A further rapid growth area in the use of data in education is Learning Analytics (LA). LA has been defined as “the measurement, collection, analysis and reporting of data about learners and their contexts, for purposes of understanding and optimizing learning and the environments in which it occurs” (SoLAR, 2011). It is seen as assisting in informing decisions in education systems, promoting personalized learning and enabling adaptive pedagogies and practices. At least in the initial stages of development and use, universities and schools have tended to harvest existing data drawn from Virtual Learning Environments (VLEs) and to analyse that data to predict individual performance and undertake interventions which can, for instance, reduce drop-out rates. Other potential benefits include that LA can allow teachers and trainers to assess the usefulness of learning materials, to increase their understanding of the learning environment in order to improve it, and to intervene to advise and assist learners. Perhaps more importantly, it can assist learners in monitoring and understanding their own activities and interactions and participation in individual and collaborative learning processes and help them to reflect on their learning.
Pardo and Siemens (2014) point out that “LA is a moral practice and needs to focus on understanding instead of measuring.” this is an important distinction … else the popular use of LA based on AI will be on displacing teachers (schools without teachers) which will impoverish learning, and these experiments will occur in poorly resourced schools, as teacher displacement tools .. and certainly not in elite, well resourced schools. In this understanding: “learners are central agents and collaborators, learner identity and performance are dynamic variables, learning success and performance is complex and multidimensional, data collection and processing needs to be done with total transparency.”
Although initially LA has tended to be based on large data sets already available in universities, school based LA applications are being developed using teacher inputted data. This can allow teachers and understanding of the progress of individual pupils and possible reasons for barriers to learning.
There has been only very limited use of Learning Analytics in developing countries. However, in 2018 Global Learning for Development published ‘Learning Analytics for the Global South; (Lim, C. P., & Tinio, V. L. (Eds.), 2018). The publication considered how the collection, analysis, and use of data about learners and their contexts have the potential to broaden access to quality education and improve the efficiency of educational processes and systems in developing countries around the world. An opening discussion paper by Gašević examined how the potential of learning analytics could support critical digital learning and education through quality learning at scale and the acquisition of 21st century skills.
This was followed by four responses from experts in Africa, mainland China, Latin America and South East Asia.
In viewing Learning Analytics through the lens of three key challenges facing education systems in the Global South: quality, equity, and efficiency, Gašević suggested “that the implementation of learning analytics in developing countries has significant potential to support learning at scale, to provide personalized feedback and learning experience, to increase the number of graduates, to identify biases affecting student success, to promote the development of 21st century skills, and to optimize the use of resources.”
Gašević acknowledged that while there is an increasing number of guidelines for addressing issues of privacy and ethics in learning analytics, citing Ferguson et al (2016) and Sclater, (2016), guidelines specific to different regions of the Global South, consistent with local cultures, legislation, and practices, need to be developed. Moreover, he said that in order to promote equity in the Global South, specific guidelines for the use of learning analytics need to be designed.
Paul Prinsloo from the University of South Africa, citing Selwyn (2014), said Learning Analytics, like all (educational) technology, must be “understood as a knot of social, political, economic and cultural agendas that is riddled with complications, contradictions and conflicts.”
Learning analytics can provide relevant and actionable information by analyzing the impact of learner’s socio-economic context, the school or college’s quality, the learner’ engagement, and the effectiveness of the educational systems.