Developing learning analytics design knowledge in the “middle space”: The student tuning model and align design framework for learning analytics use

Alyssa Friend Wise, Jovitamaria Vytasek, Simone Hausknecht, Yuting Zhao

Research output: Contribution to journalArticle

Abstract

This paper addresses a relatively unexplored area in the field of learning analytics: how analytics are taken up and used as part of teaching and learning processes. Initial steps are taken towards developing design knowledge for this “middle space,” with a focus on students as analytics users. First, a core set of challenges for analytics use identified in the literature are compiled. Then, a process model is presented for conceptualizing students’ learning analytics use as part of a self-regulatory cycle of grounding, goal-setting, action and reflection—the Student Tuning Model. Finally, the Align Design Framework is presented with initial validation as a tool for pedagogical design that addresses the identified challenges and supports students’ use of analytics as part of the tuning process. Together, the framework’s four interconnected principles of Integration, Agency, Reference Frame and Dialogue / Audience provide a useful starting point for further inquiry into well-designed learning analytics implementations.

Original languageEnglish (US)
JournalJournal of Asynchronous Learning Network
Volume20
Issue number2
StatePublished - Jun 2016

ASJC Scopus subject areas

  • Education
  • Computer Networks and Communications

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