Skip to main content

Metacognitive Processes Involved in Human Robot Interaction in the School Learning Environment

  • Conference paper
  • First Online:
Human-Computer Interaction (HCII 2023)

Abstract

With the ongoing digital revolution and the ever-increasing development of new technology, schools must provide students with a grounding in certain technical skills, such as computational thinking, which are likely to be required for future roles, including working with and operating robots. For such robot human interaction to be successful, introduction of robots into school learning environments is crucial. However, we also need to better understand the learning processes involved in this human robot interaction. Acknowledging the importance of metacognitive processes as an essential aspect in achieving learning outcomes in educational contexts, this article investigates the experiences of students in human-robotic interactions and related tasks (programming and math) by exploring the student’s self-assessment of perceived performance (JOP), motivation (fun and difficulty) and learning. This aim has been achieved through a pilot study whereby the authors conducted a three-day workshop with grade 6 students and collected pre and post survey data. The findings contribute knowledge to our understanding of the importance of metacognition and in particular accurate self-assessment as crucial for both motivation and learning with humanoid robots.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 129.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 169.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Crompton, H., Gregory, K., Burke, D.: Humanoid robots supporting children’s learning in an early childhood setting. Br. J. Edu. Technol. 49(5), 911–927 (2018)

    Article  Google Scholar 

  2. Meichenbaum, D.: Teaching thinking: a cognitive-behavioral perspective. Thinking Learn. Skills 2, 407–426 (1985)

    Google Scholar 

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

    Article  Google Scholar 

  4. Efklides, A.: Metacognition: Defining its facets and levels of functioning in relation to self-regulation and co-regulation. Eur. Psychol. 13(4), 277–287 (2008)

    Article  Google Scholar 

  5. Schripsema, N.R., et al.: Impact of vocational interests, previous academic experience, gender and age on situational judgement test performance. Adv. Health Sci. Educ. 22(2), 521–532 (2017)

    Article  Google Scholar 

  6. Boud, D., Lawson, R., Thompson, D.G.: The calibration of student judgement through self-assessment: disruptive effects of assessment patterns. High. Educ. Res. Dev. 34(1), 45–59 (2015)

    Article  Google Scholar 

  7. Yeung, N., Summerfield, C.: Metacognition in human decision-making: confidence and error monitoring. Philos. Trans. Roy. Soc. B: Biol. Sci. 367(1594), 1310–1321 (2012)

    Article  Google Scholar 

  8. Balogh, R. Educational robotic platform based on arduino. in Proceedings of the 1st international conference on Robotics in Education, RiE2010. FEI STU, Slovakia. 2010

    Google Scholar 

  9. Powers, K., et al. Tools for teaching introductory programming: what works? In: Proceedings of the 37th SIGCSE Technical Symposium on Computer Science Education (2006)

    Google Scholar 

  10. Leite, I., et al.: Modelling empathic behaviour in a robotic game companion for children: an ethnographic study in real-world settings. In: Proceedings of the Seventh Annual ACM/IEEE International Conference on Human-Robot Interaction (2012)

    Google Scholar 

  11. Newton, D.P., Newton, L.D.: Humanoid robots as teachers and a proposed code of practice. in Frontiers in Education. Frontiers (2019)

    Google Scholar 

  12. Pandey, A.K., Gelin, R.: Humanoid robots in education: a short review. In: Goswami, A. Vadakkepat, P. (eds.) Humanoid Robotics: A Reference, pp. 2617–2632. Springer, Dordrecht (2017). /https://doi.org/10.1007/978-94-007-6046-2_113

  13. Belpaeme, T., et al.: Social robots for education: a review. Sci. Robot. 3(21), eaat5954 (2018)

    Google Scholar 

  14. Lehmann, H., Rossi, P.G.: Social robots in educational contexts: developing an application in enactive didactics. J. e-Learning Knowl. Soc. 15(2) (2019)

    Google Scholar 

  15. Kazakoff, E.R., Sullivan, A., Bers, M.U.: The effect of a classroom-based intensive robotics and programming workshop on sequencing ability in early childhood. Early Childhood Educ. J. 41(4), 245–255 (2013)

    Article  Google Scholar 

  16. Ros, R., Baroni, I., Demiris, Y.: Adaptive human–robot interaction in sensorimotor task instruction: from human to robot dance tutors. Robot. Auton. Syst. 62(6), 707–720 (2014)

    Article  Google Scholar 

  17. Rhodes, M.G., Tauber, S.K.: The influence of delaying judgments of learning on metacognitive accuracy: a meta-analytic review. Psychol. Bull. 137(1), 131 (2011)

    Article  Google Scholar 

  18. Moore, D.A., Healy, P.J.: The trouble with overconfidence. Psychol. Rev. 115(2), 502 (2008)

    Article  Google Scholar 

  19. Pajares, F., Graham, L.: Self-efficacy, motivation constructs, and mathematics performance of entering middle school students. Contemp. Educ. Psychol. 24(2), 124–139 (1999)

    Article  Google Scholar 

  20. Zhang, C.: An Inquiry into Student Math Self-Efficacy, As Told from the Perspective of Ontario Secondary Teachers (2017)

    Google Scholar 

  21. Lee, E.J.: Biased self-estimation of maths competence and subsequent motivation and achievement: differential effects for high-and low-achieving students. Educ. Psychol. 1–21 (2020)

    Google Scholar 

  22. Kanaparan, G.: Self-efficacy and engagement as predictors of student programming performance: an international perspective (2016)

    Google Scholar 

  23. Kong, S.-C., Chiu, M.M., Lai, M.: A study of primary school students’ interest, collaboration attitude, and programming empowerment in computational thinking education. Comput. Educ. 127, 178–189 (2018)

    Article  Google Scholar 

  24. Master, A., et al.: Programming experience promotes higher STEM motivation among first-grade girls. J. Exp. Child Psychol. 160, 92–106 (2017)

    Article  Google Scholar 

  25. Shim, J., Kwon, D., Lee, W.: The effects of a robot game environment on computer programming education for elementary school students. IEEE Trans. Educ. 60(2), 164–172 (2016)

    Article  Google Scholar 

  26. Chen, P.: Exploring the accuracy and predictability of the self-efficacy beliefs of seventh-grade mathematics students. Learn. Individ. Differ. 14(1), 77–90 (2003)

    Article  Google Scholar 

  27. Chen, P., Zimmerman, B.: A cross-national comparison study on the accuracy of self-efficacy beliefs of middle-school mathematics students. J. Exp. Educ. 75(3), 221–244 (2007)

    Article  Google Scholar 

  28. Moore, D.A., Cain, D.M.: Overconfidence and underconfidence: when and why people underestimate (and overestimate) the competition. Organ. Behav. Hum. Decis. Process. 103(2), 197–213 (2007)

    Article  Google Scholar 

  29. Klayman, J., et al.: Overconfidence: it depends on how, what, and whom you ask. Organ. Behav. Hum. Decis. Process. 79(3), 216–247 (1999)

    Article  Google Scholar 

  30. Rachmatullah, A., Ha, M.: Examining high-school students’ overconfidence bias in biology exam: a focus on the effects of country and gender. Int. J. Sci. Educ. 41(5), 652–673 (2019)

    Article  Google Scholar 

  31. Hosein, A., Harle, J.: The relationship between students’ prior mathematical attainment, knowledge and confidence on their self-assessment accuracy. Stud. Educ. Eval. 56, 32–41 (2018)

    Article  Google Scholar 

  32. Dupeyrat, C., et al.: Positive biases in self-assessment of mathematics competence, achievement goals, and mathematics performance. Int. J. Educ. Res. 50(4), 241–250 (2011)

    Article  Google Scholar 

  33. Hardy, E.: Fostering accurate self-efficacy beliefs in middle school mathematics students. Evergreen State College (2013)

    Google Scholar 

  34. Rachmatullah, A., Mayhorn, C.B., Wiebe, E.N.: The effects of prior experience and gender on middle school students’ computer science learning and monitoring accuracy in the Use-Modify-Create progression. Learn. Individ. Differ. 86, 101983 (2021)

    Article  Google Scholar 

  35. Harrington, B., et al.: Gender, confidence, and mark prediction in CS examinations. In: Proceedings of the 23rd Annual ACM Conference on Innovation and Technology in Computer Science Education (2018)

    Google Scholar 

  36. Mishra, D., Ostrovska, S., Hacaloglu, T.: Assessing team work in engineering projects. Int. J. Eng. Educ. 31(2), 627–634 (2015)

    Google Scholar 

  37. Alaoutinen, S., Smolander, K.: Student self-assessment in a programming course using bloom's revised taxonomy. In: Proceedings of the fifteenth annual conference on Innovation and technology in computer science education. 2010

    Google Scholar 

  38. Brown, M., Brown, P., Bibby, T.: “I would rather die”: reasons given by 16-year-olds for not continuing their study of mathematics. Res. Math. Educ. 10(1), 3–18 (2008)

    Article  Google Scholar 

  39. Jenkins, T.: On the difficulty of learning to program. In: Proceedings of the 3rd Annual Conference of the LTSN Centre for Information and Computer Sciences. Citeseer (2002)

    Google Scholar 

  40. Giannakos, M.N., Jaccheri, L.: What motivates children to become creators of digital enriched artifacts? In: Proceedings of the 9th ACM Conference on Creativity & Cognition (2013)

    Google Scholar 

  41. Papavlasopoulou, S., Sharma, K., Giannakos, M.N.: How do you feel about learning to code? Investigating the effect of children’s attitudes towards coding using eye-tracking. Int. J. Child-Comput. Int. 17, 50–60 (2018)

    Google Scholar 

  42. Qiu, K., et al.: A curriculum for teaching computer science through computational textiles. In: Proceedings of the 12th International Conference on Interaction Design and Children (2013)

    Google Scholar 

  43. Searle, K.A., et al.: Diversifying high school students’ views about computing with electronic textiles. In: Proceedings of the Tenth Annual Conference on International Computing Education Research (2014)

    Google Scholar 

  44. Hinckle, M., et al.: The relationship of gender, experiential, and psychological factors to achievement in computer science. In: Proceedings of the 2020 ACM Conference on Innovation and Technology in Computer Science Education (2020)

    Google Scholar 

  45. Durak, H.Y., Yilmaz, F.G.K., Yilmaz, R.: Computational thinking, programming self-efficacy, problem solving and experiences in the programming process conducted with robotic activities. Contemp. Educ. Technol. 10(2), 173–197 (2019)

    Article  Google Scholar 

  46. Kaloti-Hallak, F., Armoni, M., Ben-Ari, M.: Students’ attitudes and motivation during robotics activities. In: Proceedings of the Workshop in Primary and Secondary Computing Education (2015)

    Google Scholar 

  47. Aparicio, J.T., et al.: Learning programming using educational robotics. In: 2019 14th Iberian Conference on Information Systems and Technologies (CISTI). IEEE (2019)

    Google Scholar 

  48. Ohnishi, Y., et al.: Robotics programming learning for elementary and junior high school students. J. Robot. Mechatron. 29(6), 992–998 (2017)

    Article  Google Scholar 

  49. Fabros-Tyler, G.: English, Mathematics, and Programming grades in the secondary level as predictors of academic performance in the college level. In: IISA 2014, The 5th International Conference on Information, Intelligence, Systems and Applications. IEEE (2014)

    Google Scholar 

  50. Korkmaz, Ö.: The effect of scratch-and lego mindstorms Ev3-based programming activities on academic achievement, problem-solving skills and logical-mathematical thinking skills of students. MOJES: Malaysian Online J. Educ. Sci. 4(3), 73–88 (2018)

    Google Scholar 

  51. White, G., Sivitanides, M.: An empirical investigation of the relationship between success in mathematics and visual programming courses. J. Inf. Syst. Educ. 14(4), 409 (2003)

    Google Scholar 

  52. Balmes, I.L.: Correlation of mathematical ability and programming ability of the computer science students. Asia Pacific J. Educ. Arts Sci. 4(3), 85–88 (2017)

    Google Scholar 

  53. de Souza, L.M., et al.: Mathematics and programming: marriage or divorce? In: 2019 IEEE World Conference on Engineering Education (EDUNINE). IEEE (2019)

    Google Scholar 

  54. Erdogan, Y., Aydin, E., Kabaca, T.: Exploring the psychological predictors of programming achievement. J. Inst. Psychol. 35(3) (2008)

    Google Scholar 

  55. Qahmash, A., Joy, M., Boddison, A.: To what extent mathematics correlates with programming: statistical analysis. In: International Conference on Computer Science Education Innovation & Technology (CSEIT). Proceedings. 2015. Global Science and Technology Forum (2015)

    Google Scholar 

  56. Bergin, S., Reilly, R.: Programming: factors that influence success. In: Proceedings of the 36th SIGCSE Technical Symposium on Computer Science Education (2005)

    Google Scholar 

  57. Bellon, E., Fias, W., De Smedt, B.: Metacognition across domains: Is the association between arithmetic and metacognitive monitoring domain-specific? PLoS ONE 15(3), e0229932 (2020)

    Article  Google Scholar 

  58. Sadler, D.R.: Formative assessment and the design of instructional systems. Instr. Sci. 18(2), 119–144 (1989)

    Article  Google Scholar 

  59. Zimmerman, B.J., Schunk, D.H.: Handbook of self-regulation of learning and performance. Routledge/Taylor & Francis Group. (2011)

    Google Scholar 

  60. Boud, D., Molloy, E.: Rethinking models of feedback for learning: the challenge of design. Assess. Eval. High. Educ. 38(6), 698–712 (2013)

    Article  Google Scholar 

  61. Ramdass, D., Zimmerman, B.J.: Effects of self-correction strategy training on middle school students’ self-efficacy, self-evaluation, and mathematics division learning. J. Adv. Acad. 20(1), 18–41 (2008)

    Google Scholar 

Download references

Funding

The authors have no competing interests to declare that are relevant to the content of this article.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Deepti Mishra .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Mishra, D., Lugo, R.G., Parish, K., Tilden, S. (2023). Metacognitive Processes Involved in Human Robot Interaction in the School Learning Environment. In: Kurosu, M., Hashizume, A. (eds) Human-Computer Interaction. HCII 2023. Lecture Notes in Computer Science, vol 14012. Springer, Cham. https://doi.org/10.1007/978-3-031-35599-8_6

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-35599-8_6

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-35598-1

  • Online ISBN: 978-3-031-35599-8

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics