Developing Computational Thinking Skills in Elementary School Students
Participate and share : Interactive lecture
Monday, June 24, 4:00–5:00 pm
Sarah Van Loo
Students need to develop computational thinking skills to become content producers in our digitally-driven world. In this session, I will present research-based strategies for supporting struggling learners with computational thinking in computer science and other subjects. Attendees will then brainstorm practical solutions for supporting their own struggling students.
|Audience:||Teachers, Teacher education/higher ed faculty, Library media specialists|
|Attendee devices:||Devices useful|
|Attendee device specification:||Smartphone: Windows, Android, iOS
Laptop: Chromebook, Mac, PC
Tablet: Android, iOS, Windows
|Participant accounts, software and other materials:||Not applicable.|
|Focus:||Digital age teaching & learning|
|Topic:||Computer science and computational thinking|
|Subject area:||Computer science|
|ISTE Standards:||For Educators:
Participants will be able to list and describe at least three distinct intervention strategies for supporting at-risk and struggling learners with computational thinking in computer science and other subjects.
Participants will be able to identify and apply specific strategies that may be useful for supporting struggling students or apply strategies to made-up scenarios if preferred by participants.
Models employed: Universal Design for Learning.
Instructional activities employed: Use of scaffolding techniques. Use of unplugged activities, including but not limited to, those such as can be found at Code.org.
Evidence of success: Students will be able to complete coding projects as assigned, with support.
1. Introduction of myself and my literature review (5 minutes)
2. Overview of the history of the term “computational thinking” and the need to develop computational thinking in all students (5 minutes)
3. Presentation of research and findings, including specific examples of each type of intervention presented - Universal Design for Learning, explicit instruction, student-to-student collaboration, unplugged activities, pair programming, real-world problem solving, game maker platforms, concept maps, comprehensive K-6 computer science curriculum (20 minutes)
4. Workshop time - participants will identify struggling students, then work collaboratively in small groups to identify what supports will benefit those students. Note: I will bring scenarios in case participants are unable to think of specific examples during the workshop time. (25 minutes)
5. Q&A (5 minutes)
As cited in my literature review (APA):
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), 47-57. Retrieved from http://www.jstor.org.proxy1.cl.msu.edu/stable/jeductechsoci.19.3.47
Dwyer, H., Hill, C., Carpenter, S., Harlow, D., & Franklin, D. (2014). Identifying elementary students pre-instructional ability to develop algorithms and step-by-step instructions. Proceedings of the 45th ACM Technical Symposium on Computer Science Education - SIGCSE 14, 511-516. doi:10.1145/2538862.2538905
Flórez, F. B., Casallas, R., Hernández, M., Reyes, A., Restrepo, S., & Danies, G. (2017). Changing a generation’s way of thinking: Teaching computational thinking through programming. Review of Educational Research, 87(4), 834-860. doi:10.3102/0034654317710096
Gibson, J. P. (2012). Teaching graph algorithms to children of all ages. Proceedings of the 17th ACM Annual Conference on Innovation and Technology in Computer Science Education - ITiCSE 12. doi:10.1145/2325296.2325308
Hsu, T., Chang, S., & Hung, Y. (2018). How to learn and how to teach computational thinking: Suggestions based on a review of the literature. Computers & Education, 126, 296-310. doi:10.1016/j.compedu.2018.07.004
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. doi:10.1016/j.compedu.2014.11.022 DEVELOPING COMPUTATIONAL THINKING SKILLS 19
Israel, M., Wherfel, Q. M., Pearson, J., Shehab, S., & Tapia, T. (2015). Empowering K–12 students with disabilities to learn computational thinking and computer programming. TEACHING Exceptional Children, 48(1), 45-53. doi:10.1177/0040059915594790
Koehler, M. J., & Mishra P. (2008). Introducing TPCK. In AACTE Committee on Innovation and Technology (Eds.), Handbook of Technological Pedagogical Content Knowledge (TPCK) for educators (pp. 3–29). New York, NY: Routledge. Retrieved from http://www.punyamishra.com/wp-content/uploads/2008/05/koehler_mishra_08.pdf
Kong, S. (2016). A framework of curriculum design for computational thinking development in K-12 education. Journal of Computers in Education, 3(4), 377-394. doi:10.1007/s40692-016-0076-z
Meyer, A., Rose, D. H., & Gordon, D. (2014). Universal design for learning: Theory and practice. Wakefield, MO: CAST. Retrieved from http://udltheorypractice.cast.org/home?1
Mladenović, M., Boljat, I., & Žanko, Ž. (2017). Comparing loops misconceptions in block-based and text-based programming languages at the K-12 level. Education and Information Technologies, 23(4), 1483-1500. doi:10.1007/s10639-017-9673-3
Papert, S. (1980). Mindstorms: Children, computers, and powerful ideas. New York, NY: Basic Books.
Piaget, J. (1962). The stages of the intellectual development of the child. Bulletin of the Menninger Clinic, 26(3), 120.
Schunck, D. H. (2012). Learning theories: An educational perspective (6th ed.). Boston, MA: Pearson. DEVELOPING COMPUTATIONAL THINKING SKILLS 20
Shute, V. J., Sun, C., & Asbell-Clarke, J. (2017). Demystifying computational thinking. Educational Research Review, 22, 142-158. doi:10.1016/j.edurev.2017.09.003
Touretzky, D. S., Marghitu, D., Ludi, S., Bernstein, D., & Ni, L. (2013). Accelerating K-12 computational thinking using scaffolding, staging, and abstraction. Proceeding of the 44th ACM Technical Symposium on Computer Science Education - SIGCSE 13, 609-614. doi:10.1145/2445196.2445374
Vihavainen, A., Airaksinen, J., & Watson, C. (2014). A systematic review of approaches for teaching introductory programming and their influence on success. Proceedings of the Tenth Annual Conference on International Computing Education Research - ICER 14, 19-26. doi:10.1145/2632320.2632349
Wing, J. M. (2006). Computational thinking. Communications of the ACM, 49(3), 33. doi:10.1145/1118178.1118215