Refine
Year of publication
Document Type
- Article (unreviewed) (6)
- Conference Proceeding (4)
- Article (reviewed) (3)
- Part of a Book (2)
- Report (1)
Conference Type
- Konferenzartikel (4)
Has Fulltext
- no (16) (remove)
Is part of the Bibliography
- yes (16) (remove)
Keywords
- Informatik (3)
- Algorithmus (1)
- Autonomie (1)
- Didaktisches Design (1)
- Digitale Lernszenarien (1)
- E-Learning (1)
- Gamification (1)
- Gamification-Design (1)
- Hochschulbildung (1)
- Hochschuldidaktik (1)
Institute
Open Access
- Open Access (8)
- Closed (2)
- Closed Access (2)
- Diamond (2)
- Bronze (1)
Das Virtuelle Informatiklabor soll Schülern und Studierenden den übergroßen Respekt vor dem Fach Informatik nehmen und sie beim Lernen der Inhalte unterstützen. Zu diesem Zweck werden grundlegende Algorithmen der Informatik anhand konkreter Aufgabenstellungen in interaktiven Anwendungen behandelt, um den Lernenden das explorative Erkunden zu ermöglichen. Animationen sollen das Verstehen fördern, Experimente das eigenständige, durch vielfältige Hilfen unterstützte Anwenden und Umsetzen des Gelernten. Der erste Themenbereich im Virtuellen Informatiklabor umfasst die Rekursion, die in mehreren Anwendungen präsentiert wird.
Autonome Systeme im Consumerbereich - Was bedeutet die Autonomie technischer Systeme für den Kunden
(2018)
This paper describes the concept and some results of the project "Menschen Lernen Maschinelles Lernen" (Humans Learn Machine Learning, ML2) of the University of Applied Sciences Offenburg. It brings together students of different courses of study and practitioners from companies on the subject of Machine Learning. A mixture of blended learning and practical projects ensures a tight coupling of machine learning theory and application. The paper details the phases of ML2 and mentions two successful example projects.
Machine learning (ML) has become highly relevant in applications across all industries, and specialists in the field are sought urgently. As it is a highly interdisciplinary field, requiring knowledge in computer science, statistics and the relevant application domain, experts are hard to find. Large corporations can sweep the job market by offering high salaries, which makes the situation for small and medium enterprises (SME) even worse, as they usually lack the capacities both for attracting specialists and for qualifying their own personnel. In order to meet the enormous demand in ML specialists, universities now teach ML in specifically designed degree programs as well as within established programs in science and engineering. While the teaching almost always uses practical examples, these are somewhat artificial or outdated, as real data from real companies is usually not available. The approach reported in this contribution aims to tackle the above challenges in an integrated course, combining three independent aspects: first, teaching key ML concepts to graduate students from a variety of existing degree programs; second, qualifying working professionals from SME for ML; and third, applying ML to real-world problems faced by those SME. The course was carried out in two trial periods within a government-funded project at a university of applied sciences in south-west Germany. The region is dominated by SME many of which are world leaders in their industries. Participants were students from different graduate programs as well as working professionals from several SME based in the region. The first phase of the course (one semester) consists of the fundamental concepts of ML, such as exploratory data analysis, regression, classification, clustering, and deep learning. In this phase, student participants and working professionals were taught in separate tracks. Students attended regular classes and lab sessions (but were also given access to e-learning materials), whereas the professionals learned exclusively in a flipped classroom scenario: they were given access to e-learning units (video lectures and accompanying quizzes) for preparation, while face-to-face sessions were dominated by lab experiments applying the concepts. Prior to the start of the second phase, participating companies were invited to submit real-world problems that they wanted to solve with the help of ML. The second phase consisted of practical ML projects, each tackling one of the problems and worked on by a mixed team of both students and professionals for the period of one semester. The teams were self-organized in the ways they preferred to work (e.g. remote vs. face-to-face collaboration), but also coached by one of the teaching staff. In several plenary meetings, the teams reported on their status as well as challenges and solutions. In both periods, the course was monitored and extensive surveys were carried out. We report on the findings as well as the lessons learned. For instance, while the program was very well-received, professional participants wished for more detailed coverage of theoretical concepts. A challenge faced by several teams during the second phase was a dropout of student members due to upcoming exams in other subjects.