Course signals at Purdue: using learning analytics to increase student success
Abstract
In this paper, an early intervention solution for collegiate faculty called Course Signals is discussed. Course Signals was developed to allow instructors the opportunity to employ the power of learner analytics to provide real-time feedback to a student. Course Signals relies not only on grades to predict students' performance, but also demographic characteristics, past academic history, and students' effort as measured by interaction with Blackboard Vista, Purdue's learning management system. The outcome is delivered to the students via a personalized email from the faculty member to each student, as well as a specific color on a stoplight -- traffic signal -- to indicate how each student is doing. The system itself is explained in detail, along with retention and performance outcomes realized since its implementation. In addition, faculty and student perceptions will be shared.
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Systems like Degree Compass consider a student's selected major and past course achievement, comparing that achievement with similar peers, and then nudge students to enroll in particular classes that will best help undergraduates move through their programs of study most successfully and most expeditiously.
“Systems like Degree Compass consider a student's selected major and his or her achievement in past courses, comparing that achievement with similar peers.37 The system then nudges students to enroll in particular classes that”
From Learning Analytics and the Academic Library: Professional Ethics Commitments at a Crossroads (2018) · cites this paper as [37]
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- Authors
- Arnold, K. E., & Pistilli, M. D.
- Affiliations
- Purdue University West Lafayette (US)
- ORCID
- Pistilli, Matthew D.
- Venue
- Proceedings of the 2nd International Conference on Learning Analytics and Knowledge
- Publisher
- ACM
- Published
- 2012-04-29
- Pages
- 267-270
- OpenAlex
- W2071097428
- Cited by
- 987
- References
- 0
- Citations by year
- 2026: 85 · 2025: 70 · 2024: 63 · 2023: 77 · 2022: 62 · 2021: 66 · 2020: 86 · 2019: 93 · 2018: 86 · 2017: 85 · 2016: 94 · 2015: 54 · 2014: 48 · 2013: 17 · 2012: 1
- FWCI
- 26.43
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- International Conference on Learning Analytics and Knowledge (LAK) (publishedVersion)
- Funders
- Purdue University
- Topics
- Physical Sciences › Computer Science › Computer Science Applications › Online Learning and Analytics
- Physical Sciences › Computer Science › Artificial Intelligence › Intelligent Tutoring Systems and Adaptive Learning
- Social Sciences › Education › Online and Blended Learning
- Keywords
- learning analytics, early intervention, student retention, student performance prediction, real-time student feedback, virtual learning environments, demographic factors, teacher perceptions, student perceptions, Purdue University
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