Modeling Students’ Procrastination in Higher Education: Causes, Outcomes, and Prediction

Abstract

Students spend little time completing tasks when deadlines are far off; however, theytend to increase their work amounts as deadline approaches. This phenomenon, which is called deadline rush, can be modeled by exponential distributions. Deadline reactivity, represented by a rate parameter of the exponential distribution, parameterizes individual differences in procrastination. That is, an individual with high reactivity to deadlines procrastinates more than an individual with low deadline reactivity. While the phenomenon and parametric models of individual differences in procrastination have been investigated, practical applications in the classroom setting have garnered little attention from researchers. Past research on procrastination has not much considered its relationships with learning environment factors and academic performance, with a lack of objective measurements and heavy reliance on self-reported questionnaires. My dissertation will respond to this gap in the research by modeling students’ individual procrastination in university classroom settings, paying close attention to factors influencing the students’ procrastination as well as the effects of procrastination on performance. In particular, the dissertation will answer the following three research questions: (1) Do learning environments (i.e., online learning, task complexity, and time in the academic term) affect students’ procrastination? (2) Does procrastination affect individual and team performance? and (3) How can procrastination be predicted through physiological responses (i.e., eye movement, heart rate, electrodermal activity, and skin temperature)? The first two research questions have been answered by longitudinal field studies, while controlled laboratory experiments are conducted to answer the third research question. My findings shed light on how objective modeling and prediction of procrastination can be applied in the classroom setting. In particular, the findings will provide instructors, researchers, and online learning platforms with practical strategies to better design classes and interventions of procrastination for improvements in students’ performance.