What is Learning Analytics?
The definition and aims of Learning Analytics are contested. One earlier definition discussed by the community suggested that “Learning analytics is the use of intelligent data, learner-produced data, and analysis models to discover information and social connections for predicting and advising people’s learning.”
But this definition has been criticised:
1. “I somewhat disagree with this definition – it serves well as an introductory concept if we use analytics as a support structure for existing education models. I think learning analytics – at an advanced and integrated implementation – can do away with pre-fab curriculum models”. George Siemens, 2010.
2. “In the descriptions of learning analytics we talk about using data to “predict success”. I’ve struggled with that as I pore over our databases. I’ve come to realize there are different views/levels of success.” Mike Sharkey 2010.
A more holistic view than a mere definition is provided by the framework of learning analytics by Greller and Drachsler (2012). It uses a general morphological analysis (GMA) to divide the domain into six “critical dimensions”.
A systematic overview on learning analytics and its key concepts is provided by Chatti et al. (2012) and Chatti et al. (2014) through a reference model for learning analytics based on four dimensions, namely data, environments, context (what?), stakeholders (who?), objectives (why?), and methods (how?).
The Context of Learning Analytics
In “The State of Learning Analytics in 2012: A Review and Future Challenges” Rebecca Ferguson tracks the progress of analytics for learning as a development through:
1. The increasing interest in ‘big data’ for business intelligence
2. The rise of online education focussed around Virtual Learning Environments (VLEs), Content Management Systems (CMSs), and Management Information Systems (MIS) for education, which saw an increase in digital data regarding student background (often held in the MIS) and learning log data (from VLEs). This development afforded the opportunity to apply ‘business intelligence’ techniques to educational data
3. Questions regarding the optimisation of systems to support learning particularly given the question regarding how we can know whether a student is engaged / understanding if we can’t see them?
4. Increasing focus on evidencing progress and professional standards for accountability systems
5. This focus led to a teacher stake hold in the analytics – given that they are associated with accountability systems
6. Thus an increasing emphasis was placed on the pedagogic affordances of learning analytics
7. This pressure is increased by the economic desire to improve engagement in online education
for the deliverance of high quality – affordable – education
History of Learning Analytics in Higher Education
The first graduate program focused specifically on learning analytics was created by Dr. Ryan Baker and launched in the Fall 2015 semester at Teachers College – Columbia University. The program description states that “data about learning and learners are being generated today on an unprecedented scale. The fields of learning analytics (LA) and educational data mining (EDM) have emerged with the aim of transforming this data into new insights that can benefit students, teachers, and administrators. As one of world’s leading teaching and research institutions in education, psychology, and health, we are proud to offer an innovative graduate curriculum dedicated to improving education through technology and data analysis.”
Methods for learning analytics include:
• Content analysis – particularly of resources which students create (such as essays)
• Discourse Analytics Discourse analytics aims to capture meaningful data on student interactions which (unlike ‘social network analytics’) aims to explore the properties of the language used, as opposed to just the network of interactions, or forum-post counts, etc.
• Social Learning Analytics which is aimed at exploring the role of social interaction in learning, the importance of learning networks, discourse used to sensemake, etc.
• Disposition Analytics which seeks to capture data regarding student’s dispositions to their own learning, and the relationship of these to their learning. For example, “curious” learners may be more inclined to ask questions – and this data can be captured and analysed for learning analytics.
Analytics have been used for:
• Prediction purposes, for example to identify ‘at risk’ students in terms of drop out or course failure
• Personalization & Adaptation, to provide students with tailored learning pathways, or assessment materials
• Intervention purposes, providing educators with information to intervene to support students
• Information visualization, typically in the form of so-called learning dashboards which provide overview learning data through data visualisation tools