Most people have waited until the last minute to complete a school assignment at some point in their lives, but a new study finds that first-generation students and those belonging to underrepresented ethnic and racial groups take assignments later, on average. than their non-marginalized students. peers.
In the largest study of its kind to date, Sunil Sabnis ’22 analyzed anonymous student grades and assignment submission times for more than 2,000 courses at a major US university.
“We found that there are significant differences in procrastination between sociodemographic groups,” said Sabnis, who studied computer science. “More marginalized and vulnerable populations have unique experiences that can cause those differences.”
Sabnis presented this work at the Learning at Scale 2022 conference in a paper entitled, “Large-scale student data reveal sociodemographic gaps in procrastination† He received the Best Undergraduate Paper Award for the study.
“We believe this is a group-level phenomenon related to the systemic problems faced by college students, especially first-generation students and students of color,” said co-author Rene Kizilcec, assistant professor in the Department of Information Science at Cornell Ann S. Bowers College of Computing and Information Science. “These groups face unique challenges in navigating college — called the hidden curriculum — and they may have part-time jobs to make ends meet. Those are just some of the factors that we believe are driving the vast differences in procrastination. that we see in the data.”
As a frequent procrastinator, Sabnis was interested in the impact of procrastination on the performance of first-generation and minority students. For decades, marginalized students underperformed in college compared to their white and college peers, and previous research has shown that procrastination negatively affects grades and well-being. Sabnis wanted to know whether procrastination plays a role in this performance gap.
He analyzed approximately 2.6 million submission records from more than 25,000 students, using data obtained from Canvas, the learning management system at the university he studied. He merged the anonymized data with the registrar’s student demographics to see if certain groups of students had a later average turn-in time than others.
Racial minority individuals and first-generation students procrastinate more than other groups, he found. As expected, students who submitted assignments later performed worse than students who submitted them earlier. The impact of the delay on their grades was enough to explain about 20% of the performance gap between marginalized groups and their peers.
In contrast to previous research in this area, which looked at submission times in relation to a deadline, Sabnis compared the submission times for groups of students. This “allows us to run a new behavior-based stock-based analysis in our paper,” he said.
This method also provides educators with an easy and scalable way to identify procrastinators in their classes who could benefit from additional help. It could also be a way to measure the success of interventions for at-risk learners, to see if they reduce procrastination.
The study provides evidence that the relationship between procrastination and grades varies significantly between courses in the dataset, suggesting that some courses are better than others at minimizing the damage caused by procrastination. Finding out which of these strategies are effective may be a focus for future research. By providing this information to instructors, they could tinker with their courses — perhaps calculating grades differently, shifting deadlines, or leaving room for late work — to lessen the penalty caused by procrastination.
Finally, this highlights the value of tapping into the wealth of data in learning management systems to look not just at student performance, but behavior as well, Kizilcec said. He hopes it will encourage more equity-driven research into learning analytics.
“We are just at the beginning of opening the black box of learning management systems used by universities around the world,” Kizilcec said. “Using educational data science, we can understand the behavioral sources of educational inequalities and develop new approaches to close long-standing performance gaps.”
Renzhe Yu of the University of California, Irvine is a co-author of the paper.
Patricia Waldron is a writer for the Cornell Ann S. Bowers College of Computing and Information Science.