Social Media Data Analysis for Enhancing Student Evaluation of Teaching Styles

In the realm of education, understanding the impact of different teaching styles on student engagement and satisfaction is essential. Recent advancements in sentiment analysis provide new avenues for evaluating student feedback, particularly through informal channels such as social media. While formal student evaluations offer structured feedback on teaching styles, they may not fully capture the nuanced opinions and sentiments expressed by students in informal settings, such as social media. This research aims to address the gap by integrating sentiment analysis of social media data to evaluate teaching effectiveness across various styles and comparing it with formal evaluation results. This study employs sentiment analysis using the VADER (Valence Aware Dictionary and sEntiment Reasoner) tool to analyze student posts on social media platforms. The analysis includes the extraction of sentiment distributions, identification of common keywords, and tracking of sentiment trends over time. Additionally, formal student evaluations (Likert scale) are collected to offer a direct comparison. The teaching styles analyzed include lecture-based teaching, project-based learning, flipped classrooms, online learning, hybrid learning, and traditional exam-based learning. The findings demonstrate that student sentiment varies significantly across teaching styles. Flipped classrooms and project-based learning received the highest positive sentiment scores, while traditional exam-based teaching showed the most negative sentiment. Social media feedback tended to align with formal evaluations for certain teaching styles, such as the flipped classroom and hybrid learning but showed divergence in others, like online learning, which received higher sentiment in social media feedback. Trends over time reveal evolving sentiments, with fluctuating satisfaction as the academic semester progressed. The integration of social media sentiment analysis provides a more dynamic and real-time understanding of student experiences, offering deeper insights into teaching style effectiveness.