Abstract
Considering learning and how to improve students' performances, an adaptive educa- tional system must know how an individual learns best. In this context, this work presents an in- novative approach for student modeling through probabilistic learning styles combination. Experi- ments have shown that our approach is able to automatically detect and precisely adjust students' learning styles, based on the non-deterministic and non-stationary aspects of learning styles. Be- cause of the probabilistic and dynamic aspects enclosed in automatic detection of learning styles, our approach gradually and constantly adjusts the student model, taking into account students' performances, obtaining a fine-tuned student model. Promising results were obtained from experi- ments, and some of them are discussed in this paper.
Highlights
Learning Styles (LS) and their effects on learning processes are carefully exposed by Coffield et al (2009)
We propose a stochastic approach based on Markov Chains for automatic and dynamic modeling of students’ LS in Adaptive Educational Systems (AES), which detects and precisely adjusts students’ LS based on non-stationary and non-deterministic aspects of LS, which may change during the learning process in an unexpected and unpredictable way (Graf and Kinshuk, 2009; Graf et al, 2010)
If all real preferences appear in the stochastically generated LS combination (LSC), and there is no occurrence of non-satisfied real preference (NSRP), the students’ performance simulation process (SPSP) takes into account a probability of failure equal to 15% ( LS and many factors exert some influence on students’ performances)
Summary
Learning Styles (LS) and their effects on learning processes are carefully exposed by Coffield et al (2009). We believe that stochastic modeling techniques can cope with this problem, Markov Chains In this context, we propose a stochastic approach based on Markov Chains for automatic and dynamic modeling of students’ LS in AES, which detects and precisely adjusts students’ LS based on non-stationary and non-deterministic aspects of LS, which may change during the learning process (non-stationary) in an unexpected and unpredictable way (non-deterministic) (Graf and Kinshuk, 2009; Graf et al, 2010). Our approach aims to gradually tune students’ LS stored in SM during the learning process Another important aspect of FSLSM is that it considers LS as tendencies and it takes into account the fact that students may act differently according to specific situations, in a non-deterministic way, as pointed out by Kinshuk et al (2009), Graf and Kinshuk (2009).
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