Moodle UDE

Learning Analytics (LA) has attracted a great deal of attention as practitioners, institutions, and researchers are increasingly seeing the potential that LA has to shape the future technology-enhanced learning landscape. LA is an emerging data science field that represents the application of big data and analytics in education. It deals with the development of methods that harness educational data sets to support the learning process. LA is an interdisciplinary field involving competencies from computer science, cognitive psychology, and pedagogy. It leverages various computer science methods. These include statistics, big data, machine learning, data/text mining, information visualization, visual analytics, and recommender systems. The first part of the course will provide a systematic overview of this emerging field and its key concepts through a reference model for LA-based on four dimensions, namely data, environments, context (what?), stakeholders (who?), objectives (why?), and methods (how?). In the second part of this course, we will discuss various methods and techniques required to develop innovative LA systems, in relation to each dimension of the LA reference model. In the last part of the course, current topics and trends in LA research will be presented and discussed in invited talks. The presented methods and technologies will be further investigated and applied in small student projects carried out throughout the course.

LA Webpage

About the course: Learning Analytics (LA) has attracted a great deal of attention as practitioners, institutions, and researchers are increasingly seeing the potential that LA has to shape the future technology-enhanced learning landscape. LA is an emerging data science field that represents the application of big data and analytics in education. It deals with the development of methods that harness educational data sets to support the learning process. LA is an interdisciplinary field involving competences from computer science, cognitive psychology, and pedagogy. It leverages various computer science methods. These include statistics, big data, machine learning, data/text mining, information visualization, visual analytics, and recommender systems. The first part of the course will provide a systematic overview on this emerging field and its key concepts through a reference model for LA based on four dimensions, namely data, environments, context (what?), stakeholders (who?), objectives (why?), and methods (how?). In the second part of this course, we will discuss various methods and techniques required to develop innovative LA systems, in relation to each dimension of the LA reference model. In the last part of the course, current topics and trends in LA research will be presented and discussed in invited talks. The presented methods and technologies will be further investigated and applied in small student projects carried out throughout the course.

LA Webpage

About the course: Learning Analytics (LA) has attracted a great deal of attention as practitioners, institutions, and researchers are increasingly seeing the potential that LA has to shape the future technology-enhanced learning landscape. LA is an emerging data science field that represents the application of big data and analytics in education. It deals with the development of methods that harness educational data sets to support the learning process. LA is an interdisciplinary field involving competences from computer science, cognitive psychology, and pedagogy. It leverages various computer science methods. These include statistics, big data, machine learning, data/text mining, information visualization, visual analytics, and recommender systems. The first part of the course will provide a systematic overview on this emerging field and its key concepts through a reference model for LA based on four dimensions, namely data, environments, context (what?), stakeholders (who?), objectives (why?), and methods (how?). In the second part of this course, we will discuss various methods and techniques required to develop innovative LA systems, in relation to each dimension of the LA reference model. In the last part of the course, current topics and trends in LA research will be presented and discussed in invited talks. The presented methods and technologies will be further investigated and applied in small student projects carried out throughout the course.

The students will get an overview of the use of living systems (i.e. microbial communities, microorganisms or biological molecules such as enzymes) for the production of relevant sub-stances and process optimization for human benefit. Starting with a general overview of biotechnological applications and significance, classical fermentations in food industries, special production strains, biocatalysis by enzymes as well as environmental biotechnology will be discussed

Trotz umfangreicher politischer Bekenntnisse, die 1.5 Grad-Grenze einzuhalten, schreiten die ökologischen Krisen weiter fort. Dieser Kurs untersucht Art und Umfang des vorliegenden Problems und beleuchtet Leerstellen der standardökonomisch sowie politisch dominanten Lösungsansätze, die auf CO2-Bepreisung und grünes Wachstum abzielen. Die inhaltliche Auseinandersetzung deckt dabei drei Themenfelder ab:  

1. den Zusammenhang zwischen Wirtschaftswachstum, öffentlicher Daseinsvorsorge und Wohlergehen; 
2. die Verzahnung von Klimapolitik und sozioökonomischer Ungleichheit;  
3. die Rolle von Macht und Politik für die Nachhaltigkeitswende 

Der Kurs geht der Frage nach, wie ein “gutes Leben für alle” innerhalb planetarer Grenzen organisiert werden kann, er unternimmt eine empirische Bestandsaufnahme des Status Quo und analysiert politische sowie ökonomische Hemmnisse des Übergangs zu einer emissionsarmen und sozial gerechten Wirtschaftsweise.