Taxshila Research Page

Cognitive blindness represents a critical yet often overlooked limitation in contemporary education. This is defined by the understanding deficiencies in the processing of knowledge transfer. Here, learners are exposed to information or lessons but fail to achieve meaningful understanding, retention and application. Understanding Cognitive Blindness: Why Learners Fail to Understand This paper conceptualizes cognitive blindness as a functional deficiency in the learning brain. It is characterized by the inability to process, structure, and internalize knowledge despite active participation in classroom activities. The study examines how traditional, teacher-centered instructional models and passive learning environments contribute to fragmented knowledge transfer and superficial learning outcomes. Drawing on the principles of learnography and book-to-brain learning, the study identifies key factors underlying cognitive blindness. These factors are the absence of structured knowledge fra...

Mathematics fits naturally into the brainpage learning model of learnography because the learner's brain is inherently designed to recognize patterns, sequences and structures. These are the building blocks of mathematical reasoning. Mathematics: Bridge Between Neural Learning, AI Algorithms and Universal Laws ♾️ Research Introduction: Language of Everything Mathematics is the universal language that underlies the structure of intelligence, technology and the nature. This article explores how mathematics operates as a common framework in brainpage learning, artificial intelligence, and the natural world. In human brain, mathematical understanding emerges through pattern recognition, spatial awareness, and motor-based brainpage modulation. In machines, artificial neural networks use mathematical models to mimic cognition, decision-making, and prediction. Meanwhile, n...

This article challenges the outdated pedagogy of periodic teaching in education system. System learnography introduces brainpage development, motor-driven learning, and the One Day One Book model as a transformative approach to real knowledge transfer. Unlocking Deep Learning: Escaping the Limits of Periodic Pedagogy 👨‍🏫 Research Introduction: Flaws of Periodic Teaching in Education System Periodic teaching system is a cornerstone of conventional education, which divides teaching and learning into segmented time blocks and relies heavily on teacher-led instruction. While widely practiced, this model often results in fragmented understanding, passive learning, and poor knowledge retention. This article explores the inherent limitations of periodic teaching and questions its effectiveness in achieving real knowledge transfer. The study introduces the concept of learnography for rea...

The process of learning and knowledge transfer is governed by highly organized systems, both in biological intelligence and artificial models. This paper explores the parallels and interactions between the human brain's architecture, specifically neural circuits and pathways, and the computational structure of artificial neural networks (ANNs). Parallel Evolution of Intelligence: Learning Machines and Thinking Minds 🧠 In human learnography, brain regions such as the prefrontal cortex, hippocampus and cerebellum collaborate through synaptic plasticity and motor science to build memory and skillsets. Similarly, ANNs utilize the layers of interconnected nodes to simulate these mechanisms, enabling machines to learn from data, recognize patterns, and make informed decisions. By examining the learning parts and structures in both systems, this study highlights how biological insights i...

Artificial Neural Networks (ANNs) are at the core of modern artificial intelligence (AI), enabling machines to learn from data, recognize complex patterns, and make accurate predictions. From Equations to Intelligence: Mathematics Drives Neural Networks ♾️ We explore the essential mathematical principles that form the backbone of ANNs. We use linear algebra for data representation and matrix transformations. Calculus is applied for optimization through backpropagation, and probability and statistics are used for uncertainty modeling and predictive reasoning. Advanced optimization algorithms are focused for performance tuning. By uncovering the mathematical mechanisms that drive neural computations, this study provides a comprehensive understanding of how ANNs function and evolve. These foundational concepts not only enhance our grasp of machine learning models but also empower resear...

Mathematics is not just a subject, but this is the universal language that unites the human brain, artificial intelligence, and the fundamental laws of the universe — Shiva Narayan Mathematics: Language of Everything 🔥 This article explores how mathematical structures govern the behavior of neural networks, both biological and artificial. It also describes how brainpage learning harnesses these structures to optimize knowledge acquisition and problem-solving. What's the difference between intelligence and reality? By examining the roles of pattern recognition, calculation and neural modulation, we reveal how mathematics functions as the bridge between intelligence and reality. From brain circuits and motor learning to deep learning algorithms and cosmic patterns, mathematics emerges as the shared syntax of cognition, computation and creation. This integrated perspective encou...

Deep learning is revolutionizing AI by enabling machines to think, learn and adapt like the human brain . From image recognition and natural language processing (NLP) to robotics and autonomous systems, deep learning powers cutting-edge innovations. World of the Neural Networks, Machine Learning and AI Advancements Deep learning is a subfield of artificial intelligence (AI) and machine learning (ML) . It mimics the way the human brain processes information using artificial neural networks (ANNs). Highlights: Deep Learning: Forefront of Artificial Intelligence in Machine Learnography How Deep Learning Works: Human Brain-Inspired Model Key Technologies Behind Deep Learning Real-World Applications of Deep Learning Deep Learning vs Traditional Machine Learning Challenges & Ethical Concerns in Deep Learning The Future of Deep Learning This article demystifies neural networks, explores real-world AI applications , and discusses the challenges and future of deep learning. Deep Learning: F...

School administrators have long focused on instructional leadership , ensuring that the teachers deliver lessons. But does teaching truly transfer knowledge? With the periodic teaching system becoming outdated, it’s time to replace passive instruction with brainpage development, self-learning and motor-driven knowledge transfer. Office of School Administrator The periodic teaching of education system is outdated , as real knowledge transfer doesn’t happen in the classroom. This article explores why administrators must lead the shift from pedagogy to learnography. This is transforming schools into the hubs of independent knowledge acquisition. Highlights: Conventional Education: Teaching System Becoming Outdated Real Knowledge Transfer doesn't Happen in the Classroom Outdated Education Model: Why Periodic Teaching Fails in Schools New Role of School Administration: Enabling Knowledge Transfer How Administrators Can Drive Academic Learning Transformation Why This Shift Matters: The F...