This work is focused on the measurement and prevention of procrastination behavior among college level introductory physics students completing online assignments in the form of mastery-based online learning modules. The research is conducted in two studies. The first study evaluates the effectiveness of offering students the opportunity to earn a small amount of extra credit for completing portions of their homework early. Unsupervised machine learning is used to identify an optimum cutoff duration which differentiates taking a short break during a continuous study session from a long break between two different study sessions. Using this cutoff, the study shows that the extra credit encouraged students to complete assignments earlier. The second study examines the impact of adding a planning-prompt survey prior to a string of assignments. In the survey, students were asked to write a plan for when and where they would work on their online homework assignments. Using a difference in differences method, a multilinear modeling technique adopted from economics research, the study shows that the survey led to students completing their homework on average 18 hours earlier and spreading their efforts on the homework over time significantly more. On the other hand, behaviors associated with disengagement, such as guessing or answer-copying, were not impacted by the introduction of the planning prompt. These studies showcase novel methods for measurement of procrastination behavior, as well as evaluating the effectiveness of the designed interventions to help students avoid waiting until the last minute to make progress on assigned tasks.
| Identifer | oai:union.ndltd.org:ucf.edu/oai:stars.library.ucf.edu:etd2020-2829 |
| Date | 15 August 2023 |
| Creators | Felker, Zachary |
| Publisher | STARS |
| Source Sets | University of Central Florida |
| Language | English |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | Electronic Theses and Dissertations, 2020- |
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