Learning Analytics
By Sharon Slade, Paul Prinsloo. 2013
540 citations recorded by the source corpus.
The field of learning analytics has the potential to enable higher education institutions to increase their understanding of their students’ learning needs and to use that understanding to positively influence student learning and progression. Analysis of data relating to students and their engagement with their learning is the foundation of this process. There is an inherent assumption linked to learning analytics that knowledge of a learner’s behavior is advantageous for the individual, instructor, and educational provider. It seems intuitively obvious that a greater understanding of a student cohort and the learning designs and interventions they best respond to would benefit students and, in turn, the institution’s retention and success rate. Yet collection of data and their use face a number of ethical challenges, including location and interpretation of data; informed consent, privacy, and deidentification of data; and classification and management of data. Approaches taken to understand the opportunities and ethical challenges of learning analytics necessarily depend on many ideological assumptions and epistemologies. This article proposes a sociocritical perspective on the use of learning analytics. Such an approach highlights the role of power, the impact of surveillance, the need for transparency, and an acknowledgment that student identity is a transient, temporal, and context-bound construct. Each of these affects the scope and definition of learning analytics’ ethical use. We propose six principles as a framework for considerations to guide higher education institutions to address ethical issues in learning analytics and challenges in context-dependent and appropriate ways.
Subfields: Computer Science Applications, Education
View Paper: https://www.semanticscholar.org/paper/30ff2e007a1791977c59d061a2d0f7d3177d5a22
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In Computer Science Applications
In Education
Citations in the corpus
- Ethical principles for artificial intelligence in education (2022, background): “These incorporate the spheres of data interpretation and management, different perspective on the data usage, and the power relation among involved stakeholders such as stu - dents, teachers, and the educational objectives (Slade & Prinsloo, 2013).”
- Learning Analytics Methods, Benefits, and Challenges in Higher Education: A Systematic Literature Review. (2016, background): “To accomplish privacy and educational goals, Slade and Prinsloo (2013) proposed the use of an ethical framework based on six principles that strives to maintain educational effectiveness for instructors and students.”