Prototype for Analyzing Instructor Profiles in Online Courses: A Fuzzy Logic-Based Approach

Abstract

Online education has grown exponentially in recent years, becoming the main form of education in many countries due to the global pandemic. However, evaluating the performance of online instructors can be challenging as obtaining accurate feedback from students is not always easy. This research project proposes an intelligent evaluation model for analyzing the instructor’s profile in online courses using fuzzy deformable prototypes. By identifying the personality traits and emotions expressed by students in surveys, the model evaluates the instructor’s personality based on the five main traits of psychology: openness to experience, conscientiousness, agreeableness, extraversion, and neuroticism. The results obtained can help instructors improve their methodologies and communication, providing better quality online education. The proposed method involves data cleaning, natural language processing, and fuzzy logic analysis, and the results are presented in a user-friendly web interface. The approach has the potential to revolutionize instructor evaluation in online education.