Design of a Quantum Computing Framework for Enhancing E-Learning Efficiency at Gombe State University, Gombe State
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- NGN 5000
Background of the Study
E-learning has emerged as a powerful tool for education, enabling students to access learning materials and interact with instructors in a virtual environment. However, as e-learning platforms grow, so do the challenges in ensuring their efficiency and responsiveness to large numbers of users. Traditional computing systems often face limitations in processing power, which can hinder the performance of e-learning platforms, especially when dealing with large datasets or complex tasks such as real-time video streaming and interactive simulations (Khan et al., 2023). Quantum computing, with its ability to process large datasets and perform parallel computations, offers a potential solution to these challenges.
At Gombe State University, the integration of a quantum computing framework could enhance the efficiency and scalability of e-learning platforms, ensuring that students and instructors have a smooth and responsive experience, even during peak usage times. This research will focus on designing a quantum computing framework to optimize e-learning platforms at the university, addressing issues such as computational efficiency, real-time interactivity, and scalability. By leveraging quantum computing’s capabilities, the university can improve the overall learning experience for students.
Statement of the Problem
Gombe State University faces challenges in optimizing its e-learning platform to handle large volumes of data and user interactions. The current computing infrastructure struggles to provide the necessary processing power to maintain high performance during peak times, leading to delays and inefficiencies. The university requires an innovative solution to enhance the performance of its e-learning platform, and quantum computing may offer the computational power needed to overcome these limitations. However, the practical application of quantum computing in e-learning systems remains largely unexplored in the context of Nigerian universities.
Objectives of the Study
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To design a quantum computing framework to enhance the efficiency of e-learning platforms at Gombe State University.
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To evaluate the potential impact of quantum computing on e-learning platform performance, including real-time interactivity and scalability.
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To assess the feasibility of integrating quantum computing into existing e-learning systems at the university.
Research Questions
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How can a quantum computing framework enhance the efficiency of e-learning platforms at Gombe State University?
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What impact does quantum computing have on the scalability and real-time interactivity of e-learning systems?
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What challenges exist in integrating quantum computing into existing e-learning systems at the university?
Significance of the Study
This study will provide insights into how quantum computing can be used to optimize e-learning systems at Gombe State University, addressing current limitations in system performance and scalability. By enhancing the efficiency of e-learning platforms, the research will contribute to improving the quality of education at the university and other institutions facing similar challenges. Furthermore, the findings will help pave the way for the broader adoption of quantum computing in educational technology.
Scope and Limitations of the Study
The study will focus on the design, implementation, and evaluation of a quantum computing framework for enhancing e-learning efficiency at Gombe State University, Gombe State. The research will not cover other applications of quantum computing in the education sector or extend beyond the university's e-learning platform.
Definitions of Terms
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Quantum Computing Framework: A structured approach to integrating quantum computing technologies into existing systems to enhance their performance.
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E-Learning Platform: A digital environment where educational content is delivered and where students interact with instructors and peers.
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Scalability: The ability of a system to handle an increasing number of users or tasks without performance degradation.
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