Decision Strategy with Reinforcement Learning Based Chinese Exercise Recommendation Auxiliary Teaching System

Chinese Exercise recommendation is the essential phase for enabling the personalized learning. Considering suitable exercises facilitated the learners' learning. Chinese Exercise recommendation is the particular exercise recommendation application. Hence, recommendation framework to Chinese language learning issues which combine learning styles of learners is developed. Moreover, certain challenges are to be resolved like quantifying learning behaviour, describing Chinese language learning issues and quantifies the learning strategies. In this manuscript, developed the Decision Strategy with Reinforcement Learning (DS-RL) is proposed with Markov Decision Process (MDP) offers possible improve for learning experience on every student offering with personalized and efficient learning path. Employing MDP and DS-RL algorithms like Q-learning using Sequential Path Recommendation (SRP) and learning growth is much obtainable. The proposed algorithm obtained less training time of 4.120s, average reward of 6.85±1.48, standard deviation of 2.12001, maximum reward of 8.0, minimum reward of 3.0, optimal action considered of 5, mean success rate of 79.65%, mean step rage of 16.06, number of training time of optimum rate 7 and good reward of 36942.35068873256.