Learning Analytics: The next frontier for computer assisted language learning in big data age

Abstract

Abstract. Learning analytics (LA) has been applied to various learning environments, though it is quite new in the field of computer assisted language learning (CALL). This article attempts to examine the application of learning analytics in the upcoming big data age. It starts with an introduction and application of learning analytics in other fields, followed by a retrospective review of historical interaction between learning and media in CALL, and a penetrating analysis on why people would go to learning analytics to increase the efficiency of foreign language education. As approved in previous research, new technology, including big data mining and analysis, would inevitably enhance the learning of foreign languages. Potential changes that learning analytics would bring to Chinese foreign language education and researches are also presented in the article.Keywords. learning analytics; big data; CALL; media The defining of learning analytics and how does it workIn higher education, businesses and governments, the data focus is increasingly expressed in the term learning analytics. Though still a young concept in education, learning analytics (LA) is frequently used in relation to data clusters. The ubiquity of the term analytics partly contributes to the breadth of meanings attached to it. A reasonable definition of learning analytics comes from the 1 st International Conference on Learning Analytics and Knowledge. It says, "Learning analytics is the measurement, collection, analysis and reporting of data about learners and their contexts, for purpose of understanding and optimizing learning and the environments in which it occurs." ( Banff and Alberta, 2011) Learning analytics, since then, has always been shaping the scope and range of activities in higher education, affecting administration, research, teaching and learning, and support resources. (Siemens and Long, 2011) Broadly speaking, a learning analytics program evaluate large data sets to provide decision makers or teachers with information that can help to improve learning outcomes, such as, grades, retention, or completion. LA collects and analyzes the "digital breadcrumbs" that students leave as they interact with various computer systems to look for correlations between those activities and learning outcomes. A professional LA software could do what the conventional data analysis software could not: it compares a students' activity with others in the class, with students who previously took the course, even with students who are in other countries but have this class online, to create a model for how each students is likely to fare. In this way, it could capitalize on the vast quantities of data to improve the efficiency of learning.