BRAIN-BASED LEARNING STRATEGIES FOR PERSONALIZED HYBRID EDUCATION - Raul Gomez; Clara Mendes; Rudy Surbakti (Journal Neosantara Hybrid Learning, 2026)
Personalized hybrid education offers flexible learning opportunities, yet technological adaptation alone often fails to address differences in attention, memory, cognitive load, prior knowledge, and self-regulation that shape individual learning. This study examined the effectiveness of evidence-informed brain-based learning strategies within personalized hybrid education and developed an adaptive framework for cognitively responsive instruction. A quasi-experimental mixed-methods design compared students receiving brain-informed personalized hybrid instruction with those experiencing conventional hybrid learning. The intervention integrated prior-knowledge activation, retrieval practice, spaced learning, cognitive-load management, adaptive scaffolding, formative feedback, and metacognitive reflection across physical and digital environments. Findings indicated stronger academic achievement, knowledge retention, cognitive engagement, motivation, and self-regulated learning among students receiving personalized instruction, accompanied by more manageable cognitive demands. Learning analytics and qualitative evidence further demonstrated that effective personalization required dynamic adjustments based on learner readiness, performance, progress, and support needs rather than fixed learning-style classifications. The study concludes that brain-informed personalization is most effective when grounded in credible learning science and implemented through continuous cycles of diagnosis, adaptation, retrieval, feedback, monitoring, and recalibration. The proposed framework advances personalized hybrid education by integrating cognitive responsiveness, pedagogical orchestration, and adaptive support while avoiding unsupported neuromyth-based instructional assumptions in contemporary educational practice.