Educational data mining | EPFL Graph Search

Educational data mining (EDM) is a research field concerned with the application of data mining, machine learning and statistics to information generated from educational settings (e.g., universities and intelligent tutoring systems). At a high level, the field seeks to develop and improve methods for exploring this data, which often has multiple levels of meaningful hierarchy, in order to discover new insights about how people learn in the context of such settings. In doing so, EDM has contributed to theories of learning investigated by researchers in educational psychology and the learning sciences. The field is closely tied to that of learning analytics, and the two have been compared and contrasted. Definition Educational data mining refers to techniques, tools, and research designed for automatically extracting meaning from large repositories of data generated by or related to people's learning activities in educational settings. Quite often, this data is extensive, fine

Discovering Interaction Patterns in Online Learning Environments

Mina Shirvani Boroujeni

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 the social interactions among learners and to study the structure of Realto-mediated communication among different stakeholders in the VET system. Using the presented analytic methods, we provide novel insights into the interaction patterns of learners in MOOCs and Realto. Moreover, we present an implementation of these methods into an analytics dashboard for Realto researchers.

EPFL2018

Discovery and Temporal Analysis of Latent Study Patterns from MOOC Interaction Sequences

Pierre Dillenbourg, Mina Shirvani Boroujeni

Capturing students' behavioral patterns through analysis of sequential interaction logs, is an important task in educational data mining to enable more effective personalized support during the learning process. This study aims at discovery and temporal analysis of learners' study patterns in MOOC assessment periods. We address this problem using two different methods. First, following a pattern-driven approach, we identify learners' study patterns based on their interaction with video lectures and assignments. Through unsupervised clustering of study pattern sequences, we capture different longitudinal engagement profiles among learners in a MOOC course. Second, we propose temporal clustering framework for unsupervised discovery of latent patterns in learners' interaction data. We model and cluster activity sequences at each time step, and perform cluster matching to enable tracking learning behaviours over time. Our proposed pipeline is general and can be adopted for modeling and temporal analysis of interaction data at different levels of granularity, in various learning environments including MOOCs and Intelligent Tutoring Systems (ITS). We demonstrate the application of our proposed pipeline for detecting latents study patterns in a MOOC course.

2018

Special issue on: Learning Analytics (Editorial)

Maria Jesus Rodriguez Triana

With the general technological advances of the recent years, current learning environments amass an abundance of data. Albeit such data offer the chance of better understand the learning process, stakeholders – learners, teachers and institutions – often need additional support to make sense of it (Dyckhoff et al., 2013; Macfadyen and Dawson, 2012). The acknowledgement of these needs is at the heart of the recent emergence of Learning Analytics (LA), a research area that draws from multiple disciplines such as educational science, information and computer science, sociology, psychology, statistics and educational data mining (Buckingham Shum and Ferguson, 2012). This multidisciplinarity in LA has motivated the work done by Ferguson (2012), which provides a first review of the drivers, development and challenges of this novel and young research area. Our understanding of learning analytics is based on the definition from the Society for Learning Analytics (SoLAR – Society for Learning Analytics1) which specifies that “Learning analytics is the measurement, collection, analysis and reporting of data about learners and their contexts, for purposes of understanding and optimizing learning and the environments in which it occurs”. Since 2011, the Horizon reports list Learning Analytics as a hot topic in higher education and indicate the importance of data for this field (Johnson et al., 2011). Learning analytics are able to provide a fresh view on understanding of teaching and learning by observing patterns of complex data (Johnson et al., 2012). Furthermore, it will influence the evolution of higher education in a great measure. Nowadays, learners have access to a huge amount of online information having themselves the possibility of being content creators and information sharers. Therefore the quantity of available information grows in an exponential way, once that each and every citizen can access and produce information. For these purposes, learners have at their disposal many online resources, including LMSs, VLEs, MOOCs and many other online tools that facilitate the learning process and the development of competences. Taking into account these online learning facilities and therefore the learners’ acquisition of knowledge, it is also easier to measure and analyse their experiences by using learning analytics tools. Different online courses and institutions provide dashboards with information about student experiences, flaws and successes. Although the investigation of behavioural specific data makes learning analytics complex, the time comes to utilise personalised learning environments adapted to students learning paths, skills, previous knowledge, competences and motivation.

2013