Using Textual Analysis to Examine Student Engagement in Online Undergraduate Science Education

Abstract

This study investigates the relationship between student engagement and peer review performance in an online data science course. Employing a mixed-methods approach, we analyzed quantitative data on peer review metrics (word count, sentiment, and complexity) and qualitative insights from student comments from 2019 to 2021. Our findings indicate a positive correlation between higher student grades and more detailed, positive peer reviews, as measured by word count, sentiment, and keyword matches. While the overall changes in engagement metrics were not statistically significant, trends suggest increased engagement over time. These findings contribute to a growing body of research on the effectiveness of peer review in online learning environments and highlight its potential to foster student development in data science. Future research should address limitations such as sample size and explore broader applications across diverse online courses. Supplementary materials for this article are available online.

Keywords

• Data science education
• Learning outcomes
• Natural language processing (NLP)
• Online learning environments
• Sentiment analysis
• Student engagement