Tobias Hecking

Analysis of dynamic resource access patterns in a blended learning course

This paper presents an analysis of resource access patterns in a recently conducted master level university course. The specialty of the course was that it followed a new teaching approach by providing additional learning resources such as wikis, self-tests and videos. To gain deeper insights into the usage of the provided learning material we have built dynamic bipartite student -- resource networks based on event logs of resource access. These networks are analysed using methods adapted from social network analysis. In particular we uncover bipartite clusters of students and resources in those networks and propose a method to identify patterns and traces of their evolution over time.

A workbench to construct and re-use network analysis workflows: concept, implementation, and example case

In this paper we introduce the concept of a web-based analytics workbench to support researchers of social networks in their analytic processes. Making explicit these processes allows for sound design, re-use, and automated execution using an authoring system for visual representations of these analytic workflows. The workbench is implemented according to a flexible technical framework in which external and newly-defined analytic components can be integrated and used in conjunction with other analytic components. As a showcase we discuss a complex analytic process.

Investigating social and semantic user roles in MOOC discussion forums

This paper describes the analysis of the social and semantic structure of discussion forums in massive open online courses (MOOCs) in terms of information exchange and user roles. To that end, we analyse a network of forum users based on information-giving relations extracted from the forum data. Connection patterns that appear in the information exchange network of forum users are used to define specific user roles in a social context. Semantic roles are derived by identifying thematic areas in which an actor seeks for information (problem areas) and the areas of interest in which an actor provides information to others (expertise). The interplay of social and semantic roles is analysed using a socio-semantic blockmodelling approach. The results show that social and semantic roles are not strongly interdependent. This indicates that communication patterns and interests of users develop simultaneously only to a moderate extent. In addition to the case study, the methodological contribution is in combining traditional blockmodelling with semantic information to characterise participant roles.

A Flexible and Extendable Learning Analytics Infrastructure

Currently architectures for learning analytics infrastructures are being developed in different contexts. While some approaches are designed for specific types of learning environments like learning management systems (LMS) or are restricted to specific analysis tasks, general solutions for learning analytics infrastructures are still underrepresented in current research. This paper describes the design of a flexible and extendable architecture for a learning analytics infrastructure which incorporates different analytics aspects such as data storage, feedback mechanisms, and analysis algorithms. The described infrastructure relies on loosely coupled software agents that can perform different analytics task independently. Hence, it is possible to extend the analytic functionality by just adding new agent components. Furthermore, it is possible for existing analytics systems to access data and use infrastructure components as a service. As a case study, this paper describes the application of the proposed infrastructure as part of the learning analytics services in a large scale web-based platform for inquiry-based learning with online laboratories.

Dynamics of MOOC discussion forums

In this integrated study of dynamics in MOOCs discussion forums, we analyze the interplay of temporal patterns, discussion content, and the social structure emerging from the communication using mixed methods. A special focus is on the yet under-explored aspect of time dynamics and influence of the course structure on forum participation. Our analyses show dependencies between the course structure (video opening time and assignment deadlines) and the over-all forum activity whereas such a clear link could only be partially observed considering the discussion content. For analyzing the social dimension we apply role modeling techniques from social network analysis. While the types of user roles based on connection patterns are relatively stable over time, the high fluctuation of active contributors lead to frequent changes from active to passive roles during the course. However, while most users do not create many social connections they can play an important role in the content dimension triggering discussions on the course subject. Finally, we show that forum activity level can be predicted one week in advance based on the course structure, forum activity history and attributes of the communication network which enables identification of periods when increased tutor supports in the forum is necessary.

Uncovering the Structure of Knowledge Exchange in a MOOC Discussion Forum

This work explores methods to investigate the structure of knowledge exchange in discussion forums in massive open online courses (MOOCs) explicitly taking into account changing patterns over time. Various aspects of forum analysis combining different approaches are exemplified with a case of forum discussions from a Coursera MOOC.

On the Detection of Influential Actors in Social Media

Detection of influential actors in social media plays an important role for increasing the quality and efficiency of work and services in many fields such as education, marketing, etc. This work aims to introduce a new approach for the characterization of influential actors in online social media, such as Twitter. We present on a model of influence of an actor that is based on the attractiveness of the actor in terms of the number of other new actors with which he or she has established relations over time. We have used this concept and measure of influence to determine optimal seeds in a simulation of influence maximization using two empirically collected social networks for the underlying graphs.

Analysis of User Roles and the Emergence of Themes in Discussion Forums

This work explores network analysis methods for the analysis of emergent themes as well as types of users in discussion forums. The paper provides both, a description of the analysis approach and its application as a case study. To that end, keywords are extracted from forum threads and then linked to the forum users resulting in a bipartite network based on their activity in discussion threads. Applying bipartite clustering on those networks, groups of users with common interest in themes can be identified as well as groups of related keywords based on their common relations to users. As a case study, the approach is applied to a discussion forum of a Courser a MOOC. The results reveal some interesting patterns and phenomena of thematic development that take place in such large scale learning courses.

Two Make a Network: Using Graphs to Assess the Quality of Collaboration of Dyads

In this paper we explore the application of network analysis techniques in order to analyze synchronous collaborative activities of dyads. The collaborative activities are represented and visualized as networks. We argue that the characteristics and properties of the networks reflect the quality of collaboration and therefore can support the analysis of collaborative activities in an automated way. To support this argument we studied the collaborative practice of 228 dyads based on graphs. The properties of each graph were evaluated in comparison to ratings of collaboration quality as assessed by human experts. The activities were also examined with respect to the solution quality. The paper presents the method and the findings of the study.

AngeLA: Putting the teacher in control of student privacy in the online classroom

Learning analytics (LA) is often considered as a means to improve learning and learning environments by measuring student behaviour, analysing the tracked data and acting upon the results. The use of LA tools implies recording and processing of student activities conducted on software platforms. This paper proposes a flexible, contextual and intuitive way to provide the teacher with full control over student activity tracking in online learning environments. We call this approach AngeLA, inspired by an angel guarding over LA privacy. AngeLA mimics in a virtual space the privacy control mechanism that works well in a physical room: if a person is present in a room, she is able to observe all activities happening in the room. AngeLA serves two main purposes: (1) it increases the awareness of teachers about the activity tracking and (2) provides an intuitive way to manage the activity tracking permissions. This approach can be applied to various learning environments and social media platforms. We have implemented AngeLA in Graasp, a social platform that fosters collaborative activities.