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.

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