A Hidden Markov Model Approach to Predict Students' Actions in an Adaptive and Intelligent Web-Based Educational system

This paper demonstrates how Hidden Markov Model (HMM) approach is used potentially as a tool for predicting the next concepts visited by students in an Adaptive and Intelligent Web-Based Educational System (AIWBES) for teaching English as Foreign Language (EFL). This tool helps teachers to provide their students with appropriate assistance during the learning process in a timely manner. The prediction process is achieved by following three phases, Initialization phase, adjustment phase and prediction phase. The experiment results are encouraging and serve to show the promise of HMM in AIWBESs and they show accuracy in the next action prediction reaching up to 92%.