Promoting Social-Emotional Learning through Robot-Based Programming Activities in Early Childhood Education: A Systematic Review

• In Event: Evidence-Based Practice for Early STEM Teaching and Learning: Diverse Adult Roles and Contexts

Abstract

This review investigates empirical studies on robot-based programming education in early childhood education (ECE), focusing on how these activities support social-emotional learning (SEL) competencies.
To carry out this systematic review, we followed the procedures outlined by Benitti (2012) and adhered to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) reporting guidelines (Moher et al., 2009). To identify relevant literature, we used three databases: Education Resources Information Center (ERIC), American Psychological Association (APA) PsycINFO, and Web of Science, utilizing the search protocol presented in Figure 1. The inclusion and exclusion criteria used to identify the 19 relevant studies are summarized in Table 1.
RQ1. What are the general characteristics and trends of empirical studies promoting SEL in early childhood through robot-based programming education? Table 1 summarises 19 studies conducted over the past five years across various countries with young children aged 3–6. Settings included kindergartens, preschools, and other early childhood environments. Research designs included case studies, experiments, and qualitative approaches. Sample sizes ranged from small groups to over 400 children. Studies examined variables such as gender, SES, or baseline cognitive/social skills (see Table 2 for details). Bee-Bot (n = 5) is the most commonly used. Only three studies used multiple robots, while 16 studies used only one robot. Many studies (n = 8) implemented short-term interventions under six weeks, with a few lasting up to 15 weeks. Most activities were closed-ended (n = 10), involving pre-determined tasks or goals given by adults. Grounded constructivist, constructionist, and sociocultural theories, these robotics activities emphasized peer interaction and collaborative decision-making. Notably, there was a mismatch between the open-ended approach emphasized theoretically and the closed-ended nature of the actual activities (see Table 3 for details).
RQ 2. What SEL outcomes are targeted, and what instructional design features effectively promote these outcomes? Only seven out of a total of 19 explicitly describe SEL competencies as learning objectives (explicit). Most studies (n = 12) did not explicitly set SEL as a goal (implicit), but measured SEL-related competencies in the actual research process. The SEL outcomes are analyzed using the Collaborative for Academic, Social, and Emotional Learning (CASEL) framework. Self-management and relationship skills were the most frequently addressed. Interestingly, some competencies in the reviewed studies could not be fully analyzed within the CASEL framework due to its exclusive focus on human-centered relationships (for details, refer to Tables 4 and 5). Effective robot-based programming instructional features promoting SEL included Science, Technology, Engineering, Arts, Mathematics (STEAM), child-centered, multidisciplinary, and narrative-based play approaches (see Tables 6 and 7 for details). Yet, there is a lack of a clear description of the activity implemented. Future studies should provide more detailed reports on activity designs.
This review provides practical and theoretical foundations for promoting SEL competencies in young children through robot-based programming activities. The findings highlight opportunities to expand SEL within technological environments. Future research should clarify activity structures, explicitly set balanced SEL objectives, and broaden SEL to include relationships with robots and technological entities. Further detailed analyses will be presented.

Authors

• Hyeungok Kang, California State University - San Bernardino

• Eun Hye Grace Ko, Texas A&M University

• Min Sun Kim, Franklin University

• Hye Ryung Won, Slippery Rock University of Pennsylvania

• Sehyun Yun, Ball State University