Discovering Interaction Patterns in Online Learning Environments:A Learning Analytics Research

The increasing amount of data collected in online learning environments provides unique opportunities to better understand the learning processes in different educational settings. Learning analytics research aims at understanding and optimizing learning and the environments in which it occurs. A crucial step towards this goal is to adapt and develop adequate computational methods to process and analyze the learning-related data and to present information in intelligible ways to educational stakeholders. In this thesis we investigate interaction patterns of learners in two different online learning environments: Massive Open Online Courses (MOOCs) and Realto, an online platform for integrated Vocational Education and Training (VET). We analyze interaction patterns across three principal dimensions: time, activity, and social. To obtain a better understanding of the complex learning behaviours, it is essential to consider these different aspects of the educational data. We develop novel methods and use existing techniques from sequential pattern mining, content analysis, and social network analysis to model and track interaction patterns of learners across the three mentioned dimensions. As regards the time dimension, we present methods to model temporal patterns of learners' participation. We introduce novel techniques to discover and quantify online regularity in terms of following a certain daily or weekly time schedule. We investigate the relation between students' regularity level and their performance in a MOOC course. Concerning the activity dimension, we analyze learners' activity sequences in order to identify and track the evolution of their study approaches over time. By clustering study pattern sequences in a MOOC course, we extract different engagement profiles among learners and describe their properties. Furthermore, we propose a complete processing pipeline for the unsupervised discovery of study patterns from sequential interaction logs. This pipeline is applicable at different levels of actions granularity and time resolution and enables to perform temporal analysis of learners' interaction patterns throughout the course duration. For the social dimension, we explore the attributes of social interactions among learners. In the MOOC context, we combine content and social network analyses to study dynamics of forum discussions and the evolution of students' roles over time. In the context of Realto, we employ social network analysis to model