No abstract

Video is frequently used as a learning medium in a variety of educational settings, including large online courses as well as informal learning scenarios. To foster learner engagement around instructional videos, our learning scenario facilitates interactive note taking and commenting similar to popular social video-sharing platforms. This approach has recently been enriched by introducing nudging mechanisms, which raises questions about ensuing learning effects. To better understand the nature of these effects, we take a closer look at the content of the comments. Our study is based on an ex post analysis of a larger data set from a recent study. As a first step of analysis, video comments are clustered based on a feature set that captures the temporal and semantic alignment of comments with the videos. Based on the ensuing typology of comments, learners are characterized through the types of comments that they have contributed. The results will allow for a better targeting of nudges to improve video-based learning.

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 overall 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.

ABSTRACT:To further develop rich and expressive ways of modelling roles of contributors in discussion forums of online courses, particularly in MOOCs, networks of forum users are analyzed based on the relations of information-giving and information-seeking. Specific connection patterns that appear in the information exchange networks of forum users are used to characterize user roles. Additionally, semantic roles are derived by identifying thematic areas in which an actor (learner) looks for information (problem areas) and the areas of interest in which an actor provides information to others (areas of expertise). The interplay of social and semantic roles is analyzed using a socio-semantic blockmodelling approach. The results indicate that social and semantic roles are not strongly interdependent. The methodological contribution is in combining traditional blockmodelling with semantic information to characterize participant roles. Furthermore, we use sequential pattern analysis techniques to analyze the posting activity of users over time in terms of categories of cognitive engagement. The combination of the different approaches reveals that user roles derived from the analysis of engagement patterns are strongly related to socio-semantic user roles.

This paper presents an analysis of resource access patterns in two recently conducted online courses. One of these has been a master level university lecture taught as a blended learning course with a wide range of online learning activities and materials, including collaborative wikis, self-tests, and thematic videos. The other course has been offered in the form of a MOOC. As a specialty of this course, master level students from two different universities could participate as a regular university class and receive credits for successful completion. In both courses, online learning resources such as videos, scientific literature, and wikis played a central role. In this context, the motivation for our research was to investigate characteristic patterns of resource usage of the learners. In order to gain deeper insights into the usage of learning materials, we have adapted methods from social network analysis and applied them to dynamic bipartite student-resource networks built from event logs of the students' resource access. In particular, we describe the clustering of students and resources in such networks and propose a method to identify patterns of the cluster evolution over time.