An intelligent and effective e-learning system that provides tailored lessons to students
| dc.contributor.advisor | Zhou, Hayong Harry | |
| dc.contributor.author | Li, Dehui | |
| dc.contributor.department | Towson University. Department of Computer and Information Sciences | en_US |
| dc.date.accessioned | 2017-07-14T20:05:43Z | |
| dc.date.available | 2017-07-14T20:05:43Z | |
| dc.date.issued | 2017-07-14 | |
| dc.date.submitted | 2016-12 | |
| dc.description | (D. Sc.) -- Towson University, 2016 | en_US |
| dc.description.abstract | [From Introduction] In responses to these challenges and difficulties, this dissertation proposes designs, implements, and tests An Intelligent and Effective E-learning System (IELS) that provides individualized lessons to students based on their levels of comprehension, progress and weakness. IELS combines expert knowledge, analogical reasoning and fuzzy reasoning to provide tailored lessons to students. 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, IELS is able to provide personalized lessons with different levels of difficulty for students with diverse backgrounds. 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 of a lecture. A variety of students with different backgrounds and abilities can benefit from this effective, efficient and individualized pedagogical strategy. The knowledge base in IELS 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 in IELS enables it to recognize similar situations and recall and adapt its past course content for students with similar characteristics. The fuzzy reasoning component allows IELS 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. In this dissertation we will give a brief description on the System design, major component, and experiment of IELS. | en_US |
| dc.description.tableofcontents | System organization and major components -- Theoretical framework and feature -- User interface and flow chart -- Research questions -- Conclusions and suggestions for future research | |
| dc.description.uri | https://cdm17252.contentdm.oclc.org/digital/collection/etd/id/56083 | en_US |
| dc.format | application/pdf | |
| dc.format.extent | ix, 102 pages | en_US |
| dc.genre | dissertations | en_US |
| dc.identifier | doi:10.13016/M29P2W611 | |
| dc.identifier.other | DF2016Li | |
| dc.identifier.uri | http://hdl.handle.net/11603/4357 | |
| dc.language.iso | en_US | en_US |
| dc.title | An intelligent and effective e-learning system that provides tailored lessons to students | en_US |
| dc.title.alternative | An intelligent and effective e-learning system (IELS) | en_US |
| dc.type | Text | en_US |