Funded Projects
Project 3: Data Competencies for People with Special Needs
Project period:
January 2021 – December 2021
Project Partner:
Florian Berens, Chair of Quantitative Methods and Statistics, Faculty of Social Sciences, University of Göttingen
Sina Ike, Chair of Statistics, Faculty of Business and Economics, University of Göttingen
Prof. Dr. Thomas Kneib, Chair of Statistics, Faculty of Business and Economics, University of Göttingen
Prof. Dr. Karin Kurz, Chair of Sociology / Social Stratification, Faculty of Social Sciences, University of Göttingen
Dr. Nina-Kristin Pendzich, Experimental Sign Language Lab, Faculty of Humanities, University of Göttingen
Dr. Wolfgang Radenbach, Digitalization in Teaching and Learning, Central Administration, University of Göttingen
Dr. Benjamin Säfken, Chair of Statistics, Faculty of Business and Economics, University of Göttingen
Dr. Alexander Silbersdorff, Chair of Statistics, Faculty of Business and Economics, University of Göttingen
Prof. Dr. Markus Steinbach, Chair of German Linguistics, Faculty of Humanities, University of Göttingen
Project Funding:
The project was financially supported by the Lower Saxony Ministry of Science and Culture.
Project Description:
Data competencies and their teaching are becoming increasingly relevant in light of current technological developments and demands on society and the economy. This project aims at a needs- and diversity-oriented delivery of essential content for the training of basic data literacy skills by addressing students with special needs and challenges for whom traditional teaching formats have strong and often difficult to overcome barriers (auditory, visual, linguistic). Here, particular attention is paid to hearing-impaired students, visually-impaired students, and students with comprehension problems of the German language.
Building on the Learning to Read Data project, which teaches fundamental data skills across faculties, intuitive instructional videos are being developed. These outline key content in a form tailored to the specific needs of each group. These videos will be made available as Open Educational Resources (OERs) to student groups with particular needs and challenges, addressing in particular students from a range of (large-scale) courses with from the field of statistics.
Project 2: Learning Analytics for Students and Lecturers: Individual and Aggregated Feedback in Large Lectures
Project period:
January 2020 – September 2020
Project Partner:
Florian Berens, Chair of Quantitative Methods and Statistics, Faculty of Social Sciences, University of Göttingen
Dr. Sebastian Hobert, Chair of Application Systems and E-Business, Faculty of Business and Economics, University of Göttingen
Project Funding:
The project was financially supported by Göttingen Campus Q-Plus, financed by the Federal Ministry of Education and Research.
Project Description:
Learning analytics, as a scientific discipline, aims to analyze large learning-related data sets to gain insights into learning in order to improve learning processes. Learning analytics methods can also be used to provide direct feedback to courses. Particularly in large university lectures, these methods can serve as an aid for both learners and teachers, since the few personal relationships there make it even more difficult to give and receive feedback than in courses with smaller numbers of participants. Thus, teachers and learners lack information about learning status and learning process, which would be important for the adaptation of further teaching and learning. The first step of the project proposed here is therefore to supplement the existing Ole system with a "Learning Analytics for Students" and a "Learning Analytics for Lecturers" component. This will provide both students and lecturers with continuous, individually prepared feedback on their learning status and learning process during the semester. In a second step, learners will be helped to interpret and react to this feedback by setting up a Learning Analytics consultation hour that offers individual learning advice. For lecturers and tutors, students' learning is analyzed and reviewed on a weekly basis. They then revise the teaching concepts for subsequent face-to-face lectures and tutorials in joint tutorial meetings.
Project 1: Interactive Learning on Demand – Artificial Intelligence as a Tutor in Large Lectures
Project period:
January 2019 – Dezember 2019
Project Partner:
Florian Berens, Chair of Quantitative Methods and Statistics, Faculty of Social Sciences, University of Göttingen
Dr. Sebastian Hobert, Chair of Application Systems and E-Business, Faculty of Business and Economics, University of Göttingen
Project Funding:
The project was financially supported by Stifterverband.
Project Description:
In courses with large numbers of participants, it is often difficult to supervise students individually. Students are therefore responsible for performing large parts of their learning independently, which is particularly challenging in the introductory phase of studies. The aim of the project is therefore to create a tutoring service for students that is independent of location and accessible at all times.
This is implemented in the form of an app that uses artificial intelligence approaches to answer questions individually in a chat between the student and the app. The micro content made available via the app reworks the content of the class sessions and can be used as additional learning support. The app makes it easier for students to access individual support, while teachers receive constant feedback on the learning status and difficulties of their students via aggregated usage behavior, which they can use to design subsequent lectures and tutorials.
Further Links:
https://www.stifterverband.org/lehrfellowships/2018/hobert_berens