We report on our experience with shared-task-based teaching based on 12 undergraduate and master's courses.From the lessons learned in these courses, we derive a novel classification of shared tasks into four classes based on properties of their solution spaces.The classification aligns the courses with their didactic goals and supports students and instructors in achieving them.We systematically analyze the teaching and learning conditions of each class, such as the required effort and prior knowledge of students and instructors.Our analyses show that shared tasks are a promising teaching method for computer science education that can be adapted to different environments.However, the diversity of shared tasks also requires customized recommendations for instructors.

Learning Analytics (LA) is nowadays ubiquitous in many educational systems, providing the ability to collect and analyze student data in order to understand and optimize learning and the environments in which it occurs. On the other hand, the collection of data requires to comply with the growing demand regarding privacy legislation. In this paper, we use the Student Expectation of Learning Analytics Questionnaire (SELAQ) to analyze the expectations and confidence of students from different faculties regarding the processing of their data for Learning Analytics purposes. This allows us to identify four clusters of students through clustering algorithms: Enthusiasts, Realists, Cautious and Indifferents. This structured analysis provides valuable insights into the acceptance and criticism of Learning Analytics among students.

In this paper, we discuss the benefits and challenges of shared tasks as a teaching method. A shared task is a scientific event and a friendly competition to solve a research problem, the task. In terms of linking research and teaching, shared-task-based tutorials fulfill several faculty desires: they leverage students' interdisciplinary and heterogeneous skills, foster teamwork, and engage them in creative work that has the potential to produce original research contributions. Based on ten information retrieval (IR) courses at two universities since 2019 with shared tasks as tutorials, we derive a domain-neutral process model to capture the respective tutorial structure. Meanwhile, our teaching method has been adopted by other universities in IR courses, but also in other areas of AI such as natural language processing and robotics.