"Using Clickstream Data Mining Techniques to Understand and Support First-Generation College Students in an Online Chemistry Course"

​Assistant Professor Fernando Rodriguez, third-year doctoral student Hye Rin Lee, alumna Teomara Rutherford, research affiliate Christian Fischer, Professor Eric Potma (Chemistry), Professor Mark Warschauer published an article in the Proceedings of the 11th International Learning Analytics and Knowledge Conference – LAK21 exploring the use of clickstream data mining techniques.
 
The title of the article is “Using Clickstream Data Mining Techniques to Understand and Support First-Generation College Students in an Online Chemistry Course.”
 
Rodriguez is an assistant professor of teaching at UC Irvine’s School of Education and a research affiliate in the Online Learning Research Center. His research foci include using learning analytics to better understand student achievement in technology-enhanced and online STEM courses; using cognitive theories of learning to understand how students study, and whether using effective study strategies (spacing, self-testing) promotes learning in STEM courses; and understanding college students’ critical thinking abilities, especially in the context of reading misleading and fake news. Previously, Rodriguez was a postdoctoral scholar in the Digital Learning Lab, managing the NSF-funded project, Investigating Virtual Learning Environments. Before joining UC Irvine, he worked at WestEd helping schools make data-driven decisions that improved learning outcomes in classrooms.

Fernando Rodriguez

​Abstract
 
Although online courses can provide students with a high-quality and flexible learning experience, one of the caveats is that they require high levels of self-regulation. This added hurdle may have negative consequences for first-generation college students. In order to better understand and support students’ self-regulated learning, we examined a fully online Chemistry course with high enrollment (N = 312) and a high percentage of first-generation college students (65.70%). Using students’ lecture video clickstream data, we created two indicators of self-regulated learning: lecture video completion and time management. Performing a k-means clustering on these indicators uncovered four distinct self-regulated learning patterns: (1) Early Planning, (2) Planning, (3) Procrastination, and (4) Low Engagement. Early Planning behaviors were especially important for course success—they consistently predicted higher final course grades, even after controlling for important demographic variables. Interestingly, first-generation college students classified as Early Planners achieved at similar levels as their non-first-generation peers, but first-generation students in the Low Engagement group had the lowest average grades among students. Overall, our results show that self-regulation may be an important skill for determining first-generation students’ STEM achievement, and targeting these skills may serve as a useful way to support their specific learning needs.