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Using a Planning Prompt Survey to Encourage Early Completion of Homework Assignments

Published: 01 June 2022 Publication History

Abstract

In an earlier study we showed that small amounts of extra credit offered for early progress on online homework assignments can reduce cramming behavior in introductory physics students. This work expands on the prior study by implementing a planning prompt intervention inspired by Yeomans and Reich's similar treatment. In the prompt we asked students to what degree they intended to earn extra credit offered for early work on the module sequence, and what their plan was to realize their intentions. The survey was assigned for ordinary course credit and due several days before the first extra credit deadline. We found that students who completed the prompt earned on average 0.6 more extra credit points and completed the modules an average of 1.1 days earlier compared to a previous semester. We detect the impact of the survey by creating a multilinear model based on data from students exposed to the intervention as well as students in a previous semester. Data from five homework sequences are included in the model to account for differences between the two semesters that cannot be attributed to the planning prompt intervention.

References

[1]
David S. Ackerman and Barbara L. Gross. 2005. My instructor made me do it: Task characteristics of procrastination. J. Mark. Educ. 27, 1 (2005), 5--13.
[2]
Center for Distributed Learning. Obojobo.
[3]
Scott Cunningham. 2021. Causal Inference: The Mixtape. Yale University Press, New Haven.
[4]
Zachary Felker and Zhongzhou Chen. 2020. The impact of extra credit incentives on students' work habits when completing online homework assignments. In 2020 Physics Education Research Conference Proceedings, American Association of Physics Teachers, Virtual Conference, 143--148.
[5]
James G MacKinnon and Halbert White. 1985. Some heteroskedasticity-consistent covariance matrix estimators with improved finite sample properties. J. Econom. 29, 3 (September 1985), 305--325.
[6]
Shelby H. McIntyre and J Michael Munson. 2008. Exploring cramming: Student behaviors, beliefs, and learning retention in the principles of marketing course. J. Mark. Educ. 30, 3 (2008), 226--243.
[7]
Sarath A. Nonis and Gail I. Hudson. 2010. Performance of College Students: Impact of Study Time and Study Habits. J. Educ. Bus. 85, 4 (2010), 229--238.
[8]
Michelle Taub, Allison Banzon, Tom Zhang, and Zhongzhou Chen. Tracking changes in students' online SRL behaviors and achievement goals using trace clustering and process mining. Accepted to: Front. Psychol.
[9]
Michael Yeomans and Justin Reich. 2017. Planning prompts increase and forecast course completion in massive open online courses. In ACM International Conference Proceeding Series, Association for Computing Machinery, 464--473.
[10]
Tom Zhang, Michelle Taub, and Zhongzhou Chen. 2021. Measuring the impact of COVID-19 induced campus closure on student self-regulated learning in physics online learning modules. ACM Int. Conf. Proceeding Ser. 1, 1 (2021), 110--120.

Cited By

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  • (2024)Which Planning Tactics Predict Online Course Completion?Proceedings of the 14th Learning Analytics and Knowledge Conference10.1145/3636555.3636891(360-370)Online publication date: 18-Mar-2024
  • (2024)Viewing tailored nudges is correlated with improved mastery‐based assessment scoresBritish Journal of Educational Technology10.1111/bjet.1345155:5(1841-1859)Online publication date: 11-Mar-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

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  1. Using a Planning Prompt Survey to Encourage Early Completion of Homework Assignments

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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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      New York, NY, United States

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      Published: 01 June 2022

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      Author Tags

      1. SRL
      2. extra credit
      3. online learning
      4. planning prompt
      5. 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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      Overall Acceptance Rate 117 of 440 submissions, 27%

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      Cited By

      View all
      • (2024)Which Planning Tactics Predict Online Course Completion?Proceedings of the 14th Learning Analytics and Knowledge Conference10.1145/3636555.3636891(360-370)Online publication date: 18-Mar-2024
      • (2024)Viewing tailored nudges is correlated with improved mastery‐based assessment scoresBritish Journal of Educational Technology10.1111/bjet.1345155:5(1841-1859)Online publication date: 11-Mar-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

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