Reviews Evaluation for Online Courses: A Deep Learning Technique / Approach

Computer Engineering

The amount of information available online is become harder to keep up with, which has increased information overload. Recommender systems have been developed to address this problem and can offer users learning resources depending on their interests. Students from MOOCs frequently discuss their educational experiences and other course-related topics in the discussion section. These comments can be an indication of how students feel about taking online classes. However, the semantic information tucked away in these comments might assist teachers in improving the appeal of their courses and assist other students in choosing better courses. There hasn't been much research done lately on using review mining to evaluate courses. An assessment method is developed using MOOC reviews for curriculum from diverse disciplines. A topic-word distribution matrix and a comment-topic distribution matrix are produced using the Latent Dirichlet Allocation (LDA) technique. A LSTM classification model and an auto-encoder are used to determine each subject's emotional value. We develop a comprehensive technique for assessing courses on a range of topics by combining subjective and objective evaluations.

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