In recent years, educational support robots that assist learners have attracted attention. The main role of teacher-type robots in previous research has been to teach students how to solve problems and to explain learning material. Under such conditions, students may not learn the material adequately due to their reliance on the support of the robot; this paper utilizes the cognitive apprenticeship theory in order to prevent this problem. The cognitive apprenticeship theory asserts that the support provided to a student should change according to the student’s learning situation. Previous studies have reported that pedagogy based on the cognitive apprenticeship theory can improve students’ learning skills. Therefore, we hypothesize that students’ learning will improve when robots teach them how to solve questions based on the cognitive apprenticeship theory. In this paper, we investigate the learning effects of robot teaching based on the cognitive apprenticeship theory in collaborative learning with junior high-school and university students. The results of this experiment suggest that collaborative learning with robots that employ the cognitive apprenticeship theory improves the learning of high-school and university students.

1. [1] T. Kanda, “How a Communication Robot Can Contribute to Education?,” J. of Japanese Society for Artificial Intelligence, Vol.23, No.2, pp. 229-236, 2008.
2. [2] K. Shinozawa, F. Naya, J. Yamato, and K. Kogure, “Differences in effect of robot and screen agent recommendations on human decision-making,” Int. J. of Human-Computer Studies, Vol.62, Issue 2, pp. 267-279, 2005.
3. [3] J. Han, M. Jo, V. Jones, and J. H. Jo, “Comparative Study on the Educational Use of Home Robots for Children,” J. of Information Processing Systems, Vol.4, No.4, pp. 159-168, 2008.
4. [4] T. Belpaeme, J. Kennedy, A. Ramachandran, B. Scassellati, and F. Tanaka, “Social robots for education: A review,” Science Robotics, Vol.3, Issue 21, doi: 10.1126/scirobotics.aat5954, 2018.
5. [5] D. Berrett, “Cheating and the Generational Divide,” Inside Higher Ed, 2010, https://www.insidehighered.com/news/2010/11/17/cheating-and-generational-divide [accessed April 5, 2016]
6. [6] E. Dante, “The Shadow Scholar,” The Chronicle of Higher Education, 2010, http://chronicle.com/article/The-Shadow-Scholar/125329/ [accessed April 5, 2016]
7. [7] A. Collins, J. S. Brown, and S. E. Newman, “Cognitive Apprenticeship: Teaching the Crafts of Reading, Writing, and Mathematics,” L. B. Resnick (Ed.), “Knowing, Learning, and Instruction: Essays in Honor of Robert Glaser,” pp. 453-494, Lawrence Erlbaum Associates, Inc., 1989.
8. [8] S. P. Lajoie and A. Lesgold, “Apprenticeship training in the workplace: computer-coached practice environment as a new form of apprenticeship,” Machine-Mediated Learning – Intelligent Tutoring Systems, Vol.3, Issue 1, pp. 7-28, 1989.
9. [9] T. Nagai, “Zyukurenzyutsusya no Trifecta wo kakyuteki ni sokonawanai tameno kyouiku sisutemu no kouchiku –Kaiho shuzyutsu, minimamu so shuzyutsu wo jisedai ni tsutaeru koto ha kanou nanoka?–,” Japanese J. of Endourology, Vol.31, No.1, pp. 27-31, 2018 (in Japanese).
10. [10] L. Zhang and A. Hazeyama, “A Collaborative Learning Support System for Software Engineering Education Based on the Cognitive Apprenticeship and Analysis of Its Application,” IEICE Technical Report, Vol.111, No.282, pp. 43-48, 2011.
11. [11] H. Bouta and F. Paraskeva, “The cognitive apprenticeship theory for the teaching of mathematics in an online 3D virtual environment,” Int. J. of Mathematical Education in Science and Technology, Vol.44, Issue 2, pp. 159-178, 2013.
12. [12] Ellianawati, D. Rusdiana, J. Sabandar, and B. Subali, “Development of cognitive apprenticeship instruction model in mathematical physics learning to improve reflective thinking skills,” Proc. of the 1st Int. Conf. on Education, 2017.
13. [13] F. Tanaka and S. Matsuzoe, “Children Teach a Care-Receiving Robot to Promote Their Learning: Field Experiments in a Classroom for Vocabulary Learning,” J. of Human-Robot Interaction, Vol.1, No.1, pp. 78-95, 2012.
14. [14] T. Sato, Y. Kudo, and H. Osawa, “Book Introduction Robot Designed by Children for Promoting Interest in Reading,” Proc. of the 5th Int. Conf. on Human Agent Interaction (HAI’17), pp. 17-25, 2017.
15. [15] F. Jimenez, T. Yoshikawa, T. Furuhashi, and M. Kanoh, “Effects of a Novel Sympathy-Expression Method on Collaborative Learning Among Junior High School Students and Robots,” J. Robot. Mechatron., Vol.30, No.2, pp. 282-291, 2018.
16. [16] M. Alemi, A. Meghdari, and M. Ghazisaedy, “Employing Humanoid Robots for Teaching English Language in Iranian junior High-Schools,” Int. J. of Humanoid Robotics, Vol.11, No.3, Article No.1450022, 2014.
17. [17] G. Gordon, S. Spaulding, J. K. Westlund, J. J. Lee, L. Plummer, M. Martinez, M. Das, and C. Breazeal, “Affective Personalization of a Social Robot Tutor for Children’s Second Language Skills,” Proc. of the 30th AAAI Conf. on Artificial Intelligence, pp. 3951-3957, 2016.
18. [18] Chugaku gakusyu saito, https://study.005net.com/ (in Japanese) [accessed June 1, 2016]
19. [19] Chuichi sugaku houteisiki no mondai, http://sukinakazu.net/tyuugaku-suugaku-houteisiki (in Japanese) [accessed June 1, 2016]
20. [20] SPI Noto No Kai and H. Tsuda, “Korega honto no SPI3 da! 2019 nendoban,” Yosensha, 2017 (in Japanese).
21. [21] SPI Noto No Kai, “Korega honto no SPI3 tesuto senta da! 2019 nendoban,” Yosensha, 2017 (in Japanese).
22. [22] KIP Shingaku Kyoshitsu, http://www.kip-juku.com/ (in Japanese) [accessed September 1, 2016]