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How Does the Degree of Guidance Support Students’ Metacognitive and Problem Solving Skills in Educational Robotics?

Abstract

Educational robotics (ER) is an innovative learning tool that offers students opportunities to develop higher-order thinking skills. This study investigates the development of students’ metacognitive (MC) and problem-solving (PS) skills in the context of ER activities, implementing different modes of guidance in two student groups (11–12 years old, N1 = 30, and 15-16 years old, N2 = 22). The students of each age group were involved in an 18-h group-based activity after being randomly distributed in two conditions: “minimal” (with minimal MC and PS guidance) and “strong” (with strong MC and PS guidance). Evaluations were based on the Metacognitive Awareness Inventory measuring students’ metacognitive awareness and on a think-aloud protocol asking students to describe the process they would follow to solve a certain robot-programming task. The results suggest that (a) strong guidance in solving problems can have a positive impact on students’ MC and PS skills and (b) students reach eventually the same level of MC and PS skills development independently of their age and gender.

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References

  1. Akin, A., Abaci, R., & Çetin, B. (2007). The validity and reliability of the Turkish version of the metacognitive awareness inventory. Educational Sciences: Theory & Practice, 7(2), 671–678.

    Google Scholar 

  2. Alimisis, D. (2009). Teacher education on robotics-enhanced constructivist pedagogical methods. Αthens: School of Pedagogical and Technological Education.

    Google Scholar 

  3. Alimisis, D. (2014). Educational robotics in teacher education: An innovative tool for promoting quality Educatio. In L. Daniela, I. Lūka, L. Rutka, & I. Žogla (Eds.), Teacher of the 21st Century: Quality Education for Quality Teaching (pp. 14–27). Cambridge: Cambridge scholars publishing.

    Google Scholar 

  4. Anewalt, K. (2002). Experiences teaching writing in a computer science course for the first time. Journal of Computing Sciences in Colleges, 18(2), 346–355.

    Google Scholar 

  5. Atmatzidou, S., & Demetriadis, S. N. (2012). Evaluating the role of collaboration scripts as group guiding tools in activities of educational robotics: Conclusions from three case studies. In IEEE 12th International Conference on Advanced Learning Technologies (ICALT), 2012 (pp. 298-302).

  6. Atmatzidou, S., & Demetriadis, S. (2016). Advancing students’ computational thinking skills through educational robotics: A study on age and gender relevant differences. Robot Auton Syst, 75, 661–670.

    Article  Google Scholar 

  7. Atmatzidou, S., Markelis, I., & Demetriadis, S. (2008). The use of LEGO Mindstorms in elementary and secondary education: Game as a way of triggering learning. In Workshop Proceedings of International Conference on Simulation, Modelling, and Programming for Autonomous Robots (pp. 22-30).

  8. Barkley, E. F., Cross, K. P., & Major, C. H. (2014). Collaborative learning techniques: A handbook for college faculty. Hoboken: John Wiley & Sons.

    Google Scholar 

  9. Barrows, H. S. (1996). Problem-based learning in medicine and beyond: A brief overview. New directions for teaching and learning, 1996(68), 3–12.

    Article  Google Scholar 

  10. Benitti, F. B. V. (2012). Exploring the educational potential of robotics in schools: A systematic review. Comput Educ, 58(3), 978–988.

    Article  Google Scholar 

  11. Bers, M. U. (2007). Project InterActions: A multigenerational robotic learning environment. J Sci Educ Technol, 16(6), 537–552.

    Article  Google Scholar 

  12. Bers, M. U., Flannery, L., Kazakoff, E. R., & Sullivan, A. (2014). Computational thinking and tinkering: Exploration of an early childhood robotics curriculum. Comput Educ, 72, 145–157.

    Article  Google Scholar 

  13. Blanchard, S., Freiman, V., & Lirrete-Pitre, N. (2010). Strategies used by elementary schoolchildren solving robotics-based complex tasks: Innovative potential of technology. Procedia-SocialandBehavioral Sciences, 2(2), 2851–2857.

    Article  Google Scholar 

  14. Brown, A. L. (1978). Knowing when, where, and how to remember: A problem of metacognition. In R. Glaser (Ed.), Advances in instructional psychology (pp. 77–165). Hillsdale: Erlbaum.

    Google Scholar 

  15. Çalik, M., Özsevgeç, T., Ebenezer, J., Artun, H., & Küçük, Z. (2014). Effects of ‘environmental chemistry’ elective course via technology-embedded scientific inquiry model on some variables. J Sci Educ Technol, 23(3), 412–430.

    Article  Google Scholar 

  16. Çalik, M., Ebenezer, J., Özsevgeç, T., Küçük, Z., & Artun, H. (2015). Improving science student teachers’ self-perceptions of fluency with innovative technologies and scientific inquiry abilities. J Sci Educ Technol, 24(4), 448–460.

    Article  Google Scholar 

  17. Castledine, A. R., & Chalmers, C. (2011). LEGO robotics: An authentic problem solving tool? Design and Technology Education, 16(3), 19–27.

    Google Scholar 

  18. Chi, M. T., & Bassok, M. (1989). Learning from examples via self-explanations. In L.B. Resnick (Ed.), Knowing, learning, and instruction: Essays in honor of Robert Glaser (pp. 251–282). Hillsdale: Erlbaum.

  19. Chin, C., & Brown, D. E. (2000). Learning in science: A comparison of deep and surface approaches. J Res Sci Teach, 37(2), 109–138.

    Article  Google Scholar 

  20. Dennison, R. S. (1997). Relationships among measures of metacognitive monitoring. In annual meeting of the American Educational Association, Chicago, IL.

  21. Druin, A., & Hendler, J. A. (2000). Robots for kids: Exploring new technologies for learning. San Francisco: Morgan Kaufmann.

  22. Du Toit, S., & Kotze, G. (2009). Metacognitive strategies in the teaching and learning of mathematics. Pythagoras, 2009(70), 57–67.

    Google Scholar 

  23. Eguchi, A. (2014, July). Robotics as a learning tool for educational transformation.In Proceeding of 4th International Workshop Teaching Robotics, Teaching with Robotics & 5th International Conference Robotics in Education Padova (Italy).

  24. Flavell, J. H. (1979). Metacognition and cognitive monitoring: A new area of cognitive–developmental inquiry. Am Psychol, 34(10), 906.

    Article  Google Scholar 

  25. Fülöp, E. (2015). Teaching problem-solving strategies in mathematics. LUMAT (2013–2015 Issues), 3(1), 37–54.

    Google Scholar 

  26. Gama, C. (2004). Metacognition in interactive learning environments: The reflection assistant model, In Intelligent Tutoring Systems (pp. 668–677). Berlin: Springer.

    Google Scholar 

  27. Gaudiello, I., & Zibetti, E. (2013). Using control heuristics as a means to explore the educational potential of robotics kits. Themes in Science and Technology Education, 6(1), 15–28.

    Google Scholar 

  28. Goos, M., & Galbraith, P. (1996). Do it this way! Metacognitive strategies in collaborative mathematical problem solving. Educ Stud Math, 30(3), 229–260.

    Article  Google Scholar 

  29. Gura, M. (2007). In K. King & M. Gura (Eds.), Classroom robotics: Case stories of 21st century instruction for millennial students (pp. 11–31). Charlotte: Information age publishing.

    Google Scholar 

  30. Huang, L., Varnado, T., & Gillan, D. (2014).Exploring reflection journals and self-efficacy in robotics education. In Proceedings of the Human Factors and Ergonomics Society Annual Meeting (Vol. 58, no. 1, pp. 1939-1943).SAGE publications.

  31. Hussain, S., Lindh, J., & Shukur, G. (2006). The effect of LEGO training on pupils' school performance in mathematics, problem solving ability and attitude: Swedish data. Educational Technology & Society, 9(3), 182–194.

    Google Scholar 

  32. Ishii, N., Suzuki, Y., Fujiyoshi, H., Fujii, T., &Kozawa, M. (2006). A framework for designing learning environments fostering creativity. In A. Mendez-Vilas, A. Solano Martın, J.A. Mesa Gonzalez, & J. Mesa Gonzalez (Eds.), Current developments in technology-assisted education (pp. 228–232). Badajoz: Formatex. 

  33. Jacobse, A. E., & Harskamp, E. G. (2012). Towards efficient measurement of metacognition in mathematical problem solving. Metacognition and Learning, 7(2), 133–149.

    Article  Google Scholar 

  34. Jonassen, D. H. (2000). Toward a design theory of problem solving. Educ Technol Res Dev, 48(4), 63–85.

    Article  Google Scholar 

  35. Keren, G., & Fridin, M. (2014). Kindergarten social assistive robot (KindSAR) for children’s geometric thinking and metacognitive development in preschool education: A pilot study. Comput Hum Behav, 35, 400–412.

    Article  Google Scholar 

  36. Kirschner, P. A., Sweller, J., & Clark, R. E. (2006). Why minimal guidance during instruction does not work: An analysis of the failure of constructivist, discovery, problem-based, experiential, and inquiry-based teaching. Educ Psychol, 41(2), 75–86.

    Article  Google Scholar 

  37. Kramarski, B., & Mevarech, Z. R. (1997). Cognitive-metacognitive training within a problem-solving based logo environment. Br J Educ Psychol, 67(4), 425–445.

    Article  Google Scholar 

  38. La Paglia, F., Caci, B., La Barbera, D., & Cardaci, M. (2010). Using robotics construction kits as metacognitive tools: A research in an Italian primary school. Studies in Health Technology and Informatics, 154, 110–114.

    Google Scholar 

  39. La Paglia, F., Rizzo, R., & La Barbera, D. (2011). Use of robotics kits for the enhancement of metacognitive skills of mathematics: A possible approach. Studies in Health Technology and Informatics, 167, 26–30.

    Google Scholar 

  40. Lai, K. W. (1990). Problem solving in a Lego-logo learning environment: Cognitive and metacognitive outcomes, Computers in Education (pp. 403–408). Amsterdam: Elsevier.

    Google Scholar 

  41. Lai, K. W. (1993). Lego-logo as a learning environment. J Comput Child Educ, 4(3), 229–245.

    Google Scholar 

  42. Leonard, J., Buss, A., Gamboa, R., Mitchell, M., Fashola, O. S., Hubert, T., & Almughyirah, S. (2016). Using robotics and game design to enhance Children’s self-efficacy, STEM attitudes, and computational thinking skills. J Sci Educ Technol, 25(6), 860–876.

    Article  Google Scholar 

  43. Lin, C. H., & Liu, E. Z. F. (2011). A pilot study of Taiwan elementary school students learning motivation and strategies in robotics learning. In International Conference on Technologies for E-Learning and Digital Entertainment (pp. 445–449). Springer Berlin Heidelberg.

  44. Lorenzo, M. (2005). The development, implementation, and evaluation of a problem solving heuristic. Int J Sci Math Educ, 3(1), 33–58.

    Article  Google Scholar 

  45. Martin, K. J., Chrispeels, J. H., & D'Emidio-Caston, M. (1998). Exploring the use of problem-based learning for developing collaborative leadership skills. Journal of School Leadership, 8, 470–500.

    Google Scholar 

  46. McWhorter, W. (2008). The effectiveness of using LEGO Mindstorms robotics activities to influence self-regulated learning in a university introductory computer programming course.(doctoral dissertation).University of NorthTexas.

  47. Menary, R. (2007). Writing as thinking. Lang Sci, 29(5), 621–632.

    Article  Google Scholar 

  48. Miller, P. H., Kessel, F. S., & Flavell, J. H. (1970). Thinking about people thinking about people thinking about...: A study of social cognitive development. Child Development, 41(3), 613–623.

  49. Nosratinia, M., Saveiy, M., & Zaker, A. (2014). EFL learners' self-efficacy, metacognitive awareness, and use of language learning strategies: How are they associated? Theory and Practice in Language Studies, 4(5), 1080.

    Article  Google Scholar 

  50. Panaoura, A., & Philippou, G. (2003). The construct validity of an inventory for the measurement of young Pupils' metacognitive abilities in mathematics. International Group for the Psychology of Mathematics Education, 3, 437–444.

    Google Scholar 

  51. Papadopoulos, P. M., Demetriadis, S. N., Stamelos, I. G., & Tsoukalas, I. A. (2011). The value of writing-to-learn when using question prompts to support web-based learning in ill-structured domains. Educ Technol Res Dev, 59(1), 71–90.

    Article  Google Scholar 

  52. Papert, S. (1991). Situating constructionism. In S. Papert & I. Harel (Eds.), Constructionism (pp. 1–11). Norwood: Ablex Publishing.

    Google Scholar 

  53. Polya, G. (1945). How to solve it: A new aspect of mathematical model. New Jersey: Princeton University Press.

    Google Scholar 

  54. Pugalee, D. K. (2001). Writing, mathematics, and metacognition: Looking for connections through students' work in mathematical problem solving. Sch Sci Math, 101(5), 236–245.

    Article  Google Scholar 

  55. Ricca, B., Lulis, E., & Bade, D. (2006). Lego Mindstorms and the growth of critical thinking. In Intelligent tutoring systems workshop on teaching with robots, agents, and NLP. Retrieved from http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.499.7535&rep=rep1&type=pdf

  56. Rusk, N., Resnick, M., Berg, R., & Pezalla-Granlund, M. (2008). New pathways into robotics: Strategies for broadening participation. J Sci Educ Technol, 17(1), 59–69.

    Article  Google Scholar 

  57. Schmidt, H. G., Loyens, S. M., Van Gog, T., & Paas, F. (2007). Problem-based learning is compatible with human cognitive architecture: Commentary on Kirschner, Sweller, and Clark (2006). Educ Psychol, 42(2), 91–97.

    Article  Google Scholar 

  58. Schoenfeld, A. H. (1992). Learning to think mathematically: Problem solving, metacognition, and sense making in mathematics. In D. A. Grouws (Ed.), Handbook of research on mathematics teaching and learning (pp. 334–370). New York: Macmillan. 

  59. Schraw, G., & Dennison, R. S. (1994). Assessing metacognitive awareness. Contemp Educ Psychol, 19(4), 460–475.

    Article  Google Scholar 

  60. Siegel, M. A. (2012). Filling in the distance between us: Group metacognition during problem solving in a secondary education course. J Sci Educ Technol, 21(3), 325–341.

    Article  Google Scholar 

  61. Sperling, R. A., Howard, B. C., Staley, R., & DuBois, N. (2004). Metacognition and self-regulated learning constructs. Educ Res Eval, 10(2), 117–139.

    Article  Google Scholar 

  62. Stillman, G. A., & Galbraith, P. L. (1998). Applying mathematics with real world connections: Metacognitive characteristics of secondary students. Educ Stud Math, 36(2), 157–194.

    Article  Google Scholar 

  63. Sweller, J., Kirschner, P. A., & Clark, R. E. (2007). Why minimally guided teaching techniques do not work: A reply to commentaries. Educ Psychol, 42(2), 115–121.

    Article  Google Scholar 

  64. Lo Ting-kau. (1992). Lego TC logo as a learning environment in problem- solving in advanced supplementary level design & technology with pupils aged 16–19.(Master’s thesis).University of Hong Kong, Pokfulam.

  65. Turner, S., & Hill, G. (2007). Robots in problem-solving and programming. In 8th Annual Conference of the Subject Centre for Information and Computer Sciences (pp. 82–85).

  66. Van der Stel, M., & Veenman, M. V. (2010). Development of metacognitive skillfulness: A longitudinal study. Learn Individ Differ, 20(3), 220–224.

    Article  Google Scholar 

  67. Vickers, A. J. (2005). Parametric versus non-parametric statistics in the analysis of randomized trials with non-normally distributed data. BMC medical research methodology, 5(1), 35.

    Article  Google Scholar 

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Correspondence to Soumela Atmatzidou.

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Atmatzidou, S., Demetriadis, S. & Nika, P. How Does the Degree of Guidance Support Students’ Metacognitive and Problem Solving Skills in Educational Robotics?. J Sci Educ Technol 27, 70–85 (2018). https://doi.org/10.1007/s10956-017-9709-x

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Keywords

  • Educational robotics
  • Metacognition
  • Problem solving
  • Teacher guidance