Recommender System for Predicting Students' Academic performance in association with Cognitive state and Affective state using Sentiment Analysis and Association Rule Mining on the closed ended questionnaire

The recommender systems (RS) are significant in academics, business, and industry. They are frequently employed in various fields, including shopping, music, movies, travel, dining, and writing. Recently, RS can be used in education to suggest student learning styles. This paper proposes a recommender system for predicting student personality with emotions. One of the common recommender system methodologies, collaborative filtering, generates the best suggestions by finding related individuals or things based on their prior transactions. One of the main issues with the collaborative filtering process is the poor accuracy of ideas. This paper uses association rule mining to recommend student personality with emotion based on closed-ended questionnaires. This work initially uses the sentiment analysis technique to identify the student's emotions based on the answer provided for a closed-ended questionnaire. Then, polarity-based sentiment analysis is used to classify student emotions. This paper uses the Association rule mining concept to predict student personality with emotion. This is the first study of a recommender system for the student based on closed-ended questionnaires. The real- world closed-ended questionnaire like Emotional intelligence, Eysenck personality, Self-determination scale, Self-efficacy, Rosenberg's self-esteem, Positive and Negative affect schedule, and Oxford Happiness is used to evaluate the performance of the proposed research work.