Publication InformationAPA Citation
Jones, K. M. L., & LeClere, E. (forthcoming – 2017). Contextual expectations and emerging informational harms: A primer on academic library participation in learning analytics initiatives. In P. Fernandez & K. Tilton (Eds.), Applying library values to emerging technology: Tips and techniques for advancing within your mission. Chicago, IL: Association of College and Research Libraries.
Learning analytics (LA) technologies in higher education institutions (HEIs) may bring about significant gains. Advocates of this Big Data-style technology argue that operational efficiencies and, as a result, financial savings are among the most important benefits. Moreover, LA will purportedly increase student learning outcomes by enabling instructors and student support staff to develop customized learning paths and academic experiences better matched to student needs. Recently, academic libraries have begun to consider their role in LA. Some librarians have expressed great optimism about data-driven analytics and the insights such practices may develop with regard to the library’s role in improving learning outcomes or increasing graduation rates. On the face of it, information systems librarians maintain and the informational products (e.g., databases and digital libraries) to which they provide students access hold data that could reveal a unique view into students’ intellectual behaviors. Such data could provide value to LA algorithms and enhance related outcomes, and institutions may seek more involvement from their librarians in aggregating and mining student data. However, these initiatives are not without moral questions and challenges to professional ethics. In this chapter, we argue that compelling libraries to cooperate with LA is an affront to their ethical commitments. While LA may create some justifiable benefits from library data, the distribution of benefits will be unequal and risk causing students informational harms and injustices.
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