Lessons in empathy

Online education is now ubiquitous and in recent years has changed fundamentally the way many people learn. Various platforms have opened up access to knowledge for millions of people. However, there remains an ongoing challenge: how to accurately measure and enhance the quality of teaching in these digital spaces.

Conventional evaluation tools focus on test scores and student satisfaction surveys. However, these often overlook the students’ emotional experience of the course. Research in the International Journal of Information and Communication Technology, proposes a new solution that could change the way online teaching is assessed, getting closer to the heart of emotional matters.

The new work by Ruiting Bai of Puyang Medical College in Puyang, China, introduces the EduSent-Dig model, which can carry out advanced sentiment analysis and use big data techniques to evaluate teaching quality. By analysing the student emotional response given in their course feedback, the model can extract the nuances of online teaching that work most effectively. Rather than flagging the feedback as simply “positive” or “negative”, EduSent-Dig identifies specific emotional undercurrents such as joy, frustration, or surprise. It does so by using analytical tools such as Bi-LSTM, a deep learning framework, and Word2Vec, which converts words into numerical representations for computational analysis.

The study reveals that emotional experiences are not just peripheral to learning; they are central to it. How students feel about their coursework directly affects their motivation, engagement, and whether they complete a course. As such, the new model in identifying and interpreting sentiment accurately, can provide educators and course designers with insights into how to improve their educational offering. Moreover, real-time sentiment analysis undertaken as a course progresses might even allow teachers to fine tune their teaching dynamically, tailoring lessons to student needs on an ad hoc basis. This could transform the way courses are designed and how they are developed as the students progress through them. All in, the insights could foster a more empathetic and effective learning environment.

Bai, R. (2024) ‘Big data-driven deep mining of online teaching assessment data under affective factor conditions’, Int. J. Information and Communication Technology, Vol. 25, No. 11, pp.35–51.