Session Information
Paper Session
Contribution
Self-Regulated Learning (SRL) is crucial to the learning process, enabling learners to set goals, monitor their performance, and adjust their strategy to achieve planned outcomes (Zimmerman & Schunk, 2011). Zimmerman (2002) defines SRL as an active process where learners use metacognitive, motivational, and behavioral strategies to control their learning environment, leading to increased motivation, academic success, and lifelong learning.
Social and Emotional Learning (SEL) is defined as the process of acquiring basic skills for recognizing and managing emotions, setting and achieving positive goals, valuing the perspective of others, establishing and maintaining positive relationships, making responsible decisions, and constructively managing interpersonal situations (Durlak et al., 2011). SEL is structured into the five main competencies proposed by the Collaborative for Academic, Social, and Emotional Learning (CASEL): self-awareness, self-management, social awareness, relationship skills, and responsible decision-making (Meyers et al., 2015). These competencies help students to foster resilience, empathy, and problem-solving skills, thus equipping them to handle both academic and social issues (Borowski, 2019).
Over the past few years, interest has grown in integrating SEL and SRL within educational practices via innovative means. These software-based interventions often incorporate gamified features, simulated environments, and adaptative feedback modules, promoting SRL (e.g., Araka et al., 2020) and SEL (e.g., Zhang et al., 2023) competencies. Studies have shown that technology-supported SEL improves academic performance and reduce behavioral problems (Durlak et al., 2011). Instructional software programs such as ClassDojo and Panorama are examples of evidence-based interventions providing interactive, sequenced models of students developing key SEL competencies (Friedman, 2023). Furthermore, Gu and Lee (2019) found that students' motivation and use of SRL strategies improved within a web-based mathematics learning environment. However, the merging of both SEL and SRL strategies such as goal setting, time management, and self-monitoring in online classrooms has not been explored well.
Consequently, little empirical evidence exists on using technology to develop both domains concurrently, particularly in elementary education. This research is acutely needed, as elementary school represents a critical period for establishing basic SEL and SRL skills, because children are highly responsive to interventions enhancing emotional awareness and self-regulated learning during this stage (Ha, 2023). By infusing SEL and SRL strategies into computer-based applications, educators can create environments that support both emotional intelligence and academic resilience (Liew et al., 2010). Courses can teach learners setting learning goals (SRL) and frustration management (SEL) (Op't Eynde et al., 2007). Likewise, student progress-monitoring instruments may include SEL aspects, such as milestones or motivational teamwork (Lemberger‐Truelove et al., 2018). Additionally, scholars like Akintayo et al. (2024) advocates for adaptive learning algorithms with AI that personalize interventions based on the emotional and cognitive profiles of every student. These advancements have the potential to bridge the gap by creating more holistic tools that harmoniously integrate SEL and SRL. While computer-based interventions promise to be powerful tools in promoting SEL and SRL for elementary school, there exists a lack of literature on creating and implementing programs to target both areas concurrently. Thus, research should focus on evaluating the effects of combined SEL-SRL programs, their long-term effects on student achievement, and determining best practices for classroom application (Ha, 2023). By addressing this gap, practitioners and researchers will be able to craft more robust tools that equip early learners with the skills they need to thrive academically and socially in an increasingly complex world.
Method
The intervention aimed to bridge the research gap on integrating SEL and SRL models in primary education, using innovative Information and Communication Technologies (ICT), including artificial intelligence (AI). This intervention utilized a specially designed Learning Management System (LMS) called "Journey to the ARIELS Islands," developed with the assistance of the Learning Activity Management System (LAMS). This interactive, immersive, and adaptive learning environment supports both SRL and SEL skills. The LMS framework was organized into five thematic "Islands" corresponding to one of the five CASEL-defined SEL competencies (Meyers et al., 2015). The intervention consisted of ten 1.5-hour sessions provided in blended mode using LAMS to actively promote SRL strategies that encompass self-control, help-seeking behaviors, goal setting, time management practices, self-monitoring techniques, self-motivation, and collaborative engagement among peers, all while giving meticulous attention to the cognitive and emotional requirements of the students involved. Noteworthy innovations within this educational framework include the introduction of an AI-generated virtual guide named "Airela," provided timely feedback and motivational support, complemented by pirate-themed agents introducing SRL strategies. The thematic "Islands" were developed as follows: In the Self-Awareness Island, students participated in activities that facilitated self-reflection, identification of strengths and weaknesses, and recognition of feelings in oneself and others. Using artificial intelligence resources, learners created digital images and comic strips from textual inputs, to depict themselves, followed by interactive videos, to enhance self-awareness. The Island of Self-Management challenged students to create a visual daily schedule with ChatGPT, organizing their time and reflecting on emotions during each activity. They also created a personal avatar that acted as a virtual friend, offering motivational support when needed. At the Island of Social Awareness, students build empathy. through group quiz and artificial intelligence software. They then communicated with Neon, the main character of the story, using a specially created chatbot. The Relationship Skills Island was relationship- and collaboration-focused. Students created AI-digital narratives, and shared feedback, discussed the key elements of friendship and composed a "Friendship Song" using software. On the Responsible Decision-Making Island, mind maps and puzzles were used by students to recognize decision-making steps. Students worked in groups in a chatroom to solve problems by considering all available choices. The last activity was an AI escape room, to finish the game successfully. Quantitative data was collected pre and post the intervention measuring SEL and SRL competencies and providing an insight into intervention success.
Expected Outcomes
The study underscores the significant impact of emerging technologies, particularly AI-driven LMS platforms, on educational methodologies. By examining both SRL and SEL, the research illustrates how innovative pedagogical strategies can meet the changing demands of learning. The "Journey to the ARIELS Islands" initiative, exemplifies how interactive learning contexts can foster both SEL and self-regulatory skills. This intervention highlights the transformative potential of research in education, advocating for inclusive and progressive learning strategies. The intervention revealed notable enhancements in students' self-management, persistence, and perceptions of teacher performance objectives. The incorporation of AI technologies and gamification within the LMS was crucial in achieving these outcomes. Gamification and collaborative efforts fostered motivation and engagement, while personalized learning experiences were supported by adaptive feedback mechanisms. These results align with Durlak et al. (2011), who affirmed the efficacy of technology-based SEL programs in improving academic performance and reducing behavioral issues. Furthermore, the application of SRL strategies such as goal-setting and self-monitoring reflects Zimmerman's (2002) model, emphasizing the cultivation of student autonomy and lifelong learning. Practically, this intervention serves as a replicable framework for creating inclusive and interactive classroom environments. Educators can utilize AI tools to design programs that integrate SEL and SRL, promoting resilience, empathy, and collaboration. The adoption of scalable technologies, including adaptive learning algorithms (Akintayo et al., 2024), enhances accessibility and equity, rendering this approach viable across varied educational contexts. Ultimately, this research contributes to the growing body of literature advocating for the inclusion of SEL and SRL in contemporary pedagogical models. Recognizing the holistic needs of learners, it addresses the evolving landscape of 21st-century education. The implications of this study underscore the crucial role of technology and research in redefining instructional practices to equip young learners with essential skills for academic, social, and emotional success amidst an increasingly complex environment.
References
Akintayo, O. T., Eden, C. A., Ayeni, O. O., & Onyebuchi, N. C. (2024). Integrating AI with emotional and social learning in primary education: Developing a holistic adaptive learning ecosystem. Computer Science & IT Research Journal, 5(5), 1076-1089. https://doi.org/10.51594/csitrj.v5i5.1116 Araka, E., Maina, E., Gitonga, R., & Oboko, R. (2020). Research trends in measurement and intervention tools for self-regulated learning for e-learning environments—systematic review (2008–2018). Research and Practice in Technology Enhanced Learning, 15, 1-21. https://doi.org/10.1186/s41039-020-00129-5 Borowski, T. (2019). CASEL’s framework for systemic social and emotional learning. Measuring SEL: Using Data to Inspire Practice, 8, 1-7. Durlak, J. A., Weissberg, R. P., Dymnicki, A. B., Taylor, R. D., & Schellinger, K. B. (2011). The impact of enhancing students’ social and emotional learning: A meta‐analysis of school‐based universal interventions. Child development, 82(1), 405-432. https://doi.org/10.1111/j.1467-8624.2010.01564.x Gu, P., & Lee, Y. (2019). Promoting students’ motivation and use of SRL strategies in the web-based mathematics learning environment. Journal of Educational Technology Systems, 47(3), 391-410. https://doi.org/10.1177/0047239518808522 Ha, C. (2023). Students’ self-regulated learning strategies and science achievement: exploring the moderating effect of learners’ emotional skills. Cambridge Journal of Education, 53(4), 451-472. https://doi.org/10.1080/0305764X.2023.2175787 Lemberger‐Truelove, M. E., Carbonneau, K. J., Atencio, D. J., Zieher, A. K., & Palacios, A. F. (2018). Self‐regulatory growth effects for young children participating in a combined social and emotional learning and mindfulness‐based intervention. Journal of Counseling & Development, 96(3), 289-302. https://doi.org/10.1002/jcad.12203 Liew, J., Chang, Y., Kelly, L., Yalvac, B. (2010). Self-regulated and social emotional learning in the multitasking generation. In D. Sahhuseyinoglu & D. Ilisko (Eds.), How Do Children Learn Best (pp. 62-70). Ankara, Turkey: Children’s Research Center. Meyers, D.C., Gil, L., Cross, R., Keister, S., Domitrovich, C.E., & Weissberg, R.P. (2015). CASEL guide for social and emotional learning. Chicago, IL: CASEL. Op't Eynde, P., De Corte, E., & Verschaffel, L. (2007). Students' emotions: A key component of self-regulated learning?. In Emotion in education (pp. 185-204). Academic Press. https://doi.org/10.1016/B978-012372545-5/50012-5 Zhang, F., Zhang, Y., Li, G., & Luo, H. (2023). Using Virtual Reality Interventions to Promote Social and Emotional Learning for Children and Adolescents: A Systematic Review and Meta-Analysis. Children, 11(1), 41. https://doi.org/10.3390/children11010041 Zimmerman, B. J. (2002). Becoming a self-regulated learner: An overview. Theory Into Practice, 41(2), 64–70. https://doi.org/10.1207/s15430421tip4102_2 Zimmerman, B. J., & Schunk, D. H. (2011). Self-regulated learning and performance: An introduction and an overview. Handbook of self-regulation of learning and performance, 15-26. https://doi.org/10.4324/9780203839010
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