Together we stand, Together we fall, Together we win: Dynamic Team Formation in Massive Open Online Courses

Massive Open Online Courses (MOOCs) offer a new scalable paradigm for e-learning by providing students with global exposure and opportunities for connecting and interacting with millions of people all around the world. Very often, students work as teams to effectively accomplish course related tasks. However, due to lack of face to face interaction, it becomes difficult for MOOC students to collaborate. Additionally, the instructor also faces challenges in manually organizing students into teams because students flock to these MOOCs in huge numbers. Thus, the proposed research is aimed at developing a robust methodology for dynamic team formation in MOOCs, the theoretical framework for which is grounded at the confluence of organizational team theory, social network analysis and machine learning. A prerequisite for such an undertaking is that we understand the fact that, each and every informal tie established among students offers the opportunities to influence and be influenced. Therefore, we aim to extract value from the inherent connectedness of students in the MOOC. These connections carry with them radical implications for the way students understand each other in the networked learning community. Our approach will enable course instructors to automatically group students in teams that have fairly balanced social connections with their peers, well defined in terms of appropriately selected qualitative and quantitative network metrics.Comment: In Proceedings of 5th IEEE International Conference on Application of Digital Information & Web Technologies (ICADIWT), India, February 2014 (6 pages, 3 figures

Characterising Learners in Online Communities Based on Actor-Artefact Relations

Online communities are of huge interest in terms of learning and knowledge creation because of the potential to distribute knowledge among possibly large audience independently from time and place. In this context, various forms of online learning have developed over time ranging from small learning groups to massive open online courses (MOOCs) with thousands of participants. In order to support learning in those settings an increased understanding of specific characteristics of learners in online communities is necessary. Thus, dedicated means to gather valuable information from data produced in online learning environments have to be developed. This cumulative dissertation includes five publications aiming to make progress in this direction with a particular focus on the advancement of methods to analyse activity and interaction data of learners. The methodological foundation of the work is (social) network analysis, which provides a well-grounded set of methods for structural analysis of relational data. Network analysis is especially suited since the collected data about actors (in this thesis mostly learners) who create and consume digital content (artefacts) can be modelled as actor-artefact networks. Those actor-artefact networks denote the starting point of all analyses presented in this dissertation, which target different aspects of learning in online communities, in particular the usage of learning resources, emergence of interest profiles, and information exchange between learners. In the course of this work, stable artefacts that are not assumed to have changing content over time are distinguished from time-evolving dynamic artefacts (typically user generated content). In the case of stable artefacts, affiliations of learners to learning resources in online courses are analysed by identifying mixed clusters of learners and resources using network clustering algorithms. The evolution of these learner-resource clusters over time is investigated in detail leading to discoveries of typical resource access patterns that characterise learners regarding their interests in provided learning materials. The approach is further extended and combined with content analysis techniques to analyse thematic development in discussion forums. Discussion forums are also the subject of two other studies investigating information exchange between learners in MOOCs. The evolving discussion threads are considered as dynamically evolving artefacts that are used to extract social networks reflecting information exchange between forum users. These networks are analysed to uncover different roles of forum users with respect to their positions in the network. For this task different approaches are described that are capable of modelling structural characteristics of the information exchange network over time and further take discussion topics as additional information into account.Online-Gemeinschaften sind aufgrund der Möglichkeiten Wissen zeit- und ortsunabhängig unter einer großen Menge von Adressaten zu verbreiten von großem Interesse bezüglich Lernens und Wissenskonstruktion. In diesem Kontext haben sich über die Zeit verschiedene Formen des Online-Lernens entwickelt, von kleinen Lerngruppen bis zu “Massive Open Online Courses” (MOOCs) mit tausenden von Teilnehmern. Um das Lernen in diesen Bereichen zu unterstützen, ist ein besseres Verständnis spezifischer Charakteristiken von Lernenden in Online-Gemeinschaften notwendig. Um nützliche Informationen aus Daten zu gewinnen, die in Online-Umgebungen anfallen, sind dezidierte Methoden wichtig. Diese kumulative Dissertation beinhaltet Publikationen die auf Fortschritte in diesem Bereich abzielen. Ein besonderer Fokus liegt dabei auf der Weiterentwicklung von Methoden zur Analyse von Aktivitäts- und Interaktionsdaten von Lernern in Online-Gemeinschaften. Das methodische Fundament ist dabei die (Soziale) Netzwerkanalyse, welche fundierte Methoden zur strukturellen Analyse von relationalen Daten bereitstellt. Netzwerkanalyse ist besonders geeignet, da Daten über Akteure (hier meistens Lernende), die digitale Inhalte (Artefakte) erstellen und konsumieren, als Akteur-Artefakt-Netzwerk modelliert werden können. Solche Akteur-Artefakt-Netzwerke sind der Ausgangspunkt aller in dieser Dissertation vorgestellten Analysen, die auf verschiedene Aspekte des Lernens in Online-Gemeinschaften abzielen, insbesondere die Nutzung von Lernressourcen, die Entwicklung von Interessensprofilen und Wissensaustausch zwischen Lernenden. Im Verlauf dieser Arbeit wird zwischen stabilen Artefakten, die sich über die Zeit nicht verändern und sich über die Zeit entwickelnden dynamischen Artefakten (typischerweise Nutzergenerierte Inhalte) unterschieden. Im Fall von statischen Artefakten werden Affiliationen von Lernenden zu Lernressourcen in Online-Kursen untersucht, indem gemischte Cluster aus Lernenden und Lernressourcen mittels Netzwerk-Clusteringalgorithmen identifiziert werden. Die Evolution dieser Lerner-Ressourcen-Cluster wird eingehend untersucht, woraus Erkenntnisse über typische Ressourcennutzungsmuster gewonnen werden, die die Lernenden in Online-Gemeinschaften bezüglich ihrer Präferenzen zu Lernmaterialien charakterisieren. Dieser Ansatz wird zudem weiterentwickelt und mit Techniken der Inhaltsanalyse kombiniert, um thematische Entwicklungen in Diskussionsforen zu analysieren. Diskussionsforen sind auch Gegenstand zweier weiterer Studien, die den Austausch von Informationen zwischen Lernenden in MOOCs zu untersuchen. Die einzelnen Diskussionsstränge werden dabei als dynamische Artefakte angesehen, die dann dazu genutzt werden um soziale Netzwerke zu extrahieren, die den Informationsaustausch zwischen Lernenden abbilden. Diese Netzwerke werden dahingehend analysiert, unterschiedliche Rollen von Forumsnutzern bezüglich ihrer Position in dem Netzwerk zu identifizieren. Dazu werden verschiedene Ansätze vorgestellt, die die strukturellen Charakteristiken des Informationsaustauschnetzwerks über die Zeit darstellen, sowie Diskussionsthemen als zusätzliche Informationen berücksichtigen

A Brief Survey of Deep Learning Approaches for Learning Analytics on MOOCs

Massive Open Online Course (MOOC) systems have become prevalent in recent years and draw more attention, a.o., due to the coronavirus pandemic’s impact. However, there is a well-known higher chance of dropout from MOOCs than from conventional off-line courses. Researchers have implemented extensive methods to explore the reasons behind learner attrition or lack of interest to apply timely interventions. The recent success of neural networks has revolutionised extensive Learning Analytics (LA) tasks. More recently, the associated deep learning techniques are increasingly deployed to address the dropout prediction problem. This survey gives a timely and succinct overview of deep learning techniques for MOOCs’ learning analytics. We mainly analyse the trends of feature processing and the model design in dropout prediction, respectively. Moreover, the recent incremental improvements over existing deep learning techniques and the commonly used public data sets have been presented. Finally, the paper proposes three future research directions in the field: knowledge graphs with learning analytics, comprehensive social network analysis, composite behavioural analysis