Student Modeling Using NN-HMM for EFL Course

A new approach for building student model in an Adaptive and intelligent Web-based educational system (AIWBES) is introduced. This approach utilizes a hybrid algorithm based on Fuzzy-ART2 neural network and stochastic method called Hidden Markov Model (HMM), in order to evaluate and categorize students´ knowledge status in six levels: Excellent, very good, good, fair, weak and very weak; depending on 5 parameters collected through their interactions with the system. The student model is initialized by presenting a pre-test form to students and it is updated dynamically according to their study times and assessment results. Students´ knowledge status are modeled through three phases, initialization, training and recall phases. In the initialization phase, input vectors are normalized before they are categorized using unsupervised algorithm Fuzzy-ART2 in 6 clusters representing 6 knowledge status. A HMM is created for each cluster and when new students´ parameters are collected, they are introduced to Baum- Welch re-estimation algorithm to train the 6 HMMs and to maximize the observed sequence that is associated with a particular cluster. Forward algorithm evaluates then the likelihood of this sequence with respect to each of the HMMs and to determine the maximum value, which represents the actual knowledge status of the student. Experiment results show that the proposed approach is capable of categorizing student parameter vectors to their corresponding cluster with good accuracies. The result of such classifications would open new horizons and applications in AIWBES.