A Meta-Analysis on the Effects of Learning with Robots in Early Childhood Education in Korea

A Meta-Analysis on the Effects of Learning with Robots in Early Childhood Education in Korea

Sung-Deok Park (Korea National University Of Education, Cheongju-si, Korea), Eun-Jung Kim (Ho-won University, Gunsa-si, Korea) and Kyung-Chul Kim (Korea National University Of Education, Cheongju-si, Korea)
Copyright: © 2019 |Pages: 9
DOI: 10.4018/IJMBL.2019070104


This meta-analysis examines the effects of learning with robots (r-learning) on young children and, on this basis, gives suggestions for using robotics in education for young children. A test of homogeneity was performed for 27 Korean studies done between 2008 and 2016 and a random effect model introduced to reveal the effect sizes. The overall effect size was medium to large at 0.72. After analyzing gaps in effect sizes with different categorical moderator variables, the authors found significant differences depending on platform type, activity type, dependent variables for r-learning effect, and age. The study also investigates relationships between effect size and continuous moderator variables such as treatment period and year of publication. The meta-regression model showed a significantly negative relationship between effect size and year of publication. Thus, effects of r-learning on a young child are generally beneficial, and r-learning also improves variables like a child's social nature, though conversely the effect on language development appears below average.
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In recent years, an increase in the use of smart technology has greatly changed daily life and society. To keep pace with these changes, changes are needed to educational environments, including new teaching practices involving online lectures, digital textbooks, and social networking services. According to a recently published report by the Korea Institute of Science & Technology Evaluation and Planning, Report of Korea’s Dream and Challenge, service robots founded on converging technology are expected to be a core science tool in Korea.

Technological education expectations have been achieved to increasing degrees, a process that has been summed up in the acronym STEAM (science, technology, engineering, arts, and mathematics), which captures the effort in education to phase in technologies such as robots in the classroom environment to cultivate skilled workers. Indeed, robots have a somewhat symbolic role in STEAM education. In South Korea, the Ministry of Education and Science Technology (MEST) has announced “Strategies For Advancement Of Early Childhood Education,” which commit to a future-oriented curriculum involving features such as application of teacher assistant robots in early childhood education. To achieve this goal, 500 robots were provided for Korean kindergartens in 2010, at a cost of 15 million dollars, a number expanded to 8,000 by 2013. KIST (Korea Institute of Science and Technology) uses the term “robot learning” or “r-learning” to describe these efforts. R-learning in Korea is supported by a Center for R-learning Development, Promotion & Support; after certification by this center, three kinds of platform robots - Genibo, IrobiQ, and Kibot - have come into preschools, and their impacts they have on young children’s development and learning as well as their teachers’ approaches are being investigated in a remarkable proliferation of research activity - 80 studies from article to thesis length in the first five years. Whether in Korea or elsewhere, ECE r-learning research has taken place in four main areas; the Korean research will be cited first and the non-Korean research second. The first research area is positive or negative perceptions of r-learning among parents and teachers and effects of r-learning on teachers and institutions (e.g., Kim, Park, & Kim, 2010; Jung & Han, 2012; Hwang et al., 2011). These studies have shown in general that teachers are initially puzzled about how to effectively teach with robots, but gradually get accustomed to their use and develop positive attitudes toward r-learning. The second area is clarifying the effectiveness of r-learning and specific r-learning tools for children’s development -physical, linguistic, perceptual, emotional, social, and creative - and for teaching and learning (e.g., Kim, Lee, Ahn, Kim, & Cha, 2012; Kim, Lee, Hyeon, & Park, 2011; Seo, 2008). This research suggests that the overall effect is positive. The third area is designing r-learning training programs for pre- and in-service teachers as well as educational materials for use with robots, and examining their effectiveness (e.g., Lee, Lee, Eum, & Jung, 2012; Lee, Kang, & Jo, 2012; Lee, Kim, & Seo, 2011). The fourth study area is assessing interactions between children and authorized robots, including looking into robots for children with special needs (e.g., Ku & Lee, 2012; Kim, Lee, Jung, & Bae, 2011; Park, Jang, & Kim, 2011; Yoon & Hyun, 2012; Jang, 2011; Jung & Park, 2010; Hong et al., 2010). These studies show positive interactions especially in the case of autistic children, for whom robots can make a significant difference in their therapy and education.

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