Journal of Science Education and Technology

, Volume 27, Issue 1, pp 70–85 | Cite as

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

  • Soumela AtmatzidouEmail author
  • Stavros Demetriadis
  • Panagiota Nika


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.


Educational robotics Metacognition Problem solving Teacher guidance 


  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).Google Scholar
  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.CrossRefGoogle 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).Google Scholar
  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.CrossRefGoogle Scholar
  10. Benitti, F. B. V. (2012). Exploring the educational potential of robotics in schools: A systematic review. Comput Educ, 58(3), 978–988.CrossRefGoogle Scholar
  11. Bers, M. U. (2007). Project InterActions: A multigenerational robotic learning environment. J Sci Educ Technol, 16(6), 537–552.CrossRefGoogle 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.CrossRefGoogle 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.CrossRefGoogle 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.CrossRefGoogle 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.CrossRefGoogle 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.Google Scholar
  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.CrossRefGoogle Scholar
  20. Dennison, R. S. (1997). Relationships among measures of metacognitive monitoring. In annual meeting of the American Educational Association, Chicago, IL. Google Scholar
  21. Druin, A., & Hendler, J. A. (2000). Robots for kids: Exploring new technologies for learning. San Francisco: Morgan Kaufmann.Google Scholar
  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). Google Scholar
  24. Flavell, J. H. (1979). Metacognition and cognitive monitoring: A new area of cognitive–developmental inquiry. Am Psychol, 34(10), 906.CrossRefGoogle 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.CrossRefGoogle 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.Google Scholar
  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. Google Scholar
  33. Jacobse, A. E., & Harskamp, E. G. (2012). Towards efficient measurement of metacognition in mathematical problem solving. Metacognition and Learning, 7(2), 133–149.CrossRefGoogle Scholar
  34. Jonassen, D. H. (2000). Toward a design theory of problem solving. Educ Technol Res Dev, 48(4), 63–85.CrossRefGoogle 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.CrossRefGoogle 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.CrossRefGoogle 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.CrossRefGoogle 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.CrossRefGoogle 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.Google Scholar
  44. Lorenzo, M. (2005). The development, implementation, and evaluation of a problem solving heuristic. Int J Sci Math Educ, 3(1), 33–58.CrossRefGoogle 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.Google Scholar
  47. Menary, R. (2007). Writing as thinking. Lang Sci, 29(5), 621–632.CrossRefGoogle 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.Google Scholar
  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.CrossRefGoogle 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.CrossRefGoogle 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.CrossRefGoogle 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
  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.CrossRefGoogle 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.CrossRefGoogle 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. Google Scholar
  59. Schraw, G., & Dennison, R. S. (1994). Assessing metacognitive awareness. Contemp Educ Psychol, 19(4), 460–475.CrossRefGoogle 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.CrossRefGoogle 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.CrossRefGoogle 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.CrossRefGoogle 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.CrossRefGoogle 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.Google Scholar
  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).Google Scholar
  66. Van der Stel, M., & Veenman, M. V. (2010). Development of metacognitive skillfulness: A longitudinal study. Learn Individ Differ, 20(3), 220–224.CrossRefGoogle 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.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2017

Authors and Affiliations

  • Soumela Atmatzidou
    • 1
    Email author
  • Stavros Demetriadis
    • 1
  • Panagiota Nika
    • 1
  1. 1.Department of InformaticsAristotle University of ThessalonikiThessalonikiGreece

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