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

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.

Predicting Student Performance Using Clickstream Data and Machine Learning

“…This may explain why it has received insufficient attention from educators and researchers to date. Despite this limitation, studies have used clickstream data and observed connections between click actions and students' learning behaviours [10], confirming its value and potential in student performance prediction tasks. In addition to the introduction (Section 1), Section 2 of this paper presents a literature review on Learning Analytics and Educational Data Mining, student performance prediction, and clickstream data.…”

Using learning analytics to measure self‐regulated learning: A systematic review of empirical studies in higher education

“…The study in (Ye & Pennisi, 2022) used the number of late submission as an indicator of procrastination, while the study in (Ilves et al, 2018) captures anti‐procrastination from two angles using two indicators: the number of days that a student started working on the tasks after they were released, and the average number of days of submission before the deadlines. The studies in (Li et al, 2020) (Papamitsiou & Economides, 2021) (Rodriguez et al, 2021) (Feldman‐Maggor et al, 2022) measured anti‐procrastination based on the proportion of materials studied or tasks completed before the deadlines. Instead of studying procrastination per task (micro level), the study in (Saqr et al, 2019) investigates that on the course level (macro level).…”

“…Instead, they merely evaluate the validity of the proposed measurement by examining their ability to predict students' performance (predictive validity). This approach can be observed in the studies (Lu et al, 2017), (Saqr et al, 2017), (Li et al, 2018), (Ilves et al, 2018), (Saqr et al, 2019), (Montgomery et al, 2019), (Jovanovic et al, 2019), (Gadella et al, 2020), (Rodriguez et al, 2021), (Iraj et al, 2021), (Afzaal et al, 2021), (Cao et al, 2022), (Li et al, 2022), (Rakovi c et al, 2022), and(Feldman-Maggor et al, 2022). While these studies aim to measure SRL, they did not establish a link between the used indicators and SRL processes.…”

Making the Most of Slides and Lecture Captures for Better Performance: A Learning Analytics Case Study in Higher Education

“…The data provided by such lecture capture platforms have been extensively investigated, providing important insights into teaching and learning practices. For example, Rodriguez et al (2021) focused on identifying self-regulated learning patterns based on indicators of video completion and time management. In their research, the click-stream data were used to count the students' clicks on the pre-recorded videos provided by the teacher, classified based on their time-stamps, and used to identify four types of self-regulated behaviours.…”