A Novel Hybrid Student Model for Personalized Education - Advances in Personalized Web-Based Education - page 66

domain. It uses fuzzy sets to represent the student's knowledge level and a
mechanism of rules over the fuzzy sets, which is triggered after a change has
occurred on the student's knowledge level of a domain concept. This mechanism
updates the student's knowledge level of all related with this concept, concepts.
Its operation is based on the knowledge dependencies that exist between the
domain concepts of the learning material and their “strength of impact” on
each other.
Fuzzy sets are used to characterize the changeable user's state. For example,
(“Unknown”, “Known”, “Learned”} or (“Unknown”, “Insufficiently Known”,
“Known”, “Learned”, “Assimilated”} are the fuzzy sets of educational adap-
tive systems. Therefore, FS 1 , FS 2 , … , FS n are the defined fuzzy sets and µ FS I ,
i = 1, 2, 3, … , n are the corresponding membership functions. Therefore, a set
( µ FS 1 , µ FS 2 , µ FS 3 , ... , µ FS N ) is used to express the student knowledge of a domain
concept with µ FS 1 + µ FS 2 + µ FS 3 +···+ µ FS N = 1 . The fuzzy rules are depicted
in Fig. 3.2 .
Fig. 3.2 The fuzzy rules