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Large-Scale Student Data Reveal Sociodemographic Gaps in Procrastination Behavior

Published: 01 June 2022 Publication History

Abstract

University students have to manage complex and demanding schedules to keep up with coursework across multiple classes while navigating formative personal, cultural, and financial events. Procrastination, the act of deferring study effort until the task deadline, is therefore a prevalent phenomenon, but whether it is more common among historically disadvantaged students is unknown. If systematic differences in procrastination behavior exist across sociodemographic groups, they may also contribute to achievement gaps, considering that procrastination is largely negatively associated with academic performance in prior research. We therefore investigate these questions in the context of assignment submission using campus-wide learning management system (LMS) data from a large U.S. research university. We analyze 2,631,893 submission records by 25,659 students across 2,153 courses and propose a context-agnostic procrastination score for each student in each course based on their assignment submission times relative to classmates. Based on this procrastination score, we find significantly higher levels of procrastination behavior among males, racial minorities, and first-generation college students than their peers. However, these differences only explain performance gaps to a very limited extent and the negative association between procrastination behavior and performance remains relatively stable across student groups. This large-scale behavioral study advances the understanding of academic procrastination through an equity lens and informs the development of scalable interventions to mitigate the negative effects of procrastination.

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      cover image ACM Other conferences
      L@S '22: Proceedings of the Ninth ACM Conference on Learning @ Scale
      June 2022
      491 pages
      ISBN:9781450391580
      DOI:10.1145/3491140
      Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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      Published: 01 June 2022

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      Author Tags

      1. educational equity
      2. higher education
      3. learning analytics
      4. learning management system
      5. procrastination
      6. self-regulated learning

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      L@S '22
      L@S '22: Ninth (2022) ACM Conference on Learning @ Scale
      June 1 - 3, 2022
      NY, New York City, USA

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      • (2024)Toward Asset-based Instruction and Assessment in Artificial Intelligence in EducationInternational Journal of Artificial Intelligence in Education10.1007/s40593-023-00382-x34:4(1559-1598)Online publication date: 16-Jan-2024
      • (2023)Reducing procrastination on introductory physics online homework for college students using a planning prompt interventionPhysical Review Physics Education Research10.1103/PhysRevPhysEducRes.19.01012319:1Online publication date: 30-Mar-2023
      • (2023)Building a nationally representative sample of teachers’ online and offline: the Public Instructional Network of School ResourcesJournal of Research on Technology in Education10.1080/15391523.2023.2266060(1-25)Online publication date: 12-Dec-2023

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