Voice based answer evaluation system for physically disabled students using natural language processing and machine learning

In the modern educational process, there is a need to automate response assessment systems. The task of the reviewer becomes more difficult when analyzing theoretical answers, because online assessment of answers is available only for questions with multiple choice answers. The teacher carefully examines the answer before giving the appropriate mark. The existing approach requires additional staff and time to study the responses. This article introduces a natural language processing and machine learning response-based app that includes a voice prompt for visually impaired students. The application automates the process of checking subjective responses by considering text extraction, feature extraction, and score classification. Evaluation measures, such as Term Frequency-Inverse Document Frequency (TF-IDF) similarity, vector similarity, keyword similarity, and grammar similarity, are considered to determine the overall similarity between teacher outcome and system evaluation. The conducted experiments showed that the system evaluates the answers with an accuracy of 95 %. The proposed methodology is designed to assess the results of exams for students who cannot write but who can speak. The application of the developed application allows reducing the labor costs and time of the teacher by reducing manual labor.