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An Automated Adaptive Mobile Learning System Using Optimal Shortest Path Algorithms

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dc.contributor.author Alshalabi, Ibrahim Alkore
dc.date.accessioned 2016-07-05T13:56:33Z
dc.date.available 2016-07-05T13:56:33Z
dc.date.issued 2016-07-05
dc.identifier.citation I.A. Alshalabi, "An Automated Adaptive Mobile Learning System Using Optimal Shortest Path Algorithms", Ph.D. dissertation, Dept. of Computer Science and Engineering, Univ. of Bridgeport, Bridgeport, CT, 2016. en_US
dc.identifier.uri https://scholarworks.bridgeport.edu/xmlui/handle/123456789/1677
dc.description.abstract Technological innovation opens the door to create a personal learning experience for any student. In this research, we discuss adaptive learning techniques and the style of learning that integrates existing learning techniques combined with new ideas. To create an effective user friendly learning environment, adaptive learning techniques should be used in order to identify the personal needs of students and reduce their individual knowledge gaps. The result will produce learning path containing relevant content that will provide a better learning direction for each student. This dissertation explores the opportunity of using adaptive learning techniques to identify the personal needs of each student by combining different learning styles, student profiles and individualized course content. By using a directed graph, we are able to represent an accurate picture of the course descriptions for online courses through computer-based implementation of various educational systems. E-learning (electronic learning) and m-learning (mobile learning) systems are modeled as a weighted directed graph where each node represents a course unit. The Learning Path Graph represents and describes the structure of the domain knowledge, including the learning goals, and all other available learning paths. In this research, we propose a system prototype that implements optimal adaptive learning path algorithms using students’ information from their profiles and their learning style. Our goal is to improve students’ learning performances through the m-learning system in order to provide suitable course contents sequenced in a dynamic form for each student. en_US
dc.language.iso en_US en_US
dc.subject Adaptive learning en_US
dc.subject Course content en_US
dc.subject Learning style en_US
dc.subject Mobile learning en_US
dc.subject Shortest path en_US
dc.subject System prototype en_US
dc.title An Automated Adaptive Mobile Learning System Using Optimal Shortest Path Algorithms en_US
dc.type Thesis en_US
dc.institute.department School of Engineering en_US
dc.institute.name University of Bridgeport en_US

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