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.
A Customizable and Incremental Processing Approach for Learning Analytics
The ability of learning analytics to improve the learning/teaching processes is widely recognized. In this paper, the learning analytics architecture developed at the Digital Content Production Center of the Technical University of Cartagena (Spain) is presented. This architecture contributes to the field of learning analytics in two aspects: it allows for dashboard customization and improves the efficiency of the analysis of learners’ interaction data. Events resulting from learners’ interaction are captured and stored in Caliper standard format, to be further processed incrementally to allow dashboards to be shown without delay to teachers. Customization is considered a mandatory requirement for learning analytics tools, however, although some proposals have recently been made, a greater research effort in this topic is necessary. In the present work, this requirement is addressed by defining a domain-specific language (DSL) that allows teachers to customize dashboards. This language allows to express indicators (logical expressions) that classify students into different groups depending on their performance level. The paper also shows how our learning analytics approach was evaluated with a course that applies a flipped classroom method, and how it compares to the most relevant related works that have been published.
Translating Legal Language and Comparative Law
Legal texts are in the focus of both lawyers and translators. This paper discusses the binary opposition of these two views especially in the light of contract law. There is one crucial epistemic difference between the point of view of the translator and the lawyer when it comes to the interpretation of legal texts. In the translator’s view legal text is traditionally conceived as static as to its nature; something that already exists in the form of text. Traditionally, the translator is interested in the text itself as it was written down in a document, i.e. the translator needs to go back to the frozen moment when a legal document was constructed and finalised. The lawyer, in contrast, is very much interested not only in the text of the legal document but also in the contextual surroundings of the text. In fact, for legal interpretation, sometimes the key-elements for the interpretation come from outside of the text itself. This paper concludes that legal translation is in many cases closer to transcreation than it is to traditionally understood translation.