Learning Analytics and Learner Activity

While data from a Learning Management System (LMS) can tell you about assessment scores and percentage completions, it can also tell you a lot about your learners, the content and their interplay. To get performance insights of your platform and the learning content, you must identify different data points, fetch the data, analyze and interpret it. Learner proficiency, Learner engagement, Learner preferences and Learner progress are some metrics that are computed by combining several data. Some examples of data that you collect from a learning platform are Clicks, Pages visited, Time spent, Search keywords, etc. There are some metrics that are directly connected to learner activity and we will look at those in this post.

Machine learning in e-learning - k-means clustering

Is it possible to use machine learning in e-learning? There are several aspects of e-learning where machine learning (ML) can be applied. Machine learning can be used directly in the learning content or on analytics. Since an LMS captures a large amount of data every day related to the content (courses or modules), learners and trainers, machine learning can immediately find application in analytics. Before we get to that, let’s understand what Machine Learning is in simple terms. Machine learning (ML) is a subset of Artificial Intelligence (AI). In Machine Learning, you write algorithms that allow the system to learn from the data provided. The system discovers data features and then uses that to conclude a fact, perform an action or improve itself.