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Computer Science Curriculum From Ellipsis Education: More Than Just Coding

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Pennsylvania Convention Center, Terrace Ballroom Lobby, Table 35

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Session description

At Ellipsis Education, we understand that computer science is more than just coding. Curriculum from Ellipsis Education includes four lesson types to support holistic and meaningful computer science learning. Explore our unique approach to curriculum design to see how these lessons work together to benefit your students!

Purpose & objective

Our proposal rests on two primary challenges in computer science education. First, computer science education is viewed as having tremendous benefits to learners of all ages. However, the notion of what constitutes 'quality' in computer science education is still rather ambiguous. This leaves practitioners scrambling to define quality on their own, while wrestling to vet a variety of approaches and resources. Second, schools and districts who are interested in supporting computer science education face unique complexities as they determine when its taught, how its taught, and who teaches it. Our curriculum, and thus our proposal, is designed to address both ideas. We are helping to shape narrative around what quality computer science education looks like by providing a curriculum that leverages best practices with a thoughtful design using four lesson types. We deliver the curriculum in such a way that it can be easily implemented in schools, even as they continue making decisions about their unique instructional strategies for computer science.

In this presentation, we will share a poster that highlights how the design of the Ellipsis computer science curriculum anticipates these challenges and supports teachers, schools, and students in high-quality computer science learning. We will share real-life stories to illustrate how the curriculum has enabled teachers' and students' success. We look forward to conversations with ISTE attendees about their current approaches to computer science education and are always happy to serve as a thought partner as they consider expansion opportunities.

Furthermore, as holders of the ISTE seal-of-alignment, we will demonstrate how our lesson support the ISTE student standards.

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Outline

This is a poster presentation so we will follow the ISTE format. Key elements will include sample lessons, examples of student work, a video that will be highlighted in an upcoming documentary on public television, and testimonials from teachers using the curriculum.

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Supporting research

Angeli, C., Voogt, J., Fluck, A., Webb, M., Cox, M., Malyn-Smith, J., et al. (2016). A K-6 computational thinking curriculum framework: Implications for teacher knowledge. Educational Technology & Society, 19(3), 47–57.
Ballard E. D., Haroldson, R. (2021). Analysis of computational thinking in children’s literature for K-6 students: Literature as a non-programming unplugged resource. Journal of Educational Computing Research, 59(8), 1487-1516.
Brennan, K., & Resnick, M. (2012). New frameworks for studying and assessing the development of computational thinking. Paper presented at AERA, Vancouver, BC.
Buitrago Flórez, F., Casallas, R., Hernández, M., Reyes, A., Restrepo, S., and Danies, G.(2017). Changing a generation’s way of thinking: Teaching computational thinking through programming. Review of Educational Research, 87(4), 834–860.
CSTA (2017). K-12 standards. https://www.csteachers.org/page/standards
CSTA (2019). Standards for CS teachers. https://csteachers.org/teacherstandards
Deming, D. J., & Noray, K. L. (2018). STEM careers and technological change. National Bureau of Economic Research, Washington DC.
Desimone, L. M. (2009). Improving impact studies of teachers’ professional development: Toward better conceptualizations and measures. Educational researcher, 38(3), 181-199.
Dou, R., Hazari, Z., Dabney, K., Sonnert, G., & Sadler, P. (2019). Early informal STEM experiences and STEM identity: The importance of talking science. Science Education, 103(3), 623-637.
Ertmer, P. A., & Ottenbreit-Leftwich, A. T. (2010). Teacher technology change: How knowledge, confidence, beliefs, and culture intersect. Journal of Research on Technology in Education, 42, 255–284.
Fessakis, G., Gouli, E., & Mavroudi, E. (2013). Problem solving by 5–6 years old kindergarten children in a computer programming environment: A case study. Computers & Education, 63, 87–97.
Grover, S., & Pea, R. (2013). Computational thinking in K–12: A review of the state of the field. Educational Researcher, 42(1), 38–43.
Grover, S., & Pea, R. (2018). Computational thinking: A competency whose time has come. In S. Sentance, S. Carsten, & E. Barendsen (Eds.), Computer science education: Perspectives and learning (pp. 19–38). London, UK: Bloomsbury.
Kim, A. Y., Sinatra, G. M., & Seyranian, V. (2018). Developing a STEM identity among young women: A social identity perspective. Review of Educational Research, 88(4), 589-625.
Kong, S. C. (2016). A framework of curriculum design for computational thinking development in K-12 education. Journal of Computers in Education, 3(4), 377–394.
Kong, S. C., & Wang, Y.-Q. (2020). Formation of computational identity through computational thinking perspectives development in programming learning: A mediation analysis among primary school students. Computers in Human Behavior, 106, 1-12.
Kong, S. C., Lai, M., & Sun, D. (2020). Teacher development in computational thinking: Design and learning outcomes of programming concepts, practices and pedagogy. Computers and Education, 151, 1-19.
Kong, SC., & Lai, M. (2021). A proposed computational thinking teacher development framework for K-12 guided by the TPACK model. Journal of Computer Education, 1-24.
Lye, S. Y., & Koh, J. H. L. (2014). Review on teaching and learning of computational thinking through programming: What is next for K-12? Computers in Human Behavior, 41, 51–61.
Mason, S. L., & Rich, P. J. (2019). Preparing elementary school teachers to teach computing, coding, and computational thinking. Contemporary Issues in Technology and Teacher Education, 19(4), 790–824.
Mishra, P., & Koehler, M. J. (2006). Technological pedagogical content knowledge: A framework for integrating technology in teacher knowledge. Teachers College Record, 108(6), 1017–1054.
Morales-Chicas, J., Castillo, M., Bernal, I., Ramos, P., & Guzman, B. L. (2019). Computing with relevance and purpose: A review of culturally relevant education in computing. International Journal of Multicultural Education, 21(1), 125-155.
National Academies of Sciences, Engineering, and Medicine (2021). Cultivating interest and competencies in computing: Authentic experiences and design factors. Washington, DC: The National Academies Press.
National Academies of Sciences, Engineering, and Medicine 2018. How people learn II: Learners, contexts, and cultures. Washington, DC: The National Academies Press.
Nouri, J., Zhang, L., Mannila, L., & Norén, E. (2020). Development of computational thinking, digital competence and 21st century skills when learning programming in K-9. Education Inquiry, 11(1), 1-17.
Oda, M., Noborimoto, Y., & Horita, T. (2021). International Trends in K-12 Computer Science Curricula through Comparative Analysis: Implications for the Primary Curricula. International Journal of Computer Science Education in Schools, 4(4), 1-25.
Sadik, O., Ottenbreit-Leftwich, A., & Brush, T. (2020). Secondary computer science teachers’ pedagogical needs. International Journal of Computer Science Education in Schools, 4(1), 33–52.
Sentance, S., & Csizmadia, A. (2017). Computing in the curriculum: Challenges and strategies from a teacher’s perspective. Education and Information Technologies, 22(2), 469–495.
Shute, V. J., Sun, C., & Asbell-Clarke, J. (2017). Demystifying computational thinking. Educational Research Review, 22, 142-158.
Tissenbaum, M., & Ottenbriet-Leftwich, A. (2020). A vision of k-12 computer science education for 2030. Communications of the ACM, 63(5), 42-44.
Webb, M., Davis, N., Bell, T. et al. Computer science in K-12 school curricula of the 2lst century: Why, what and when? Education Information Technologies, 22, 445–468.
Wing, J. M. (2008). Computational thinking and thinking about computing. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 366(1881), 3717-3725.
Wong Lung-Hsiang, Chan Tak-Wai, Chen, W., Chee-Kit, L., Chen, Z., Liao Calvin, C. Y., . . . Wong, S. L. (2020). IDC theory: Interest and the interest loop. Research and Practice in Technology Enhanced Learning, 15(1), 1-17.
Yang, D., Swanson, S. R., Chittoori, B., & Baek, Y. (2018). Work in progress: integrating computational thinking in STEM education through a project-based learning approach. American Society for Engineering. Proceedings of ASEE Annual Conference (Salt Lake City, UT).
Zhang, L., & Nouri, J. (2019). A systematic review of learning computational thinking through Scratch in K-9. Computers & Education, 141, 1-25.
Zilberman, A., & Ice, L. (2021). Why computer occupations are behind strong STEM employment growth in the 2019-29 decade. Beyond the Numbers, 10(1). Bureau of Labor Statistics.

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Session specifications

Topic:
Computer science & computational thinking
Grade level:
PK-12
Skill level:
Beginner
Audience:
Curriculum/district specialists, Principals/head teachers, Teachers
Attendee devices:
Devices not needed
Participant accounts, software and other materials:
None
Subject area:
Computer science, STEM/STEAM
ISTE Standards:
For Students:
Empowered Learner
  • Students understand the fundamental concepts of technology operations, demonstrate the ability to choose, use and troubleshoot current technologies and are able to transfer their knowledge to explore emerging technologies.
Digital Citizen
  • Students engage in positive, safe, legal and ethical behavior when using technology, including social interactions online or when using networked devices.
Innovative Designer
  • Students know and use a deliberate design process for generating ideas, testing theories, creating innovative artifacts or solving authentic problems.