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.
Slotted Waveguide Antenna With Two Radiation Nulls
A design of a linear array antenna having two radiation nulls and otherwise omnidirectional radiation pattern is reported. A new configuration of a waveguide broad wall longitudinal slot doublet with positive and negative offsets with respect to the waveguide center line is used to design the array. This type of doublet produces two radiation nulls. Each of the null directions can be shifted at least up to plusmn25deg by proper choice of slot offset. Resonance and radiation characteristics of the proposed doublet are also presented. Finally, one linear array antenna consisting of seven doublets has been fabricated. Measurements show that the 15 dB null widths are 14.2deg and the null depths are at least 24.8 dB. Maximum gain ripple in the omnidirectional region is 1.75 dB.