Learning analytics defines as the measurement, collection, analysis and reporting of data about learners. This is an emerging field of research in educational technology. By effectively using learning analytics, educators can better understand the learning level and ability of each student and then tailor the learning experience.
It can also answer when is a student at risk of failing or dropping out? and does a student need extra support in a specific area? Learning analytics helps identify gaps in courseware and determine intervention strategies based on analytical data. It typically provides an objective and valuable source of data that helps advance the field of educational technology research. Learning analytics research leverages large, anonymous sets of collected data as a source of feedback on student interactions with online learning activities.
Why Learning Analytics Matters?
Learning analytics has the ability to help foresee learners’ performance during the eLearning course. For instance, an online facilitator may predict if a particular learner is likely not to pass the eLearning course, they provide additional support such as further readings or tutoring sessions. Using learning analytics, online instructors can also gain the ability to custom tailor eLearning experiences for each individual learner.
Used effectively, it can help an educator to accomplish better student experience with students benefitting from a more personalized learning experience. Learning analytics can also help improve student retention by emphasizing where extra support is needed at an early stage.
Since governments, universities, testing centers, and a large number of open online course providers are gleaning data about learners and provide insights into how they learn, all that data has been mostly unexploited until the fairly recent development of the methods and tools to do so.
In its Horizon Report: 2019 Higher Education Edition, EDUCAUSE Learning Initiative identifies learning analytics as one of the digital strategies expected to enter mainstream use in years to come. The report serves as a reference and technology planning guide for educators, higher education leaders, administrators, policymakers, and technologists.
Enabling More Meaningful Learning
Over the years, the teaching and learning process has been experiencing major revolutionary changes thanks to technological innovations. It is not only transforming instructional design and education delivery but also student advising and assessment. In the recent development of massive open online courses are also redefining the education sector that inevitably generates a massive amount of learning-related data. This is where learning analytics comes into the rescue making use of big data to analyze that voluminous data to turn into meaningful lessons.
Learning analytics consists of the use of a wide range of data and techniques for analysis. They include the development of metrics such as predicators and indicators for various factors to understand the current situation and scale teaching and learning effectiveness; the use of different educational technologies to envisage and interpret data and prompt remedial actions; and the refinement of the metrics and derivation of interventions to shape the learning environment.