skip to main content
10.1145/3231644.3231668acmotherconferencesArticle/Chapter ViewAbstractPublication Pagesl-at-sConference Proceedingsconference-collections
research-article

How a data-driven course planning tool affects college students' GPA: evidence from two field experiments

Published: 26 June 2018 Publication History

Abstract

College students rely on increasingly data-rich environments when making learning-relevant decisions about the courses they take and their expected time commitments. However, we know little about how their exposure to such data may influence student course choice, effort regulation, and performance. We conducted a large-scale field experiment in which all the undergraduates at a large, selective university were randomized to an encouragement to use a course-planning web application that integrates information from official transcripts from the past fifteen years with detailed end-of-course evaluation surveys. We found that use of the platform lowered students' GPA by 0.28 standard deviations on average. In a subsequent field experiment, we varied access to information about course grades and time commitment on the platform and found that access to grade information in particular lowered students' overall GPA. Our exploratory analysis suggests these effects are not due to changes in the portfolio of courses that students choose, but rather by changes to their behavior within courses.

References

[1]
Elizabeth A Armstrong and Laura T Hamilton. 2013. Paying for the Party. Harvard University Press.
[2]
Albert Bandura. 1977. Self-efficacy: Toward a unifying theory of behavioral change. Psychological Review 84, 2 (1977), 191.
[3]
Talia Bar, Vrinda Kadiyali, and Asaf Zussman. 2008. Quest for knowledge and pursuit of grades: information, course selection, and grade inflation. Available at SSRN: https://ssrn.com/abstract=1019580 or (2008).
[4]
Talia Bar, Vrinda Kadiyali, and Asaf Zussman. 2009. Grade information and grade inflation: The Cornell experiment. The Journal of Economic Perspectives 23, 3 (2009), 93--108.
[5]
Elizabeth Bruch and Fred Feinberg. 2017. Decision-making processes in social contexts. Annual review of sociology 43 (2017), 207--227.
[6]
Daniel F Chambliss. 2014. How college works. Harvard University Press.
[7]
Michael D Cohen, James G March, and Johan P Olsen. 1972. A garbage can model of organizational choice. Administrative science quarterly (1972), 1--25.
[8]
Wendy Nelson Espeland and Michael Sauder. 2007. Rankings and reactivity: How public measures recreate social worlds. American journal of sociology 113, 1 (2007), 1--10.
[9]
Wendy Nelson Espeland and Mitchell L Stevens. 2008. A sociology of quantification. European Journal of Sociology/Archives Européennes de Sociologie 49, 3 (2008), 401--136.
[10]
Martin Fishbein. 1979. A theory of reasoned action: some applications and implications. (1979).
[11]
Valen E Johnson. 2006. Grade inflation: A crisis in college education. Springer Science & Business Media.
[12]
René F Kizilcec, Glenn M Davis, and Geoffrey L Cohen. 2017. Towards Equal Opportunities in MOOCs: Affirmation Reduces Gender & Social-Class Achievement Gaps in China. In Proceedings of the ACM Conference on Learning at Scale. ACM, 121--130.
[13]
Joyce B Main and Ben Ost. 2014. The impact of letter grades on student effort, course selection, and major choice: A regression-discontinuity analysis. The Journal of Economic Education 45, 1 (2014), 1--10.
[14]
Ivana Ognjanovic, Dragan Gasevic, and Shane Dawson. 2016. Using institutional data to predict student course selections in higher education. The Internet and Higher Education 29 (2016), 49--62.
[15]
Paul R Pintrich and Akane Zusho. 2007. Student motivation and self-regulated learning in the college classroom. In The scholarship of teaching and learning in higher education: An evidence-based perspective. Springer, 731--810.
[16]
Peter Smith. 1995. On the unintended consequences of publishing performance data in the public sector. International Journal of Public Administration 18, 2-3 (1995), 277--310.
[17]
Claude M Steele. 1997. A threat in the air: How stereotypes shape intellectual identity and performance. American psychologist 52, 6 (1997), 613.
[18]
Karl E Weick. 1976. Educational organizations as loosely coupled systems. Administrative science quarterly (1976), 1--19.
[19]
Philip H Winne. 1995. Inherent details in self-regulated learning. Educational Psychologist 30, 4 (1995), 173--187.
[20]
Wendy Wood. 2000. Attitude change: Persuasion and social influence. Annual Review of Psychology 51, 1 (2000), 539--570.
[21]
Barry J Zimmerman and Dale H Schunk. 2001. Self-regulated learning and academic achievement: Theoretical perspectives. Routledge.

Cited By

View all
  • (2025)AI in the Lecture Room: Analysing Two Use Cases in the Context of Higher EducationEmotional Data Applications and Regulation of Artificial Intelligence in Society10.1007/978-3-031-80111-2_11(183-199)Online publication date: 23-Jan-2025
  • (2024)AdVizor: Using Visual Explanations to Guide Data-Driven Student Advising2024 IEEE VIS Workshop on Visualization Education, Literacy, and Activities (EduVIS)10.1109/EduVIS63909.2024.00008(21-29)Online publication date: 13-Oct-2024
  • (2024)Study path analyses for quality assurance and support of study planningInformatik Spektrum10.1007/s00287-024-01574-y47:3-4(97-104)Online publication date: 23-Sep-2024
  • Show More Cited By

Recommendations

Comments

Information & Contributors

Information

Published In

cover image ACM Other conferences
L@S '18: Proceedings of the Fifth Annual ACM Conference on Learning at Scale
June 2018
391 pages
ISBN:9781450358866
DOI:10.1145/3231644
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].

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 26 June 2018

Permissions

Request permissions for this article.

Check for updates

Author Tags

  1. GPA
  2. dashboard
  3. higher education
  4. information platform
  5. randomized field experiment

Qualifiers

  • Research-article

Conference

L@S '18
L@S '18: Fifth (2018) ACM Conference on Learning @ Scale
June 26 - 28, 2018
London, United Kingdom

Acceptance Rates

L@S '18 Paper Acceptance Rate 24 of 58 submissions, 41%;
Overall Acceptance Rate 117 of 440 submissions, 27%

Contributors

Other Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

  • Downloads (Last 12 months)61
  • Downloads (Last 6 weeks)9
Reflects downloads up to 14 Feb 2025

Other Metrics

Citations

Cited By

View all
  • (2025)AI in the Lecture Room: Analysing Two Use Cases in the Context of Higher EducationEmotional Data Applications and Regulation of Artificial Intelligence in Society10.1007/978-3-031-80111-2_11(183-199)Online publication date: 23-Jan-2025
  • (2024)AdVizor: Using Visual Explanations to Guide Data-Driven Student Advising2024 IEEE VIS Workshop on Visualization Education, Literacy, and Activities (EduVIS)10.1109/EduVIS63909.2024.00008(21-29)Online publication date: 13-Oct-2024
  • (2024)Study path analyses for quality assurance and support of study planningInformatik Spektrum10.1007/s00287-024-01574-y47:3-4(97-104)Online publication date: 23-Sep-2024
  • (2023)Innovating at Campus Scale: The Case of Michigan's AtlasProceedings of the Tenth ACM Conference on Learning @ Scale10.1145/3573051.3596177(306-310)Online publication date: 20-Jul-2023
  • (2023)Impressions and Strategies of Academic Advisors When Using a Grade Prediction Tool During Term PlanningProceedings of the 2023 CHI Conference on Human Factors in Computing Systems10.1145/3544548.3581575(1-18)Online publication date: 19-Apr-2023
  • (2022)Explore Big Data Analytics Applications and Opportunities: A ReviewBig Data and Cognitive Computing10.3390/bdcc60401576:4(157)Online publication date: 14-Dec-2022
  • (2022)Towards strengthening links between learning analytics and assessmentComputers in Human Behavior10.1016/j.chb.2022.107304134:COnline publication date: 27-Jun-2022
  • (2021)Studying Undergraduate Course Consideration at ScaleAERA Open10.1177/23328584219911487Online publication date: 15-Feb-2021
  • (2021)Towards Equity and Algorithmic Fairness in Student Grade PredictionProceedings of the 2021 AAAI/ACM Conference on AI, Ethics, and Society10.1145/3461702.3462623(608-617)Online publication date: 21-Jul-2021
  • (2021)Orienting Students to Course Recommendations Using Three Types of ExplanationAdjunct Proceedings of the 29th ACM Conference on User Modeling, Adaptation and Personalization10.1145/3450614.3464483(238-245)Online publication date: 21-Jun-2021
  • Show More Cited By

View Options

Login options

View options

PDF

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader

Figures

Tables

Media

Share

Share

Share this Publication link

Share on social media