Conclusions and Discussion - Advances in Personalized Web-Based Education - page 117

The ability of the presented novel fuzzy student model to recognize the altera-
tions of the student's learning states and dynamically adapt the presentation of the
learning material accordingly, renders the particular student modeling approach a
novel generic tool for adaptive learning. The gain from the presented approach is
significant as fuzzy logic can be used in combination with overlay and stereotype
models to provide adaptivity and personalization in other interactive systems in
addition to educational applications. The application of this innovative approach is
possible where the user's changeable state and/or preferences are affected by the
existing dependencies among the system's elements (like concepts, preferences,
events, choices).
Contribution to Programming Tutoring Systems
Programming tutoring systems teaches computer programming to learners providing
adaptivity. Mainly, these systems adapt the learning process dynamically to the stu-
dent's knowledge level and needs. However, they do not consider how the learner's
performance in a domain concept affects the learner's knowledge level of other related
domain concepts of the learning material. Consequently, the gain of the presented
approach is the modeling of the learning or forgetting process of a student that gives
the ability to the system to adapt dynamically to each individual learner's needs by
scheduling the sequence of lessons instantly.
The presented approach allows the tutoring system to recognize either the
learner's knowledge level, or the alterations that occur in the learner's knowledge
of a domain concept. Then, the system updates the student's knowledge level of
the domain concepts of the learning material that are related with the concept that
the student has learnt or forgotten. Therefore, the presented novel approach allows
the programming tutoring system to model either the possible increase or decrease
of the learner's knowledge. Furthermore, the presented approach introduces the
reasoning of errors that are related with other programming languages. In particu-
lar, each time the system checks if the learner's errors were due to possible con-
fusion with features of another previously-known programming language. In this
way, the system allows each learner to complete the e-learning course at their own
pace, taking decisions about which concepts have to be delivered, which concepts
need revision and which concepts are known and do not need further reading.
Contribution to Fuzzy Logic
The gain from the novel fuzzy student model is that the learner's knowledge level
is represented in a more realistic way. It considers the fact that the learner's knowl-
edge level is a moving target and models automatically the learning and forgetting
process. In particular, the presented fuzzy student modeling approach helps the