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Okul öncesi çocukların sosyal-duygusal öğrenme becerilerinin bilgi işlemsel düşünme etkinlikleriyle geliştirilmesi
This qualitative case study aimed to explore how preschool children's participation in Computational Thinking (CT) activities facilitated the development of Social-Emotional Learning (SEL) skills. The study was conducted in an early childhood education center with four children aged 4–5. Over eight weeks, participants engaged in CT activities designed based on Positive Technological Development (PTD) and SEL frameworks. The first four weeks involved unplugged activities, while the following four weeks incorporated plugged-in activities. Data was collected through video recordings of the class sessions and analyzed using thematic analysis. The findings indicated that CT activities supported the development of key SEL skills such as self-awareness, social awareness, relationship skills, self-management, and responsible decision-making. These skills emerged through three interconnected characteristics of CT activities: collaborative design, the nature of teacher guidance, and step-by-step progression. Collaborative tasks helped children work together, solve problems, and make group decisions. Teacher guidance supported emotional expression, encouraged children to notice others' feelings, and helped them communicate and cooperate. Step-by-step activities allowed children to focus on small parts of a task, reflect on their actions, and manage their emotions. The findings suggest that when carefully designed and implemented, CT activities offer a developmentally appropriate and engaging context for nurturing social-emotional skills in early childhood.
Eğitsel robotik kodlama bağlamında okul öncesi çocukların uzamsal becerilerinin incelenmesi
An Exploratory Study of Young Children's Spatial Skills in the Context of Educational Robotics This qualitative study explored young children's spatial skills in the context of educational robotics. The study aimed to investigate two research questions: (1) How do 5-6 years-old pre-school children's spatial skills emerge when they engage with programmable robot toys? (2) How do 5- to 6-year-old pre-school children use spatial vocabulary while interacting with programmable robot toys over a semester? Data were collected through video recordings of young children participating in an 11-week robotics curriculum that included both unplugged and plugged-in activities. These video recordings were analyzed through multimodal analysis with the lens of embodied learning. Five themes emerged from the data analysis: (a) Robotic activities encourage preschool children's use of spatial skills while planning their robotic programming; (b) Designing tangible materials for plugged-in robotic activities supports preschool children's use of spatial skills; (c) Robotic activities enable preschool children to use spatial skills while solving problems in the programming phase; (d) Unplugged robotic activities that focus on different spatial concepts prepare preschool children for plugged-in activities more; (e) Providing a context, robotic activities facilitate preschool children's frequent use of specific spatial vocabulary. The findings suggest that engaging with educational robotics in the context of embodied learning not only enhance preschool children's spatial skills but also enriches their spatial vocabulary.
Üretken yapay zekanın altıncı sınıf öğrencilerinin sera etkisi konusundaki bilimsel argümantasyon kalitesini artırmadaki rolünün incelenmesi
This case study aimed to explore how a learning module designed based on the Argument-Driven Inquiry (ADI) instructional model with generative AI integration facilitated sixth-grade students' written scientific argument quality in the context of the greenhouse effect, their interaction with the chatbot, and their perceptions of this experience. Conducted after school hours at a public middle school in Mersin, Turkey, the study involved five high-achieving sixth-grade students over three weekly 120-minute sessions. During each session, participants investigated different aspects of the greenhouse effect results, engaging in various simulations, text materials, and chatbot interactions to form their scientific arguments. Data, including participants' written scientific arguments, chatbot conversation logs, and perception forms, were analyzed using content analysis to assess argument quality and participant experiences. Findings showed that the chatbot's feedback and guidance improved argument quality, particularly in refining claims, evidence, and reasoning. Although participants initially struggled with interpreting complex responses, the chatbot generally produced contextually appropriate responses. Participants reported enhanced topic understanding and new knowledge acquisition. Due to technological limitations at the school, the small sample size led the study to focus on individual written argumentation, offering deep insights into chatbot efficacy while limiting the transferability of findings to larger populations.