Teaching

Learning goals:
The lecture lays the foundation for the research and application field of educational technologies. The participants learn about the interplay of the three main disciplines in the field of educational technologies mainly educational science, psychology, and computer science. On this foundation, the participants will learn about the main theories, methods, and technologies applied in the field.

Content:
Technology is affecting the way people learn and can make learning more meaningful, transferable, effective, or more attractive. Within this lecture, we will look into the research and application field of educational technologies. We will explore how the latest technological trends are transforming the way individuals learn and how organizations can plan sustainable learning interventions by taking advantage of the latest technologies and approaches. These approaches will not only be studied; rather, we also aim to embed them into the lecture in order to experience how educational technologies can change the way we learn and teach. The lecture will first lay the theoretical educational and psychological foundation for ongoing educational technology practitioners and experts. Based on this basics, we will explore various pressing research topics in the field of educational technologies such as Trusted Learning Analytics or Sensor-based Learning Technologies.
• Trusted Learning Analytics is the collection and analysis of data about learners and their contexts, in order to understand and optimize learning experiences and the environments in which they occur.
• Sensor-based Learning Technologies investigates new technologies like AR/VR, sensors and wearables that emerge in an ever-increasing pace. While none of these technologies are directly aimed at education, they do have a strong impact on society and thus on education by creating the opportunity for new ways of learning.

Learning goals:
The lecture lays the foundation for the research and application field of educational technologies. The participants learn about the interplay of the three main disciplines in the field of educational technologies mainly educational science, psychology, and computer science. On this foundation, the participants will learn about the main theories, methods, and technologies applied in the field.

Organisation:
The lecture and the exercise will be given in English. The quality of English writing and talking of the participants will not be considered for the final marks lecture.

Learning goals:
The lecture aims to give an introductionary overview over the basic technologies and methodologies used in educational technologies. The students will have a good overview of didactics, methods and interfaces used. Furthermore, also basics about web technologies, human computer interaction and visualization are known.

Content:
This lecture provides an overview of technical systems and platforms in the field of eLearning, in particular learning management systems (LMS), examination systems, eLecture systems, ePortfolio systems and campus management systems. In addition to the structure and use, exchange formats and individual solutions for digital learning scenarios are also presented. In addition to the purely functional software requirements and their implementation, the  requirements from the perspective of the teachers and students are also dealt with. The user interfaces of the systems used must have a good user experience, which can be measured using methods of human-computer interaction. These are dealt with with a focus on didactic scenarios. In principle, personal data must be used in the teaching / learning context so that  various analyzes can be carried out. These form the basis for the learning analytics. Data protection requirements must be taken into account.

Organisation:
In addition to a theoretical overview, various didactic scenarios are implemented using current systems and analyzed according to technical criteria. Within the exercise, individual examples are presented with a current system and challenges are addressed. These are compared with current research results and discussed critically.

In the exercises, four-weekly homeworks or small projects are to be worked on in teams and presented in the exercise groups and the solutions defended. QIS Link

Learning goals: 
The students are familiar with the basics of test theory and questionnaire construction and can apply these within the framework of guided exercise projects. They know methods of generating and building hypotheses as well as population-describing and hypothesis-testing methods. The students carry out simple statistical procedures (e.g. procedures such as frequency and cross tables, t-tests) and sophisticated methods for data analysis (e.g. linear regression, factor or cluster analysis, general linear models, PPT models) and are able to perform these classify and reflect critically. On the basis of empirically research-oriented exercise projects, the students acquire the ability to work independently or independently in small groups. Develop solutions and present them to the plenary.

Contents:
The lecture deals with current concepts and methods of technology-related empirical teaching / learning research. It introduces the development of suitable research designs on issues of educational technologies and methods of data collection and data evaluation of the classical test theory (KTT) and probabilistic test theory (PTT) and problematizes the different theoretical bases of the approaches. In the accompanying exercise, the concepts and methods of the lecture are practically tested using tasks and the implementation of an empirical exercise project.

Content: Within the practicum, we dig deeper into the topics that the EduTec 1 lecture was introducing by working concrete research challenges on pressing research topics in the field of educational technologies such as: Open Education, Trusted Learning Analytics, New Learning Experience, and Mobile Learning.

• Open Online Education offers an alternative path for education, competence development and professionalization beyond the traditional borders of educational institutions. Learners enter and engage in open educational practices to meet, network, collaborate, work, learn and innovate.
• Trusted Learning Analytics is the collection and analysis of data about learners and their contexts, in order to understand and optimize learning experiences and the environments in which they occur.
• New Learning Experience investigates new technologies like AR/VR, sensors and wearables that emerge in an ever-increasing pace. While none of these technologies are directly aimed at education, they do have a strong impact on society and thus on education by creating the opportunity for new ways of learning.
• Mobile Learning focuses on how learners easily move from one „place“ to another and create their own learning „places“, e.g. by using mobile devices and cloud technology. Learners‘ mobility and control on what, when, where, and how they want to learn are in the center of mobile learning. Mobile Learning explores new innovative technology and pedagogy and extends the formal classroom with learning experiences in the field. The course will be supported by a Moodle environment and various technology enhancements that will enable all participants to experiences a technology-enriched learning scenario. Communication will be in English, but final reports can also be written in German. The quality of English writing and talking of the participants will not be considered for the final marks.

Content: Technology is affecting the way people learn and can make learning more meaningful, transferable, effective, continuous and fun for learners. Within this seminar, we will look into the research and application field of educational technologies. Within this course, we will explore how latest technological trends are transforming the way individuals learn and how organizations can plan sustainable learning interventions by taking advantage of latest technologies and approaches. Within the seminar will research the state of the art of various topics of Educational Technologies based on meta-reviews and new publications. We will cover topics like:

1. Mobile Learning,
2. Robots for Education,
3. Network Learning, 
4. Open Education,
5. Augmented reality in Education,
6. Trusted Learning Analytics,
7. Computer Supported Collaborative Learning,
8. Gamification,
9. Computational Thinking for Schools,
10. Digital Skills,
11. and AI for Education.

Organisation: The seminar will take place via Moodle and Zoom Sessions. The seminar can be completed by studying a topic of Educational Technologies and writing a scientific report of 10-12 pages. The whole seminar will be in English but the quality for English writing and presenting will not be considered for the final mark of the seminar.

Summary:
Students will be introduced to research methods in Educational Technologies by interactively following a whole cycle of research starting from the identification of a problem, investigating the literature, prototyping an intervention, designing a study to test the intervention, collecting data, analyzing data with various methods and tools, reporting, evaluating and presenting the results. This specification is a step stone module for preparing a BA / MA thesis on Educational Technologies.
Learning Goals:

• Students should get the experience of conducting research on Educational Technologies a field from applied computer science.
• Students will develop scientific thinking skills.
• Students will practice 21^st century skills such as collaboration and presentation.
• Students will be well prepared to write their BA / MA thesis after the seminar.

Summary:
Many concepts of Artificial Intelligence (AI) such as ‘machine learning’ or ‘artificial neural networks’ are inspired by human intelligence. Most of the applications people use every day are powered by human data such as movie streaming apps, speech recognition, machine translation, or music recommendations. But can we design AI tools which can support humans in their decision and augment human abilities in day-to-day life? In this seminar, we will focus on different ways in which AI can support human decisions and activities, with a particular focus on education and human learning. We will look at the opportunities that AI brings to humans as well as the connected risks. We will delve into the assumptions, models, and principles which need to be embedded in the AI system to make them fair, trustable, explainable, and understandable for the user.

Learning Goals: The students will learn about:

• Peculiarities of AI systems for human support
• Various roles that AI can have for humans (with special attention to education)
• Design Components of Human-AI systems
• Connections to human psychology, communication, and learning
• User modelling and user adaptation
• Roles humans can have in shaping and influencing AI systems
• Multimodal and multi-sensor data collection and analysis
• Role of feedback in AI applications
• Components for Responsible AI systems including Ethics, Privacy and Trust
• Algorithmic transparency and explainability
• Research directions and opportunities in the field