From Evidence to Practice: A Mixed-Method Study of Deep Learning and Nature-Based Learning for Fostering Creativity in Early Childhood
Keywords:
early childhood creativity, deep learning, nature-based Learning, mixed-method, needs analysisAbstract
Creativity is a foundational competency in early childhood education, yet teacher-centered routines and assessment-driven practices often curtail children’s authentic exploration. This paper examines how deep-learning strategies can be integrated with nature-based learning to foster creativity in developmentally appropriate ways. We adopted a mixed-method design: a Systematic Literature Review (2014–2024) that identified 41 eligible studies, and a needs analysis with 45 stakeholders (teachers, school leaders, curriculum developers) using surveys and follow-up interviews. The review shows that nature-based pedagogies reliably support divergent thinking, problem-solving, and socio-emotional well-being through multisensory, open-ended activities, whereas current deep-learning applications in early childhood are concentrated on personalization and assessment rather than creativity. Field data indicate strong practitioner openness to hybrid approaches—conditional on teacher mediation, minimal screen time, privacy-by-design, and feasibility under infrastructure constraints. Synthesizing both strands, we propose a nature-first, tech-light framework: outdoor provocations as the creative engine; AI used for low-intrusion documentation, pattern-spotting, and reflective prompts; offline-first delivery; and assessment that privileges portfolios and observational rubrics over narrow screen-based proxies. The study advances the field by translating scattered evidence into implementable design requirements and by offering a context-sensitive pathway for scaling creativity-oriented hybrid pedagogy in early childhood settings.
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