Technische 2024欧洲杯开户_欧洲杯APP下载-投注|官网 Ingolstadt (THI) is being funded by the programm ?Hochschullehre durch Digitalisierung st?rken“(Strengthening University Teaching Through Digitization) for the period of 2021 to 2024 to implement the THISuccessAI project. The project aims to significantly increase the student’s success in STEM degree programs through individualized learning paths. To this end, the university's AI is used to collect student’s learning and teaching information via learning analytics, evaluate it via pattern recognition and customize individualized learning paths from it. These are designed with (digital) teaching and learning units, among other things. A dashboard visualizes the learning status for students and lecturers. The project includes the creation of AI-driven tools that continuously evolve through further data enrichment, as well as the creation of digital learning nuggets for the design of the individualized learning paths. After the three-year development and implementation phase focusing on test subjects, the project will be extended to all THI subjects and programs.
Building AI-powered learning pathways using learning analytics
Project cluster
Subproject Learning Analytics
In the Subproject "Learning Analytics", the diverse data related to the learning process is subjected to systematic analysis and evaluation, considering data protection and ethical aspects. The aim is to assess the learner’s level of competence, to evaluate the individual impact of didactic methods and to give recommendations and prognoses for the design of learning paths..
People involved:
- Prof. Dr. Munir Georges (Language and Text Comprehension)
- Prof. Dr. S?ren Gr?ttrup (Machine Learning and Statistics)
- Prof. Dr. Stefanie Schmidtner (Sustainable Urban Development and Artificial Intelligence)
- Kai Hartung (Data Analyst)
- Panos Pagonis (Data Management)
- Di Wu (Data Analyst)
Subproject Platform- and Tool - Development
In the Subproject "Platform and Tool Development", the technical foundation for the collection, description and provision of the data streams previously obtained in the Subproject 1.1. will be created. According to the generation of individual learning paths in the LMS Moodle, the Subproject develops user-friendly and partially customized tools, such as a dashboard for students and teachers, an editor for the simple creation of learning nuggets, a Learning Companion plug-in to improve networking and exchange opportunities between students, and a chatbot (Thim) as a learning assistant.
People involved:
- Prof. Dr. Sebastian Apel (Programming and Software Engineering)
- Prof. Dr. Hans-Michael Windisch (Computer Engineering and Software Engineering)
- Prof. Dr. Christan Locher (Digital Business)
- Sambit Mallick (Chatbot Developer)
- Manjula Gangisetty (Web Developer AI)
- Khabbab Zakaria (Software Developer)
- Tanyo Dietz (UX Designer)
People involved:
- Prof. Dr. Katherine Roegner (Higher Education Didactics of Mathematics)
- Prof. Dr. Bernhard Glavina (Computer Engineering and Electrical Engineering)
- Prof. Dr. Robert Gold (Software Engineering und Language Programming)
- Prof. Dr. rer. nat. Franz Regensburger (Practical Computer Science and Programming)
- Verena Birkmeir (Scientific Employee for Statistics)
- Stefanie Boldt (Scientific Employee for Statistics)
- Rebekka Gerlach (Scientific Employee for Mathematics)
- Alen Jurisic (Scientific Employee for Electrical Engineering)
- Malina Lang (Scientific Employee for the STEM modules)
- Marcel Schmitt (Scientific Employee)
- Philipp S?chtig (Scientific Employee for Instructional Design)
- Florian Beil (Scientific employee for the STEM modules)
Subproject Individual Learning Paths for Static Groups (Stage I)
In the Subproject "Individual Learning Paths for Static Groups (Stage I)" the individualization of learning paths in the subjects Statistics, Mathematics, Electrical Engineering and Programming, for partially automated defined groups or learning styles, is carried out. The learning paths are designed using various placement tests, as well as pattern recognition. They are supported by learning nuggets tailored to learning styles, as well as dialog offers of the digital learning assistant "Thim".
Subproject Individual Learning Paths for Static Groups (Stage II)
In the Subproject "Individual Learning Paths for Static Groups (Stage II)", built on the results of Stage I, the fixed learning styles are successively dissolved and transferred into individualized AI-supported learning paths. In addition, the learning assistant "Thim" will be further developed, and the learning nuggets will be made more flexible.
Subproject Project organization
In the Subproject "Project Organization", the structures for a successful project implementation are established. Technical and didactic demands on the project are taken into account and quality is ensured in the long term by means of an accompanying evaluation.
People involved:
- Peter Huck (Project Coordination)
- Susanne Huber (Project Management)
Subproject Transfer
In the Subproject "Transfer", innovative exchange and participation formats are realized aiming an internal and external knowledge transfer on "AI and university teaching", e.g. the event of the "Digital Teaching Day" as well as the "Hackathon". Furthermore, data protection and ethical issues are clarified there.
People involved:
- Prof. Dr. Christian Stummeyer (Business Informatics und Digital Commerce)
- Prof. Dr. Matthias Uhl (Social Implications and Ethical Aspects of AI)
- Barbara Rehr (Data Protection)
Project management
Prof. Dr. Katherine Roegner
Phone: +49 841 9348-2544
Room: D024
E-Mail: Katherine.Roegner@thi.de
Prof. Dr.-Ing. Hans-Joachim Hof
Phone: +49 841 9348-2526
Room: D021
E-Mail: Hans-Joachim.Hof@thi.de
Prof. Dr. Sebastian Apel
Phone: +49 841 9348-5176
Room: A128
E-Mail: Sebastian.Apel@thi.de
With its program "Hochschullehre durch Digitalisierung st?rken", the "Stiftung Innovation in der Hochschullehre" is funding a total of 139 project proposals from colleges and universities over the next few years, including 115 individual and 24 collaborative applications, with a total funding volume of up to �330 million.
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