Fine-grained Main Ideas Extraction and Clustering of Online Course Reviews

Abstract

Online course reviews have been an essential way in which course providers could get insights into students' perceptions about the course quality, especially in the context of massive open online courses (MOOCs), where it is hard for both parties to get further interaction. Analyzing online course reviews is thus an inevitable part for course providers towards the improvement of course quality and the structuring of future courses. However, reading through the often-time thousands of comments and extracting key ideas is not efficient and will potentially incur non-coverage of some important ideas. In this work, we propose a key idea extractor that is based on fine-grained aspect-level semantic units from comments, powered by different variations of state-of-the-art pre-trained language models (PLMs). Our approach differs from both previous topic modeling and keyword extraction methods, which lies in: First, we aim to not only eliminate the heavy reliance on human intervention and statistical characteristics that traditional topic models like LDA are based on, but also to overcome the coarse granularity of stateof-the-art topic models like top2vec. Second, different from previous keyword extraction methods, we do not extract keywords to summarize each comment, which we argue is not necessarily helpful for human readers to grasp key ideas at the course level. Instead, we cluster the ideas and concerns that have been most expressed throughout the whole course, without relying on the verbatimness of students' wording. We show that this method provides high and stable coverage of students' ideas.

Geography of Technology Transfer in China

“…Moreover, integrating learning data, such as the examinee's prior interactions with materials, can offer a longitudinal perspective on their learning trajectory and readiness for new concepts. Additionally, the analysis of educational content, encompassing textual, visual, and auditory materials [136,169], allows for a richer understanding of how examinees interact with multifaceted information. Such machine learning-driven approaches promise to refine CAT systems comprehensively, enabling them to deliver assessments that are not just accurate reflections of an examinee's proficiency but also predictive of their potential for future learning.…”

core technology behind and beyond ChatGPT

“…The trend in the use of language models in student feedback can be seen as a development towards more efficient and accurate ways of measuring students' opinions and perceptions. Previous studies (Esmaeilzadeh et al, 2022;Masala et al, 2021;Xiao et al, 2022) show the advancements in this area of research.…”

“…This is a significant improvement in terms of efficiency as it reduces the amount of data to be analyzed and allowing for faster and more accurate analysis of student feedback. The work of Xiao et al (2022) focuses on getting insights into students' perceptions about course quality in MOOCs.…”