Deriving adaptive fuzzy learner models for Learning-Object recommendation

Adaptive e-learning systems are growing in popularity in recent years. These systems can offer personalized learning experiences to learners, by supplying each learner with learning contents that meet his/her specific interests and needs. The efficacy of such systems is strictly related to the possibility of automatically deriving models encoding the preferences of each learner, analyzing their navigational behavior during their interactions with the system. Since learner preferences may change over time, there is the need to define mechanisms of dynamic adaptation of the learner models so as to capture the changing learner interests. Moreover, learner preferences are characterized by imprecision and gradedness. Fuzzy Set Theory provides useful tools to deal with these characteristics. In this paper a novel strategy is presented to derive and update learner models by encoding preferences of each individual learner in terms of fuzzy sets. Based on this strategy, adaptation is continuously performed, but in earlier stages it is more sensitive to updates (plastic phase) while in later stages it is less sensitive (stable phase) to allow Learning-Object suggestion. Simulation results are reported to show the effectiveness of the proposed approach. © 2009 by Knowledge Systems Institute Graduate School.