Fuzzy Students’ Knowledge Modelling System through Revised Bloom’s Taxonomy

Abstract

The conveniences of web-based educational systems have attracted a large heterogeneous group of learners with various knowledge levels, learning goals, and others learning characteristics, to study online. To enhance the effectiveness of the web-based educational system in delivery knowledge, a system should be capable to identify the learners’ learning characteristics, and adapt the instructional process accordingly. Hence, this paper presented a students’ knowledge modelling system that is capable of infer and updating the students’ knowledge level in accordance to the cognitive processes dimension in the Revised Bloom’s Taxonomy. However, the students’ knowledge modeling process consists of tasks and factors that are vague and unmeasured, thus Fuzzy Logic is integrated into the students’ knowledge modeling system to deal with such uncertainties. The proposed fuzzy students’ knowledge modeling system uses fuzzy sets to represent students’ knowledge level and other influencing factors, and uses Mamdani type inference technique to determine and update knowledge levels.