E-SATNet: Evaluating Student Satisfaction with Lecturer Responses in Asynchronous Online Discussions Using Sentiment and Semantic Similarity Analysis

Abstract

Assessing e-learning students’ satisfaction with lecturers’ interactions in asynchronous forums is essential for enhancing teaching and learning processes. The discussion forum allows students to share comments and ideas with peers or lecturers, stimulating diverse perspectives and improving learning efficacy. However, lecturers’ responses are often similar or redundant to previous students’ comments, limiting feedback depth and potentially reducing students’ perceived value of the interaction. Machine learning classifiers have been widely used to assess satisfaction based on sentiment or semantic similarity. However, integrating sentiment and semantic similarity between students’ comments or opinions and lecturers’ responses in asynchronous online discussion forums has received limited attention and may be improved. Through this research, we propose a novel model called E-learning Satisfaction Assessment using Textual Neural Network (E-SATNet). The E-SATNet model has two main sub-networks. The first sub-network employs a Convolutional Neural Network (CNN) to extract sentiment-related features from students’ reactions to lecturers’ responses. The second sub-network utilizes a Bidirectional Long Short-Term Memory (BiLSTM) to extract semantic features from lecturers’ responses and compute their similarity with the overall discussion content. Evaluation results show that E-SATNet effectively assesses satisfaction, achieving an average F1-score of 88.12.