Using Learning Analytics Technologies to Find Learning Structures from Online Examination System

The field of learning analytics has the potential to enable education institutions to increase their understanding of their students´ learning needs and to use that understanding to positively influence student learning and progression. Analysis of data relating to students and their engagement with their learning is the foundation of this process. In this work we investigate the database of learners´ answers to the asked questions by applying the Markov chains. We want to understand whether the learners´ answers to the already asked questions can affect the way they will answer the subsequent asked questions and if so, to what extent. Results showed that influential structures were identified in the history of learners´ answers considering the Markov chain of different orders. The results could be used to identify undergraduates who have difficulties after couple of steps and to optimize the way questions are asked for each undergraduate individually.