Abstract:

In the latest years learning analytics (LA) have been introduced as a field that seeks to provide answers to questions that are related to the education and the data which are gathered when the students are engaging in the Learning experience (Larusson & White, 2014). The reasons why using analytics in education has shown growth during the recent years is basically because of the increase in the data quantity, the improved data formats, the advances in computing, and the availability of more sophisticated tools for analytics (Baker, & Inventado, 2014). Undoubtedly, learning analytics can be a useful tool for learning and eLearning experiences. But each student has different needs. For this reason, we are suggesting of creating a tool which will be tailored to every student’s needs. This will happen by using simulated data in an elearning environment. A student profile is a set of attributes and their values which can describe the student. In order for this to happen, we will use two kinds of dataset. The static student dataset which will provide basic information about the student such as his age or his past academic performance. The second data set will be the dynamic one which will refer to the student’s performance, engagement, absences etc. By performing simulations, a lot of useful conclusions can be derived and by using these data we will come up with useful suggestions based on the personalized learning techniques.

Keywords:

Learning Analytics, e-learning, Data Mining, Clustering Algorithms.