Pages 31–55

Authors

Kitichart Nukaew and Arinya Pongwat

Abstract

Massive Open Online Courses (MOOCs) have seen continuous growth in popularity and rapid expansion. In the instructional design process, receiv-ing feedback from learners is crucial, as it helps tailor the content to better meet learners' needs. The application of NLP models in analyzing learners' feedback is an effective approach for extracting insights from a large volume of comments related to the courses. These models can categorize feedback into three distinct categories: course, instructors, and assessments. Addi-tionally, the models can predict the sentiment of the feedback, determining whether it is positive or negative. In developing these models, semi-supervised learning techniques have been employed to address the chal-lenge of limited data availability. Experimental results indicate that, for feedback categorization, a GRU model combined with tri-training with dis-agreement yields the highest prediction accuracy. Conversely, for sentiment analysis, a GRU model combined with tri-training produces the best out-comes.