Investigating the Importance of Psychological and Environmental Factors for Improving Learner's Performance Using Hidden Markov Model

In the proposed work, hidden Markov model (HMM) has been deployed to improve the learner's performance or grades on the basis of their psychological and environmental factors like connect/gather isolation, pleasure/comfort, depression, trust, anxiety, proper guidance, improper guidance, entertainment, and stress. The categorization of psychological and environmental factors has been done on the basis of two factors as positive and negative. The responsibility of the positive factor is to boost up learner's performance or grades, whereas negative factors reduce learning performance respectively. Finally, this paper addresses the application of HMM to determine the optimal sequence of states for different states as grades A, B, and C for different emission observations. The states identification leads to training the HMM model where optimal value of individual states computed using different observation sequences which determines the probability of state sequences. The probability of achieved optimal states is shown in different logical combinations where best state is searched among available different states using different search techniques. The computational results obtained after training are encouraging and useful