Learning Analytics (LA): In a Nutshell<\/b><\/p>\n
Learning analytics<\/a> evaluates data so that stakeholders can determine how to improve learning outcomes measured by grades, retention, or completion. LA collects and analyzes student data in order to look for correlations between student activities and learning outcomes.\u00a0However, this can be a challenging process because of the various expert perspectives regarding\u00a0 defining learning analytics.<\/p>\n A Few Definitions of Learning Analytics<\/b><\/p>\n Currently, there are many definitions of learning analytics<\/a>\u00a0because there is no expert agreement due to differences in perspectives regarding processes of implementation and levels of success. Below are a few definitions<\/a> to consider:<\/p>\n – LAK \u201811 states that LA is \u201cthe 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.”<\/p>\n – EDUCAUSE\u2019s Next Generation learning initiative states that LA is \u201cthe use of data and models to predict student progress and performance, and the ability to act on that information.\u201d<\/p>\n – Elias (2011) describes LA as \u201can emerging field in which sophisticated analytic tools are used to improve learning and education.\u201d<\/p>\n – Siemens (2010) views LA as “the use of intelligent data, learner-produced data, and analysis models to discover information and social connections, and to predict and advise on learning.”<\/p>\n – Johnson et al. (2011) defines LA as “the interpretation of a wide range of data produced by and gathered on behalf of students in order to assess academic progress, predict future performance, and spot potential issues.”<\/p>\n Learning Analytics: In Conversation<\/strong><\/p>\n This video is an introduction to learning analytics pertaining to educational settings. The conversation takes the spirit of the definitions into consideration.<\/p>\n