D.Sc. in IT Dissertation Defense

Aug. 16, 2016 at 11:00am in YR459

An Intelligent and Effective E-learning System (IELS)

Dehui Li

Advisor: Dr. Harry Zhou

Abstract

The traditional teaching environment is usually thought to be that of a classroom: a single teacher giving lectures to a group of students who are expected to use their notes and textbook to prepare for periodic examinations and demonstrate that they have learned. An obvious problem with this approach is that everyone receives the same lecture within a fixed time frame. With so many students with different levels of understanding, it is impossible for the teacher to provide tailored lessons to every student. Most of the E-learning systems lack artificial intelligence and merely present the content materials without evaluating the students’ comprehension and competence. The lecture materials in traditional e-learning systems are presented in a predefined order and within a certain timeframe regardless the student’s understanding of the topic being discussed. They cannot handle a large and potentially diverse student population. In responses to these challenges and difficulties, this dissertation proposes, designs, implements, and tests a course delivery system that provides individualized lessons to students based on their levels of comprehension, progress and weakness. By analyzing the student’s statistic data on his/her background and intellectual ability, and dynamic data collected during a lecture session in real time, our system is able to provide personalized lessons with different levels of difficulty. An Intelligent and Effective E-learning System(IELS) evaluates the student’s real time learning activity, determines their competency level, analyzes their progress, and selects appropriate teaching materials. Good students can finish a lecture unit much faster than others, while the students at the introductory level may take longer. All students, hopefully, can meet the lecture objective at the end. A variety of students with different backgrounds and abilities can benefit from this effective, efficient and individualized pedagogical strategy. IELS employs three artificial intelligent components in its design: a knowledge base, a case base and a fuzzy reasoning mechanism. The knowledge base captures the expertise of domain subject experts and uses it to dynamically construct a lecture content based on the student’s competency. The case base enables IELS to recognize similar situations and recall and adapt its past course content for students with similar characteristics. The fuzzy reasoning component allows ITS to conduct approximate reason and handle vague and imprecise terms. By combing expert’s knowledge, analogical reasoning and fuzzy reasoning, IELS demonstrates its adaptive ability to deliver personalized courses to students. To show its benefits and feasibility, IELS has been tested in the domain of computer science courses, but its design and structure promise to be domain-independent. Without any structure changes, any domain subject expert, such as in the fields of SAT, GRE, MCAT or any college courses, can input their lectures with ease. The potential applications of  IELS are promising and unlimited.