
Robotics and Coding: A Framework for Examining Cognitive Demand
References
- Arbaugh, F., & Brown, C. A. (2005). Analyzing mathematical tasks: A catalyst for change? Journal of Mathematics Teacher Education, 8, 499–536. DOI: 10.1007/s10857-006-6585-3
- Bevan, B. (2017). The promise and the promises of making in science education. Studies in Science Education, 53(1), 75–103. DOI: 10.1080/03057267.2016.1275380
- Bevan, B., Gutwill, J. P., Petrich, M., & Wilkinson, K. (2015). Learning through STEM-rich tinkering: Findings from a jointly negotiated research project taken up in practice. Science Education, 99(1), 98–120. DOI: 10.1002/sce.21151
- Boaler, J., & Staples, M. (2008). Creating mathematical futures through an equitable teaching approach: The case of Railside School. Teachers College Record, 110(3), 608–645. DOI: 10.1177/016146810811000302
- Boston, M. D. (2013). Connecting changes in secondary mathematics teachers’ knowledge to their experiences in a professional development workshop. Journal of Mathematics Teacher Education, 16, 7–31. DOI: 10.1007/s10857-012-9211-6
- Boston, M. D., & Smith, M. S. (2009). Transforming secondary mathematics teaching: Increasing the cognitive demands of instructional tasks used in teachers’ classrooms. Journal for Research in Mathematics Education, 40(2), 119–156. DOI: 10.2307/40539329
- Bransford, J., Vye, N., & Bateman, H. (2002).
Creating high-quality learning environments: Guidelines from research on how people learn . In P. A. Graham & N. Stacey (Eds.), The knowledge economy and postsecondary education: Report of a workshop (pp. 159–198) Washington, D.C.: National Academy Press. DOI: 10.17226/10239 - Bureau of Labor Statistics. (2022, September 8). Employment by detailed occupation.
https://www.bls.gov/emp/tables/emp-by-detailed-occupation.htm - Cai, J., Wang, N., Moyer, J. C., Wang, C., & Nie, B. (2011). Longitudinal investigation of the curricular effect: An analysis of student learning outcomes from the LieCal Project in the United States. International Journal of Educational Research, 50(2), 117–136. DOI: 10.1016/j.ijer.2011.06.006
- Chevalier, M., Giang, C., Piatti, A., & Mondada, F. (2020). Fostering computational thinking through educational robotics: A model for creative computational problem solving. International Journal of STEM Education, 7(1), 1–18. DOI: 10.1186/s40594-020-00238-z
- Chin, C., & Brown, D. E. (2000). Learning in science: A comparison of deep and surface approaches. Journal of Research in Science Teaching, 37(2), 109–138. DOI: 10.1002/(SICI)1098-2736(200002)37:2<;109::AID-TEA3>3.0.CO;2-7
- DeDecker, S., Chouvalova, A., Gordon, K., Clemmer, R., & Vale, J. (2022). Memorization: Friend or foe when solving problems in STEM undergraduate courses. Proceedings of the Canadian Engineering Education Association (CEEA). DOI: 10.24908/pceea.vi.15945
- Doyle, W. (1988). Work in mathematics classes: The context of students’ thinking during instruction. Educational Psychologist, 23, 167–180. DOI: 10.1207/s15326985ep2302_6
- Doyle, W., & Carter, K. (1984). Academic tasks in classrooms. Curriculum Inquiry, 14(2), 129–149. DOI: 10.2307/3202177
- Doyle, W., & Sanford, J. P. (1985). Managing students’ work in secondary classrooms: Practical lessons from a study of classroom tasks (Report No. RDCTE-6193). Austin: Research and Development Center for Teacher Education, University of Texas at Austin.
https://eric.ed.gov/?id=ED271319 - Early, D. M., Rogge, R. D., & Deci, E. L. (2014). Engagement, alignment, and rigor as vital signs of high-quality instruction: A classroom visit protocol for instructional improvement and research. The High School Journal, 97(4), 219–239. DOI: 10.1353/hsj.2014.0008
- Estrella, S., Zakaryan, D., Olfos, R., & Espinoza, G. (2020). How teachers learn to maintain the cognitive demand of tasks through Lesson Study. Journal of Mathematics Teacher Education, 23, 293–310. DOI: 10.1007/s10857-018-09423-y
- Grove, N. P., & Bretz, S. L. (2012). A continuum of learning: from rote memorization to meaningful learning in organic chemistry. Chemistry Education Research and Practice, 13(3), 201–208. DOI: 10.1039/C1RP90069B
- Grouws, D. A., Tarr, J. E., Chávez, Ó., Sears, R., Soria, V. M., & Taylan, R. D. (2013). Curriculum and implementation effects on high school students’ mathematics learning from curricula representing subject-specific and integrated content organizations. Journal for Research in Mathematics Education, 44(2), 416–463. DOI: 10.5951/jresematheduc.44.2.0416
- Halfin, H. H. (1973). Technology: A process approach (Unpublished doctoral dissertation). West Virginia University, Morgantown.
- Hartman, J. R., & Nelson, E. A. (2021). A paradigm shift: The implications of working memory limits for physics and chemistry instruction. DOI: 10.48550/arXiv.2102.00454
- Henningsen, M., & Stein, M. K. (1997). Mathematical tasks and student cognition: Classroom-based factors that support and inhibit high-level mathematical thinking and reasoning. Journal for Research in Mathematics Education, 28(5), 524–549. DOI: 10.5951/jresematheduc.28.5.0524
- Hill, R. (1997). The design of an instrument to assess problem solving activities in technology education. Journal of Technology Education, 9(1), 31–46. DOI: 10.21061/jte.v9i1.a.3
- Hill, R. B. (2006). New perspectives: Technology teacher education and engineering design. Journal of Industrial Teacher Education, 43(3),
45 . - International Technology and Engineering Educators Association. (n.d.). Technology and Engineering Education Collegiate Association (TEECA). Retrieved from
https://www.iteea.org/About/Leadership/40079/TEECA.aspx#tabs - International Technology and Engineering Educators Association. (2020). Standards for technological and engineering literacy: The role of technology and engineering in STEM education.
www.iteea.org/STEL.aspx - International Technology and Engineering Educators Association. (2021). Engineering byDesign.
https://www.iteea.org/File.aspx?id=111278&v=5c360a06 - Kang, H., Windschitl, M., Stroupe, D., & Thompson, J. (2016). Designing, launching, and implementing high quality learning opportunities for students that advance scientific thinking. Journal of Research in Science Teaching, 53(9), 1316–1340. DOI: 10.1002/tea.21329
- Merisio, C., Bozzi, G., Datteri, E. (2021).
There is no such thing as a “Trial and error strategy” . In M. Malvezzi, D. Alimisis & M. Moro (Eds.), Education in & with Robotics to Foster 21st-Century Skills: Proceedings of EDUROBOTICS 2020 (pp. 190–201). Springer, Cham. DOI: 10.1007/978-3-030-77022-8_17 - Pagano, L. C., Haden, C. A., & Uttal, D. H. (2020). Museum program design supports parent–child engineering talk during tinkering and reminiscing. Journal of Experimental Child Psychology, 200. DOI: 10.1016/j.jecp.2020.104944
- Patton, M. Q. (2015). Qualitative research and evaluation methods (4th ed.). Sage.
- Peters, M. (2015). Using cognitive load theory to interpret student difficulties with a problem-based learning approach to engineering education: a case study. Teaching Mathematics and its Applications: An International Journal of the IMA, 34(1), 53–62. DOI: 10.1093/teamat/hru031
- Plass, J. L., Moreno, R., & Brünken, R. (Eds.) (2010). Cognitive load theory. Cambridge University Press. DOI: 10.1017/CBO9780511844744
- Poce, A., Amenduni, F., & De Medio, C. (2019). From tinkering to thinkering. Tinkering as critical and creative thinking enhancer. Journal of e-Learning and Knowledge Society, 15(2). DOI: 10.20368/1971-8829/1639
- Robins, A., Rountree, J., & Rountree, N. (2003). Learning and teaching programming: A review and discussion. Computer Science Education, 13(2), 137–172. DOI: 10.1076/csed.13.2.137.14200
- Rojewski, J. W., & Hill, R. B. (2014). Positioning research and practice in career and technical education: A framework for college and career preparation in the 21st century. Career and Technical Education Research, 39(2), 137–150. DOI: 10.5328/cter39.2.137
- Schoenfeld, A. H. (2002). Making mathematics work for all children: Issues of standards, testing, and equity. Educational Researcher, 31, 13–25. DOI: 10.3102/0013189X031001013
- Schwandt, T. A. (2015). The SAGE dictionary of qualitative inquiry (4th ed.). Sage. DOI: 10.4135/9781483398969
- Simpson, A., Burris, A., & Maltese, A. (2020). Youth’s engagement as scientists and engineers in an afterschool making and tinkering program. Research in Science Education, 50(1), 1–22. DOI: 10.1007/s11165-017-9678-3
- Smith, M. S., & Stein, M. K. (1998). Reflections on practice: Selecting and creating mathematical tasks: From research to practice. Mathematics Teaching in the Middle School, 3(5), 344–350. DOI: 10.5951/MTMS.3.5.0344
- Stein, M. K., Grover, B. W., & Henningsen, M. (1996). Building student capacity for mathematical thinking and reasoning: An analysis of mathematical tasks used in reform classrooms. American Educational Research Journal, 33(2), 455–488. DOI: 10.3102/00028312033002455
- Stein, M. K., & Kaufman, J. H. (2010). Selecting and supporting the use of mathematics curricula at scale. American Educational Research Journal, 47(3), 663–693. DOI: 10.3102/0002831209361210
- Stein, M. K., & Lane, S. (1996). Instructional tasks and the development of student capacity to think and reason: An analysis of the relationship between teaching and learning in a reform mathematics project. Educational Research and Evaluation, 2(1), 50–80. DOI: 10.1080/1380361960020103
- Sweller, J. (1988). Cognitive load during problem solving: Effects on learning. Cognitive Science, 12(2), 257–285. DOI: 10.1016/0364-0213(88)90023-7
- Sweller, J. (2011).
Cognitive load theory . In Psychology of learning and motivation (Vol. 55, pp. 37–76). Academic Press. DOI: 10.1016/B978-0-12-387691-1.00002-8 - Sweller, J., van Merriënboer, J. J. G., & Paas, F. (1998). Cognitive architecture and instructional design. Educational Psychology Review, 10, 251–296.
https://www.jstor.org/stable/23359412 . DOI: 10.1023/A:1022193728205 - Sztajn, P., Confrey, J., Wilson, P. H., & Edgington, C. (2012). Learning trajectory based instruction: Toward a theory of teaching. Educational Researcher, 41(5), 147–156. DOI: 10.3102/0013189X12442801
- Tarr, J. E., Reys, R. E., Reys, B. J., Chávez, Ó., Shih, J., & Osterlind, S. J. (2008). The impact of middle-grades mathematics curricula and the classroom learning environment on student achievement. Journal for Research in Mathematics Education, 39(3), 247–280. DOI: 10.2307/30034970
- Tekkumru-Kisa, M., Schunn, C., Stein, M. K., & Reynolds, B. (2019). Change in thinking demands for students across the phases of a science task: An exploratory study. Research in Science Education, 49, 859–883. DOI: 10.1007/s11165-017-9645-z
- Tekkumru-Kisa, M., Stein, M. K., & Coker, R. (2018). Teachers’ learning to facilitate high-level student thinking: Impact of a video-based professional development. Journal of Research in Science Teaching, 55(4), 479–502. DOI: 10.1002/tea.21427
- Tekkumru-Kisa, M., Stein, M. K., & Doyle, W. (2020). Theory and research on tasks revisited: Task as a context for students’ thinking in the era of ambitious reforms in mathematics and science. Educational Researcher, 49(8), 606–617. DOI: 10.3102/0013189X20932480
- Tekkumru-Kisa, M., Stein, M. K., & Schunn, C. (2015). A framework for analyzing cognitive demand and content-practices integration: Task analysis guide in science. Journal of Research in Science Teaching, 52(5), 659–685. DOI: 10.1002/tea.21208
- Vossoughi, S., & Bevan, B. (2014). Making and tinkering: A review of the literature. National Research Council Committee on Out of School Time STEM, 67, 1–55.
- Vossoughi, S., Escudé, M., Kong, F., & Hooper, P. (2013). Tinkering, learning & equity in the after-school setting. In Annual FabLearn conference. Palo Alto, CA:
Stanford University . - Walkoe, J. (2015). Exploring teacher noticing of student algebraic thinking in a video club. Journal of Mathematics Teacher Education, 18, 523–550. DOI: 10.1007/s10857-014-9289-0
- Weiss, I. R., & Pasley, J. D. (2004). What is high-quality instruction? Educational Leadership, 61(5), 24–28.
https://eric.ed.gov/?id=EJ716718 - Wicklein, R. C. (1993). Developing goals and objectives for a process-based technology education curriculum. Journal of Industrial Teacher Education 30(3), 66–80.
https://eric.ed.gov/?id=EJ463556 - Yadav, A., Hong, H., & Stephenson, C. (2016). Computational thinking for all: Pedagogical approaches to embedding 21st century problem solving in K-12 classrooms. TechTrends, 60, 565–568. DOI: 10.1007/s11528-016-0087-7
DOI: https://doi.org/10.21061/jte.631 | Journal eISSN: 1045-1064
Language: English
Page range: 7 - 31
Submitted on: Oct 10, 2023
Accepted on: Dec 6, 2023
Published on: Jan 29, 2024
Published by: Virginia Tech
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Keywords:
© 2024 Anna Bloodworth, AnnaMarie Conner, Claire Miller, Lorraine Franco, Timothy Foutz, Roger B. Hill, published by Virginia Tech
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