The case of Canvas: Longitudinal datafication through learning management systems

ABSTRACT

The Canvas Learning Management System (LMS) is used in thousands of universities across the United States and internationally, with a strong and growing presence in K-12 and higher education markets. Analyzing the development of the Canvas LMS, we examine 1) ‘frictionless’ data transitions that bridge K12, higher education, and workforce data 2) integration of third party applications and interoperability or data-sharing across platforms 3) privacy and security vulnerabilities, and 4) predictive analytics and dataveillance. We conclude that institutions of higher education are currently ill-equipped to protect students and faculty required to use the Canvas Instructure LMS from data harvesting or exploitation. We challenge inevitability narratives and call for greater public awareness concerning the use of predictive analytics, impacts of algorithmic bias, need for algorithmic transparency, and enactment of ethical and legal protections for users who are required to use such software platforms.

KEYWORDS:

• Data ethics
• data privacy
• predictive analytics
• higher education
• dataveillance

Disclosure statement

No potential conflict of interest was reported by the author(s).

ORCID

Roxana Marachi http://orcid.org/0000-0002-3483-4121

Lawrence Quill http://orcid.org/0000-0002-7020-6106

Correction Statement

This article has been republished with minor changes. These changes do not impact the academic content of the article.

Notes

1 In 2017, a student discovered how to un-mute his ‘muted’ grade and made a YouTube video documenting the exploit. (CanvasLMS 2017). https://community.canvaslms.com/thread/16830-student-discovered-his-muted-grade.

2 Advertising ID: ‘Transmitted during testing’ Explanation: ‘The Android Advertising ID (AAID is used for tracking and behavioral advertising. You can modify the settings of your phone to reset this identifier, to prevent tracking over time, or opt-out of behavioral advertising altogether.’

3 Android ID: ‘Transmitted during testing’ Explanation: ‘The Android ID is a random serial number that is created when you first configure your phone. It is a globally unique identifier that could be used to track you over time and across apps, and can only be reset by performing a factory reset of your phone.’

4 Device Descriptions: ‘Transmitted during testing’ Explanation: ‘This refers to a list of your phone’s current configuration parameters, including a description of your phone’s hardware and software. While this is often used by developers to collect performance data and detect software bugs, it could also be used to construct a unique “fingerprint” of your device for tracking purposes.’