A Ubiquitous Learning Analytics Architecture for a Service-Oriented MOOC Platform
As Massive Open Online Courses (MOOCs) generate a huge amount of learning activity data through its thousands of users, great potential is provided to use this data to understand and optimize the learning experience and outcome, which is the goal of Learning Analytics. But first, the data needs to be collected, processed, analyzed and reported in order to gain actionable insights. Technical concepts and implementations are rarely accessible and therefore this work presents an architecture how Learning Analytics can be implemented in a service-oriented MOOC platform. To achieve that, a service based on extensible schema-agnostic processing pipelines is introduced for the HPI MOOC platform. The approach was evaluated regarding its scalability, extensibility, and versatility with real-world use cases. Also, data privacy was taken into account. Based on five years of running the service in production on several platform deployments, six design recommendations are presented which can be utilized as best practices for platform vendors and researchers when implementing Learning Analytics in MOOCs.
Taking Informed Action on Student Activity in MOOCs
This paper presents a novel approach to understand specific student behavior in MOOCs. Instructors currently perceive participants only as one homogeneous group. In order to improve learning outcomes, they encourage students to get active in the discussion forum and remind them of approaching deadlines. While these actions are most likely helpful, their actual impact is often not measured. Additionally, it is uncertain whether such generic approaches sometimes cause the opposite effect, as some participants are bothered with irrelevant information. On the basis of fine granular events emitted by our learning platform, we derive metrics and enable teachers to employ clustering, in order to divide the vast field of participants into meaningful subgroups to be addressed individually.
Context Information in Mobile Telephony
Most research in context-aware computing offers definitions of context that consist solely of measurable information. Using mobile telephony as an example of a computing area, we provide a set of context information relevant to the area drawn from a qualitative case study: identity, location, time and present activity. After arguing that these context measures are not the same for other areas of computing, three guidelines for designing context-aware features for mobile telephony are provided. We conclude that context information should be defined for the area in which the researcher is present instead of attempting to provide an overall definition of context.