Harrison Pearl
Samuel Arrants
ABSTRACT
Familiarity with the construction, test, and refinement of computational algorithms is of critical importance to many disciplines in the 21st century. We introduce a novel learning environment that lowers the threshold to participation in algorithmic practices including using functions to transform input, using conditionals to selectively transform or manipulate input, creating simple and complex algorithms, and testing and debugging algorithms to iteratively improve them. Our learning environment leverages VR technology and principles of embodied cognition that prioritize "hands in" learning. Instead of creating algorithms through traditional computational programming (which often renders the structure and components of an algorithm opaque), students using our technology build "concrete algorithms" in the form of a virtual Rube Goldberg-type machine that makes the algorithm's structure, components, and functioning visible.
- NGSS Lead States (2013) Next generation science standards: for states, by states. The National Academies Press, Washington, DC.Google Scholar
- Brennan, K., & Resnick, M. (2012, April). New frameworks for studying and assessing the development of computational thinking. In Proceedings of the 2012 annual meeting of the American Educational Research Association, Vancouver, Canada (Vol. 1, p. 25).Google Scholar
- Selby, C., & Woollard, J. (2013). Computational thinking: the developing definition.Google Scholar
- Wing, J. M. (2006). Computational thinking. Communications of the ACM, 49(3), 33--35.Google ScholarDigital Library
- Weintrop, D., Beheshti, E., Horn, M., Orton, K., Jona, K., Trouille, L., & Wilensky, U. (2016). Defining computational thinking for mathematics and science classrooms. Journal of Science Education and Technology, 25(1), 127--147.Google ScholarCross Ref
- Wing, J. M. (2008). Computational thinking and thinking about computing. Philosophical Transactions of the Royal Society of London A: Mathematical. Physical and Engineering Sciences, 366(1881), 3717--3725.Google ScholarCross Ref
- Bell, T., Alexander, J., Freeman, I., & Grimley, M. (2009). Computer science unplugged: School students doing real computing without computers. The New Zealand Journal of Applied Computing and Information Technology, 13(1), 20--29.Google Scholar
- Grover, S., Jackiw, N., & Lundh, P. (2019). Concepts before coding: non-programming interactives to advance learning of introductory programming concepts in middle school, Computer Science Education, 29:2-3, 106-135 Google ScholarCross Ref
- Peel, A., Sadler, T. D., & Friedrichsen, P. Learning natural selection through computational thinking: Unplugged design of algorithmic explanations. Journal of Research in Science Teaching.Google Scholar
- Papert, S. (1980). Mindstorms: Children, computers, and powerful ideas. Basic Books, Inc.Google ScholarDigital Library
- Smith III, J. P., disessa, A. A., & Roschelle, J. (1994). Misconceptions reconceived: A constructivist analysis of knowledge in transition. The journal of the learning sciences, 3(2), 115--163.Google Scholar
- Edwards, L. D. (1995). Microworlds as representations. In Computers and exploratory learning (pp. 127--154). Springer, Berlin, Heidelberg.Google ScholarCross Ref
- White, B. Y., & Frederiksen, J. R. (1998). Inquiry, modeling, and metacognition: Making science accessible to all students. Cognition and instruction, 16(1), 3--118.Google Scholar
- Nathan, M. J., & Walkington, C. (2017). Grounded and embodied mathematical cognition: Promoting mathematical insight and proof using action and language. Cognitive Research: Principles and Implications, 2(1), 9.Google ScholarCross Ref
- Abrahamson, D., & Trninic, D. (2011). Toward an embodied-interaction design framework for mathematical concepts. In P. Blikstein & P. Marshall (Eds.), Proceedings of the 10th Annual Interaction Design and Children Conference (IDC 2011), Ann Arbor, MI, June 20-23 (Vol. Full papers], pp. 1--10). IDC.Google ScholarDigital Library
- Howison, M., Trninic, D., Reinholz, D., & Abrahamson, D. (2011, May). The Mathematical Imagery Trainer: from embodied interaction to conceptual learning. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (pp. 1989--1998). ACM.Google ScholarDigital Library
- Trninic, D., & Abrahamson, D. (2013). Embodied interaction as designed mediation of conceptual performance. In D. Martinovic, V. Freiman, & Z. Karadag (Eds.), Visual mathematics and cyberlearning (Mathematics education in the digital era, Vol. 1, pp. 119--139). New York: Springer.Google ScholarCross Ref
Index Terms
- The AL Goldberg machine: a virtual environment for engaging learners in algorithmic practices
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