Discovery and Temporal Analysis of MOOC Study Patterns

The large-scale and granular interaction data collected in online learning platforms such as massive open online courses (MOOCs) provide unique opportunities to better understand individuals' learning processes and could facilitate the design of personalized and more effective support mechanisms for learners. In this paper, we present two different methods of extracting study patterns from activity sequences. Unlike most of the previous works, with post hoc analysis of activity patterns, our proposed methods could be deployed during the course and enable the learners to receive real-time support and feedback. In the first method, following a hypothesis-driven approach, we extract predefined patterns from learners' interactions with the course materials. We then identify and analyze different longitudinal profiles among learners by clustering their study pattern sequences during the course. Our second method is a data-driven approach to discover latent study patterns and track them over time in a completely unsupervised manner. We propose a clustering pipeline to model and cluster activity sequences at each time step and then search for matching clusters in previous steps to enable tracking over time. The proposed pipeline is general and allows for analysis at different levels of action granularity and time resolution in various online learning environments. Experiments with synthetic data show that our proposed method can accurately detect latent study patterns and track changes in learning behaviours. We demonstrate the application of both methods on a MOOC dataset and study the temporal dynamics of learners' behaviour in this context.

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

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

Learning How to Write with a Social Robot

Shruti Chandra

Learning handwriting is one of the main occupations for children in primary education. It is a complex skill that has been associated with children’s learning and development. Poor handwriting skills affect children negatively concerning their academic performance and motivation. In schools, children with handwriting problems need extra support and one-to-one interventions from teachers. Personalised one-to-one support requires additional resources that most schools generally cannot afford. Therefore, there is a need to explore different methods of learning and contemporary technologies to develop new educational tools to foster children’s learning process of handwriting. The research presented in this thesis addresses the question of - “How to create a social robot that can help children to acquire handwriting skills?". Thus, we looked into three different research domains: education; pedagogic sciences; and social human-robot interaction (HRI). On the one hand, we investigated the types of errors commonly present in children’s handwriting. On the other hand, we needed to explore different educational theories to find alternate learning methods that would maximise children’s learning gains. In addition, we investigated various possibilities for integrating a social robot into educational scenarios and measured its impact on the children’s learning process. This thesis presents five studies that follow an incremental evolution of the research. The first three studies were based on the interaction between two children, in the presence of either a human or a robotic facilitator, with the children performing a collaborative writing activity. We explored the ‘peer-learning’ and ‘peer-tutoring’ methods and compared the differences in terms of interaction behaviour and learning gains. Further, we analysed children’s interpersonal distance and feelings of responsibility towards their peers, both in the presence of a robot and human facilitator. Finally, we examined children’s assessment behaviour towards their peers and self-disclosure towards the robotic facilitator. The outcomes of these first studies provided enough data to generate a taxonomy of children handwriting errors to be used in the subsequent studies. They hinted that the ‘peer-tutoring’ method had a positive influence on children’s learning and corrective feedback to their peers in the presence of the social robot, providing positive support to the applicability of social robots as ‘facilitators’ in handwriting learning. The next step was to study the effects of the robot in the peer-tutoring scenario when assuming the role of a peer, directly interacting and performing a collaborative writing activity with the children. Thus, we developed an educational system with an autonomous social behaviour of a robot exhibiting various levels of learning competencies. The tutor-child provides corrective feedback on the writing of the learner-robot and in turn, enhances his writing skills. The robot’s writing was generated by an algorithm incorporating human-inspired movements and could reproduce a set of writing errors from the error taxonomy. We tested the system by conducting two longitudinal studies, each of them composed of four sessions. In the fourth study, we assigned the robot two contrasting competencies: ‘learning’ and ‘non-learning’. We measured the differences in children’s learning gains and changes in their perceptions towards the learner-robot. The fifth study followed a similar interaction scenario and research questions, but this time the robot performed three learning competencies: ‘continuous-learning’;’non-learning’ and ‘personalised-learning’. The findings of these studies show that the children improved more with the robot exhibiting learning, continuous-learning and personalised-learning competencies compared to the robot showing non-learning competency. The way children’s learning and perception of the robot evolved over time confirmed the need for multiple sessions for the last two studies. This thesis presents some evidence that social robots can be successfully used as tools for enhancing children’s handwriting skills in peer-learning scenarios.