty. ACM Inroads (2010) 1(2). 4-7.
[iii] Wing, J. (2011). Research notebook: Computational thinking—What and why. The Link Magazine, 20–23.
[iv] Executive Office of the President. (2018). Charting A Course for Success: America’s Strategy for STEM Education. Retrieved from https://www.whitehouse.gov/wp-content/uploads/2018/12/STEM-Education-Strategic-Plan-2018.pdf
[v] Angevine, C., Cator, K., Roschelle, J., Thomas, S. A., Waite, C., & Weisgrau, J. (2017). Computational Thinking for a Computational World.
[vi] Yadav, A., Mayfield, C., Zhou, N., Hambrusch, S., & Korb, J. T. (2014). Computational thinking in elementary and secondary teacher education. ACM Transactions on Computing Education (TOCE), 14(1), 5.
[vii] Atmatzidou, S., & Demetriadis, S. (2016). Advancing students’ computational thinking skills through educational robotics: A study on age and gender relevant differences. Robotics and Autonomous Systems, 75, 661–670.; Denner, J., Werner, L., Campe, S., & Ortiz, E. (2014). Pair programming: Under what conditions is it advantageous for middle school students? Journal of Research on Technology in Education, 46(3), 277–296.
[viii] Israel, M., Pearson, J. N., Tapia, T., Wherfel, Q. M., & Reese, G. (2015). Supporting all learners in school-wide computational thinking: A cross-case qualitative analysis. Computers & Education, 82, 263–279.
[ix] Denner, J., Werner, L., Campe, S., & Ortiz, E. (2014). Pair programming: Under what conditions is it advantageous for middle school students? Journal of Research on Technology in Education, 46(3), 277–296.; Werner, L., Denner, J., Campe, S., & Kawamoto, D. C. (2012). The fairy performance assessment: Measuring computational thinking in middle school. 215–220. ACM.
[x] Brown, C. S., Mistry, R. S., & Yip, T. (2019). Moving from the Margins to the Mainstream: Equity and Justice as Key Considerations for Developmental Science. Child Development Perspectives.
[xi] Angeli, C., Voogt, J., Fluck, A., Webb, M., Cox, M., Malyn-Smith, J., & Zagami, J. (2016). A K-6
computational thinking curriculum framework: Implications for teacher knowledge. Journal of Educational Technology & Society, 19(3).; Duncan, C., & Bell, T. (2015). A pilot computer science and programming course for primary school students. 39–48. ACM.; Lee, I., Martin, F., & Apone, K. (2014). Integrating computational thinking across the K–8 curriculum. Acm Inroads, 5(4), 64–71.; Seiter, L., & Foreman, B. (2013). Modeling the learning progressions of computational thinking of primary grade students. 59–66. ACM.
[xii] Smith, M. (2016, January 30). Computer Science For All. Retrieved from https://obamawhitehouse.archives.gov/blog/2016/01/30/computer-science-all
[xiii] Pinkard, N., Erete, S., Martin, C. K., & McKinney de Royston, M. (2017). Digital Youth Divas: Exploring narrative-driven curriculum to spark middle school girls’ interest in computational activities. Journal of the Learning Sciences, 26(3), 477–516.; Ryoo, J. J. (2019). Pedagogy that supports computer science for all. ACM Transactions on Computing Education (TOCE), 19(4), 36.
[xiv] Santo, R., Vogel, S., & Ching, D. (2019). CS for What? Diverse Visions of Computer Science Education in Practice.
[xv] Santo, R., Vogel, S., & Ching, D. (2019). CS for What? Diverse Visions of Computer Science Education in Practice.
[xvi] Ketelhut, D. J., Mills, K., Hestness, E., Cabrera, L., Plane, J., & McGinnis, J. R. (2019). Teacher change following a professional development experience in integrating computational thinking into elementary science. Journal of Science Education and Technology, 1–15.; Yadav, A., Gretter, S., Hambrusch, S., & Sands, P. (2016). Expanding computer science education in schools: Understanding teacher experiences and challenges. Computer Science Education, 26(4), 235–254.
[xvii] diSessa, A. A. (2018). Computational literacy and “the big picture” concerning computers in mathematics education. Mathematical thinking and learning, 20(1), 3-31.
[xviii] Margolis, J. (2010). Stuck in the shallow end: Education, race, and computing. MIT Press.; Ryoo, J. J. (2019). Pedagogy that supports computer science for all. ACM Transactions on Computing Education (TOCE), 19(4), 36.
[xix] Bower, M., Wood, L. N., Lai, J. W., Howe, C., Lister, R., Mason, R., Highfield, K., & Veal, J. (2017). Improving the Computational Thinking Pedagogical Capabilities of School Teachers. Australian Journal of Teacher Education, 42(3). http://dx.doi.org/10.14221/ajte.2017v42n3.4
[xx] Adler, R. F. and Kim H. (2018). Enhancing future K-8 teachers’ computational thinking skills through modeling and simulations. Education and Information Technologies, 23(4), 1501-1514. https://doi.org/10.1007/s10639-017-9675-1; Bower, M., Wood, L. N., Lai, J. W., Howe, C., Lister, R., Mason, R., Highfield, K., & Veal, J. (2017). Improving the Computational Thinking Pedagogical Capabilities of School Teachers. Australian Journal of Teacher Education, 42(3). http://dx.doi.org/10.14221/ajte.2017v42n3.4 ; Yadav, A., Mayfield, C., Zhou, N., Hambrusch, S., & Korb, J. T. (2014). Computational thinking in elementary and secondary teacher education. ACM Transactions on Computing Education (TOCE), 14(1), 5.
[xxi] Ketelhut, D. J., Mills, K., Hestness, E., Cabrera, L., Plane, J., & McGinnis, J. R. (2019). Teacher change following a professional development experience in integrating computational thinking into elementary science. Journal of Science Education and Technology, 1–15.
[xxii] Ketelhut, D. J., Mills, K., Hestness, E., Cabrera, L., Plane, J., & McGinnis, J. R. (2019). Teacher change following a professional development experience in integrating computational thinking into elementary science. Journal of Science Education and Technology, 1–15.; Yadav, A., Hong, H., & Stephenson, C. (2016). Computational thinking for all: Pedagogical approaches to embedding a 21st century problem solving in K-12 classrooms. TechTrends, 60, 565–568. https://doi.org/10.1007/s11528-016-0087-7