Developing Learning Analytics Design Knowledge in the “Middle Space”: The Student Tuning Model and Align Design Framework for Learning Analytics Use

Alyssa Friend Wise,Yuting Zhao,Simone Hausknecht,Jovita Maria Vytasek

doi:10.24059/olj.v20i2.783

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.

Highlights

Information derives its importance from the possibilities of action (Postman, 1985, p68)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
The Student Tuning Model Drawing on the self-regulated learning literature discussed above, we describe a specific model of learning-analytics-informed reflective practice
This paper has reviewed the challenges for students’ learning analytics use identified in the literature, presented the Student Tuning Model as a conceptualization of the process by which students use learning analytics as part of a self-regulatory cycle, and proposed the Align Design Framework as a set of four interconnected principles to support such use

Summary

Introduction

Information derives its importance from the possibilities of action (Postman, 1985, p68). The creation of these analytics is only half of the endeavor; in order for the data to influence learning process, these analytic outputs need to become inputs into subsequent decision-making (Clow, 2013) This latter activity of using learning analytics to inform choices and subsequent action in-situ is the focus of this article. History shows that the use of educational innovations (and designed objects more generally) is never fully determined by the form of the technologies themselves Rather it is dynamically shaped by the affordances of the new tools in combination with the needs and abilities of the users and the constraints of the situations into which they are placed (Cuban, 1986, 2001; Gibson, 1977; Norman, 2013). We propose and conduct initial validation of a set of principles for pedagogical design that addresses the identified challenges and supports students’ use of analytics as part of the tuning process: The Align Design Framework

Literature Review

Conclusions