Event details

Critical Learning Analytics

Friday, 20 October 2017

Critical Learning Analytics

With the increasing use of digital technologies by higher education institutions, teachers, and students, the resulting traces of data are being increasing seen as valuable sources of insight. The computational analysis of this kind of data is being undertaken in the burgeoning field of Learning Analytics; a broad set of approaches that draw from research techniques in machine learning, social network analysis and statistics, and theory from psychology and the learning sciences. While learning analytics are attracting the interest of educational institutions, researchers, and funders, more work is need to understand the powerful ways these approaches can shape the educational landscape, from policy initiatives, to teacher professionalism, to student experience. The three talks in this SRHE Digital University network event offer ways to perceive learning analytics in ways that move beyond the hyperbole of technological disruption, and engage critically with key issues in higher education, teaching, and learning.

For now we see through a glass, darkly – critical perspectives on the rise of learning analytics in the age of teaching ‘excellence’
Dr Sue Timmis, Reader in Education, School of Education, University of Bristol

This paper offers a critical review of the rise of learning analytics and its growing influence on learning and teaching in higher education. The term learning analytics is not always well understood but has been referred to as the collection, measurement and monitoring of learner generated digital data (Ferguson, 2012).  Whilst there has been increasing use of surveillance technology and constantly expanding ‘digital footprints’ for auditing and management of educational performance, there has been a much slower pace of technologies designed to share control with students (Facer, 2011). Furthermore, claims that learning analytics can lead to improvements in retention and academic performance has led to institutional adoption of early warning or predictive systems, rather than student-led or collaborative approaches. This aligns with the trend towards a more individualistic and performative view of learning in higher education where the onus is firmly placed on the learner to address ‘deficiencies’ but with little control over their data. Drawing on a recent literature review, the paper gives an historical overview of the rise of learning analytics and interrogates claims being made by some of its chief proponents. It explores ethical implications, methods, value and validity in assessment and student engagement, and implications for national systems, including the UK Teaching Excellence Framework.  In conclusion, just as the enduring myth of the ‘digital native’ maintains its grip on policymakers, learning analytics is gaining credibility in policy circles in similar deterministic ways, without sufficient scrutiny of its claims to address key pedagogical ‘excellence’ challenges, what measures are being proposed or practiced, the ethical implications and the validity and value of such measures, in particular for students themselves. This is of growing concern in the UK, where the metrics-based Teaching Excellence Framework is now established, but equally elsewhere where similar regimes are being implemented or considered.


From prediction to participation: a vision of critical awareness with the Learning Analytics Report Card (LARC)
Dr Jeremy Knox, Lecturer in Digital Education, Centre for Research in Digital Education, The University of Edinburgh

This presentation will describe the design and implementation of the ‘Learning Analytics Report Card’ (or LARC); a small-scale experimental project being undertaken at The University of Edinburgh seeking to explore student participation in Learning Analytics and methods for developing critical awareness around the increasing use of data collection and analysis in education. The talk will begin with critical perspectives on Learning Analytics that provide context for the design of the project, focusing specifically on the concern for future prediction, drawing on work in critical algorithm studies, as well as emerging perspectives on the ethics of forecasting educational attainment. The second section of the talk will outline the main functions of the LARC: capturing data from a Moodle LMS course; allowing students to select from a number of report themes; and presenting the student with a textual report that summarises their progress on the course in line with the chosen themes. Finally, the talk will discuss the implementation of the LARC over three semesters with groups of postgraduate distance learners, offering an analysis of usage and a discussion of student responses. This will focus on the potential value, challenges, and dilemmas, of offering opportunities for students to participate in learning analytics, and how a shift from privileging prediction to encouraging participation might foster critical awareness of the increasing datafiction of educational activity.

Ways of Seeing Learning Through Data
Mary Loftus, PhD candidate, NUI Galway

Learning Analytics are about learning (GaÅ¡ević 2015). However, too little attention has been paid to the student’s role in data-rich learning environments (Kitto 2016). Where the student role has been foregrounded, there is still a tendency to see learning analytics from a deficit standpoint and as an early-warning system for engagement and retention issues. The potential for new learning modalities and affordances is still emerging. Biesta (2015) describes education as ‘the coming into presence of unique individual beings’ and to facilitate this, education spaces must ‘open up for uniqueness to come into the world’. This is the ontological starting point of this research along with Paulo Freire’s (1968) emphasis on the student as an agent of praxis in their learning environment. 

Data and models can provide a mirror for self-reflection and metacognition (Koedinger 2009). By making the richness of the learning process more visible, learners and teachers can access deeper and more critical insights into their shared experience. We can create the conditions for classrooms to be data informed - rather than data driven. However, this potential will require more in the way of data literacy of learners and teachers of the future. Indeed living in our more connected societies requires all of us to be more data literate in order to thrive.

This paper will consider the potential of probabilistic machine learning techniques in conjunction with other learning model approaches to produce interactive learning models (Millán 2015) that can be integrated in existing learning analytics systems. It will also consider the issues involved in facilitating their use in classrooms: exploring the needs of students and teachers around data literacy and the potential to use these skills to critically exploit the learning possibilities offered by student data and student insight on that data. 


Network: Digital University
Date(s): Friday, 20 October 2017
Times: 11:30 - 16:00
Signup Deadline: Monday, 16 October 2017
Location: SRHE, 73 Collier St, London N1 9BE
Lunch Provided: Yes
Spaces Left: No Spaces Left
Prices: Members: Free, Guests: £60.00
There are no spaces left for this event
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