An Automated Adaptive Mobile Learning System Using Optimal Shortest Path Algorithms
Loading...
Authors
Alshalabi, Ibrahim Alkore
Issue Date
2016-07-05
Type
Thesis
Language
en_US
Keywords
Adaptive learning , Course content , Learning style , Mobile learning , Shortest path , System prototype
Alternative Title
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.
Description
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.