The Cognitive Science of Deep Learning: Neural Networks in Educational Achievement

Abstract

This paper examines the intersection of cognitive science and deep learning technologies in educational contexts, investigating how artificial neural networks can enhance educational achievement through cognitively-informed design principles. The research question addresses whether deep learning systems that incorporate cognitive science principles demonstrate superior educational outcomes compared to traditional algorithmic approaches. Using a theoretical framework grounded in cognitive load theory, dual coding theory, and connectionist models of learning, this analysis synthesizes current research on neural network applications in education. The methodology employs a comprehensive literature review combined with theoretical analysis of cognitive-neural network alignment. Findings suggest that deep learning systems designed with cognitive science principles show significant promise in personalizing learning experiences, optimizing cognitive load, and improving learning outcomes. However, substantial gaps remain in understanding the precise mechanisms through which artificial neural networks can effectively model human cognitive processes in educational contexts. The implications extend to educational technology design, cognitive science research, and pedagogical practice, suggesting a need for interdisciplinary collaboration to fully realize the potential of cognitively-informed artificial intelligence in education.