Open PhD Position at the UDE Social Computing Group

In the frame of the DAAD Graduate School Scholarship Programme (GSSP) starting in 2019, the Social Computing Group at the University of Duisburg-Essen offers a 3-years fully funded position for a PhD Student in data science with a focus on user modeling, personalization and learning analytics. Detailed information can be found here and in the full job advert on the User-Centred Social Media (UCSM) graduate school Website. Application deadline is July 19, 2018. Please consider and distribute the job opening.

Appointment as Professor of Social Computing at the University of Duisburg-Essen

As of April 2017, I have left RWTH Aachen University. I am now at the University of Duisburg-Essen as professor of computer science and head of the Social Computing Group. See my new home page there.

The Open Learning Analytics Platform - OpenLAP

Open Learning Analytics

The aim of Open Learning Analytics (OLA) is to improve learning efficiency and effectiveness in lifelong learning environments. In order to understand learning and improve the learning experience and teaching practice in today's networked and increasingly complex learning environments, there is a need to scale Learning Analytics (LA) up which requires a shift from closed LA tools and systems to LA ecosystems and platforms where everyone can contribute and benefit. OLA refers to an ongoing analytics process that encompasses openness at all four dimensions of the LA reference model.

• What?: It accommodates the considerable variety in learning data and contexts. This includes data coming from traditional education settings (e.g. LMS) and from more open-ended and less formal learning settings (e.g. PLEs, MOOCs).
• Who?: It serves different stakeholders with very diverse interests and needs.
• Why?: It meets different objectives according to the particular point of view of the different stakeholders.
• How?: It leverages a plethora of statistical, visual, and computational tools, methods, and methodologies to manage large datasets and process them into metrics which can be used to understand and optimize learning and the environments in which it occurs. 

The Open Learning Analytics Platform (OpenLAP) 

 

The Open Learning Analytics Framework (OpenLAP)

OpenLAP Architecture

The Open Learning Analytics Platform (OpenLAP) provides a theoretically sound conceptual framework for open learning analytics along with a mature reference implementation.
 
OpenLAP encompasses different stakeholders associated through a common interest in LA but with diverse needs and objectives, a wide range of data coming from various learning environments and contexts, as well as multiple infrastructures and methods that enable to draw value from data in order to gain insight into learning processes.

OpenLAP follows a user-centric approach to engage end users in a flexible definition and dynamic generation of indicators. To meet the requirements of diverse stakeholders, OpenLAP provides a modular and extensible architecture that allows the easy integration of new analytics modules, analytics methods, and visualization techniques.

More information about OpenLAP is available in this recent publication and on the project Website (with use cases, detailed architecture, souce code, documentation, related publications etc.)

Citation:

Chatti, M. A., Muslim, A., & Schroeder, U. (2017). Toward an Open Learning Analytics Ecosystem. In Big Data and Learning Analytics in Higher Education (pp. 195-219). Springer International Publishing.