One of the most important functions of distance learning systems is determining the student knowledge level and performance clearly. In traditional education systems, students can be assessed in single-stage via tests and homework studies, which consist of multiple-choice questions. However, this method cannot provide accurate results since it is not able to evaluate student knowledge level and question difficulty level. In this chapter, a system and software structure that can determine student knowledge levels, topic difficulty level, and question difficulty levels according to instant student answers for the exam is introduced. In forming student knowledge levels, content monitoring and test data taken from distance education vocational school were used. In this way, more accurate results have been obtained. The fuzzy logic technique has been used to determine (classify) student knowledge levels and topic difficulty levels clearly. In order to determine next questions adaptively, the stored questions have been classified with division clustering methods, and the most suitable questions for the related student knowledge level have been found by using the nearest neighbor algorithm.
Implementing WordNet Measures of Lexical Semantic Similarity in a Fuzzy Logic Programming System
