An Activity-Based Constructivist Approach to Teaching Core Scientific Concepts
Authors-Dr. Gyanendra Rawat, Dr. Meena Bhandari
Keyword-Force, Pressure, Motion, Physical Science, Constructivist Learning, Activity-Based Teaching, Science Pedagogy.
Abstract-Background: Science education at the middle-school level often becomes abstract when taught only through definitions and formulas. Topics such as “Force and Pressure” remain difficult for learners unless they are connected with real-life contexts and supported by hands-on activities. Objectives: This study aims to design and evaluate an activity-based constructivist lesson plan to improve conceptual understanding, engagement, and questioning skills of Class VIII students in learning the concepts of Force and Pressure. Methods: A constructivist framework was applied in preparing a lesson plan aligned with NCERT curriculum guidelines. The teaching involved real-life demonstrations (e.g., knives, balloons, magnets, water bottles), experiments with every-day objects, mind mapping, guided practice, and collaborative activities. Data on student learning were gathered through pre/post evaluations, classroom participation, and observation of skill development. Results: Students taught through the constructivist activity-based approach demonstrated stronger conceptual clarity about types of forces, pressure as force per unit area, and its real-life applications. They also showed improved participation, curiosity, and the ability to link classroom learning with daily experiences (e.g., why sharp knives cut better or how porters reduce pressure using turbans). Conclusions: The findings highlight that activity-based constructivist pedagogy enhances both conceptual understanding and critical thinking. Such approaches make science learning more meaningful, relatable, and engaging, supporting the objectives of competency-based education recommended by NEP 2020 and NCF 2005.
Doi-[http://doi.org/10.5281/zenodo.17558609]
The Dynamics of Knowledge, Action and Liberation: A Study of Jñāna– Karma–Samuccaya in Śaṅkara’s Taittirīyopaniṣad Commentary
Authors-Shradha Aggarwal, Professor Om Nath Bimali
Keyword-Śaṅkara, Advaita Vedānta, Taittirīyopaniṣad Bhāṣya, jñāna (knowledge), karma (action).
Abstract-This paper investigates the intricate relationship between jñāna (knowledge), karma (action), and mokṣa (liberation) in Śaṅkara’s Taittirīyopaniṣad Bhāṣya. The study explores how Śaṅkara reinterprets Vedic exegesis—originally grounded in ritual action—through the lens of Advaita Vedānta, redefining liberation as a state of self-realization rather than ritual achievement. Through textual analysis of the Śikṣāvallī, Ānandavallī, and Bhṛguvallī, this work argues that Śaṅkara systematically dismantles the Mīmāṃsā doctrine of jñāna–karma– samuccayavāda and establishes knowledge alone as the direct means to liberation.
Doi-[http://doi.org/10.5281/zenodo.17558406]
AI-Driven Data Analytics in Enterprise Systems
Authors-Devansh Kapoor
Keyword-Artificial Intelligence, Data Analytics, Enterprise Systems, Machine Learning, Deep Learning, Natural Language Processing, Predictive Analytics, Business Intelligence, Real-Time Analytics, Data Processing, Big Data, Cloud Computing, Decision Support Systems, Automation, Data-Driven Decision Making
Abstract-AI-driven data analytics has become a critical component in modern enterprise systems, enabling organizations to transform vast amounts of data into actionable insights for strategic decision-making. With the rapid growth of data generated from business operations, customer interactions, and digital platforms, traditional analytics methods are no longer sufficient to extract meaningful value. Artificial intelligence enhances data analytics by incorporating machine learning, deep learning, and natural language processing techniques to identify patterns, predict trends, and automate analytical processes. This paper explores the integration of AI-driven analytics within enterprise systems, focusing on architectural frameworks, data processing pipelines, and intelli-gent decision-support mechanisms. It highlights how AI improves business intelligence, opera-tional efficiency, customer experience, and risk management across industries such as finance, healthcare, retail, and manufacturing. The study also examines key challenges including data quality, scalability, model interpretability, and security concerns. Emerging trends such as real-time analytics, cloud-based AI platforms, and automated data pipelines are also discussed. The findings emphasize that AI-driven data analytics is essential for building intelligent, adaptive, and competitive enterprise systems in the digital era.
A Review of Cloud Infrastructure Technologies
Authors-Liu Wenqi
Keyword-Cloud Infrastructure, Cloud Computing, Virtualization, Containerization, Software-Defined Net-working, Distributed Systems, Infrastructure as a Service, Platform as a Service, Software as a Service, Orchestration, Infrastructure as Code, Cloud Security, Scalability, Edge Computing, Serverless Computing.
Abstract-Cloud infrastructure technologies form the backbone of modern computing by providing scalable, flexible, and cost-effective resources for deploying and managing applications. With the rapid growth of digital transformation, organizations increasingly rely on cloud infrastructure to sup-port dynamic workloads, big data processing, and distributed systems. This paper presents a comprehensive review of cloud infrastructure technologies, including virtualization, containeriza-tion, software-defined networking, and distributed storage systems. It examines key service mod-els such as Infrastructure as a Service, Platform as a Service, and Software as a Service, high-lighting their roles in enabling efficient resource management and service delivery. The study also explores the importance of automation tools, orchestration frameworks, and infrastructure as code in optimizing cloud operations. Security, performance, scalability, and reliability aspects are ana-lyzed in the context of modern cloud environments. Additionally, emerging technologies such as edge computing and serverless architectures are discussed for their role in enhancing cloud capa-bilities. The review concludes that cloud infrastructure technologies are continuously evolving to meet the growing demands of modern applications and digital ecosystems.