Applying Sentiment Analysis and Machine Learning Algorithms on Students’ Reflections to Identify an Effective Teaching Strategy as a Factor of Learning Successes
This paper proposes an idea of applying sentiment analysis and machine learning algorithms to analyze over 30 thousand students’ opinions, from a review site on 2,183 instructors teaching at the department of computer science in 43 universities in California, United States. In this proposal paper, we propose using a sentiment analysis method to analyze and classify students’ opinions into 3 classes, namely positive, negative, and neutral. Further, we suggest applying machine learning algorithms on the classified opinions of students and some other objective data from the review site to identify an effective teaching strategy as a crucial factor of students’ learning successes. Eventually in the discussion, we propose a theoretical framework, based on the expected outcome of this research to strengthen the theory of technology-mediated pedagogy.
Sirithumgul, Pornpat and Prasertsilp, Pimpaka, "Applying Sentiment Analysis and Machine Learning Algorithms on Students’ Reflections to Identify an Effective Teaching Strategy as a Factor of Learning Successes" (2022). ACIS 2022 Proceedings. 67.