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.
Keywords
- self-assessment
- motivation
- learning
- humanoid robots
- robot-assisted teaching
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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)
Meichenbaum, D.: Teaching thinking: a cognitive-behavioral perspective. Thinking Learn. Skills 2, 407–426 (1985)
Flavell, J.H.: Metacognition and cognitive monitoring: a new area of cognitive–developmental inquiry. Am. Psychol. 34(10), 906 (1979)
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)
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)
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)
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)
Balogh, R. Educational robotic platform based on arduino. in Proceedings of the 1st international conference on Robotics in Education, RiE2010. FEI STU, Slovakia. 2010
Powers, K., et al. Tools for teaching introductory programming: what works? In: Proceedings of the 37th SIGCSE Technical Symposium on Computer Science Education (2006)
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)
Newton, D.P., Newton, L.D.: Humanoid robots as teachers and a proposed code of practice. in Frontiers in Education. Frontiers (2019)
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
Belpaeme, T., et al.: Social robots for education: a review. Sci. Robot. 3(21), eaat5954 (2018)
Lehmann, H., Rossi, P.G.: Social robots in educational contexts: developing an application in enactive didactics. J. e-Learning Knowl. Soc. 15(2) (2019)
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)
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)
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)
Moore, D.A., Healy, P.J.: The trouble with overconfidence. Psychol. Rev. 115(2), 502 (2008)
Pajares, F., Graham, L.: Self-efficacy, motivation constructs, and mathematics performance of entering middle school students. Contemp. Educ. Psychol. 24(2), 124–139 (1999)
Zhang, C.: An Inquiry into Student Math Self-Efficacy, As Told from the Perspective of Ontario Secondary Teachers (2017)
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)
Kanaparan, G.: Self-efficacy and engagement as predictors of student programming performance: an international perspective (2016)
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)
Master, A., et al.: Programming experience promotes higher STEM motivation among first-grade girls. J. Exp. Child Psychol. 160, 92–106 (2017)
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)
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)
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)
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)
Klayman, J., et al.: Overconfidence: it depends on how, what, and whom you ask. Organ. Behav. Hum. Decis. Process. 79(3), 216–247 (1999)
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)
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)
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)
Hardy, E.: Fostering accurate self-efficacy beliefs in middle school mathematics students. Evergreen State College (2013)
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)
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)
Mishra, D., Ostrovska, S., Hacaloglu, T.: Assessing team work in engineering projects. Int. J. Eng. Educ. 31(2), 627–634 (2015)
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
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)
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)
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)
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)
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)
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)
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)
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)
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)
Aparicio, J.T., et al.: Learning programming using educational robotics. In: 2019 14th Iberian Conference on Information Systems and Technologies (CISTI). IEEE (2019)
Ohnishi, Y., et al.: Robotics programming learning for elementary and junior high school students. J. Robot. Mechatron. 29(6), 992–998 (2017)
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)
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)
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)
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)
de Souza, L.M., et al.: Mathematics and programming: marriage or divorce? In: 2019 IEEE World Conference on Engineering Education (EDUNINE). IEEE (2019)
Erdogan, Y., Aydin, E., Kabaca, T.: Exploring the psychological predictors of programming achievement. J. Inst. Psychol. 35(3) (2008)
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)
Bergin, S., Reilly, R.: Programming: factors that influence success. In: Proceedings of the 36th SIGCSE Technical Symposium on Computer Science Education (2005)
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)
Sadler, D.R.: Formative assessment and the design of instructional systems. Instr. Sci. 18(2), 119–144 (1989)
Zimmerman, B.J., Schunk, D.H.: Handbook of self-regulation of learning and performance. Routledge/Taylor & Francis Group. (2011)
Boud, D., Molloy, E.: Rethinking models of feedback for learning: the challenge of design. Assess. Eval. High. Educ. 38(6), 698–712 (2013)
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)
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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
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