Date of Award
Summer 7-26-2024
Degree Type
Thesis
Degree Name
Master of Science in Computer Science (MSCS)
Department
Department of Computer Science
Committee Chair/First Advisor
Dr. Abhishek Parakh
Second Advisor
Dr. Maria Valero
Third Advisor
Dr. Md Abdullah Al Hafiz Khan
Abstract
In recent years, the need to make classroom learning more interactive and engaging has become increasingly important. The lack of workforce in interdisciplinary fields such as quantum networking and quantum internet requires a new approach that addresses every learner’s individual needs. To address this challenge, this thesis introduces an adaptive learning platform rooted in the theory of learning objects and Kolb’s experiential learning model. The platform aids educators and learners in designing and utilizing various learning objects for quantum networking and quantum internet.
The platform enables educators and learners to build their own lessons and lesson plans using learning objects tagged with metadata, tailored to their interactivity levels and learning preferences. Learners engage with the platform by providing background information such as the time they can dedicate to the course, their existing knowledge, and their learning goals. Based on this information, the platform generates a customized course with content designed to maximize the learner’s achievement of their learning goals, utilizing the learning object dependency graph.
This thesis also delves into a simulator exercise that helps learners visualize and understand the key generation and message transmission using the E91 and the three-stage quantum networking protocols. By exploring the design, implementation, and feasibility of the platform, this thesis highlights its potential to revolutionize the teaching and learning of complex subjects such as quantum computing and quantum networking.
Included in
Digital Communications and Networking Commons, Educational Methods Commons, Educational Technology Commons, Online and Distance Education Commons