Information and Learning Sciences: Volume 124 Issue 9/10


Table of contents - Special Issue: The Datafication of Student Life in Higher Education: Privacy Problems and Paths Forward

Guest Editors: Kyle Jones

Platforms, perceptions, and privacy: ethical implications of student conflation of educational technologies

Spencer P. Greenhalgh, Daniela K. DiGiacomo, Sarah Barriage

The purpose of this paper is to examine how higher education students think about educational technologies they have previously used – and the implications of this understanding…

PIILO: an open-source system for personally identifiable information labeling and obfuscation

Langdon Holmes, Scott Crossley, Harshvardhan Sikka, Wesley Morris

This study aims to report on an automatic deidentification system for labeling and obfuscating personally identifiable information (PII) in student-generated text.

Tracking transparency: an exploratory review of Florida academic library privacy policies

Emily Zoe Mann, Stephanie A. Jacobs, Kirsten M. Kinsley, Laura I. Spears

Building on past studies of library privacy policies, this review looks at how privacy information is shared at universities and colleges in the state of Florida. Beyond the…

“Trust us,” they said. Mapping the contours of trustworthiness in learning analytics

Sharon Slade, Paul Prinsloo, Mohammad Khalil

The purpose of this paper is to explore and establish the contours of trust in learning analytics and to establish steps that institutions might take to address the “trust…

Privacy governance not included: analysis of third parties in learning management systems

Madelyn Rose Sanfilippo, Noah Apthorpe, Karoline Brehm, Yan Shvartzshnaider

This paper aims to address research gaps around third party data flows in education by investigating governance practices in higher education with respect to learning management…

Predictive algorithms and racial bias: a qualitative descriptive study on the perceptions of algorithm accuracy in higher education

Stacey Lynn von Winckelmann

This study aims to explore the perception of algorithm accuracy among data professionals in higher education.

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  • Dr Rebecca Reynolds
  • Dr Samuel Chu