STAIR Learning Lab Concept

Motivation

The ability to connect physical and virtual objects (things) with one another in almost any way has the Internet of Things (IoT) has grown in importance over the last ten years and now permeates all areas of life. and now permeates all areas of life, such as mobility (“smart mobility”, “autonomous driving”). Mobility”, “Autonomous Driving”, “Smart City”), housing (“Smart Home”), agriculture (“Smart Agriculture”), health (“Smart Agriculture”), health (“Smart Health”) or production (“Industry 4.0”). In order to master the complexity of IoT systems and to fully exploit their potential, AI technologies, which can, for example, control autonomously control vehicles based on sensor values from the environment, for example. The high practical importance and the almost unlimited possibilities to realize digital ideas and products with AI with AI technologies via IoT systems, are offset by the high level of technical understanding required to master these technologies (AI and IoT). A basic understanding and basic knowledge of AI and IoT technologies are therefore an indispensable component of an innovation-oriented and technically sound digital education. Furthermore, AI and IoT are also enabler technologies for other natural sciences such as biology biology or physics and their education, for example when it comes to the automatic recognition of plants or the measurement of physical quantities in modern laboratories. The high level of technical complexity, such as the interaction of different sensors and actuators, as well as the different quality requirements, for example in terms of safety, usability and performance, make the testing of such systems testing of such systems, which can be carried out in physical or virtual environments as a digital of the real system and its environment, is a central and proactive activity, to understand the technical complexity and quality requirements of AI and IoT systems, and to actively and to contribute actively and analytically to the design of AI and IoT systems.

Goals

The learning lab for AI and IoT technologies therefore has the following objectives:

• Activity-based teaching of basic and advanced knowledge on developing and ensuring the functioning of AI and IoT technologies to secondary school students in grades 1 and 2
• Provide and evidence-based further development of learning materials with the involvement of Teachers
• Building a lab infrastructure for AI and IoT technologies, as well as mobile and virtual Learning materials for use in the classroom
• Collaborating with other learning labs to integrate AI and IoT technologies into their learning materials

Preliminary work

Due to several initiatives coordinated by Michael Felderer and Simon Haller-Seeber, respectively, which are described in the following described below, the Institute of Computer Science also has excellent conditions for the implementation of the for the implementation of the AI and IoT Learning Lab.

IoT Test Learning Lab

In the course of a PhD thesis supervised by Michael Felderer [ICST2020], we are in the process of exploring an IoT Testing Learning Lab to be explored [OS2019]. An illustrative and suitable scenario against this background is the development of a prototype in the context of Smart Mobility. Here, the focus is on testing the behavior of a self-driving vehicle in a circuit with traffic light control. the foreground. The physical circuit consists of lanes and an electronically controlled traffic light. On this circuit is driven by a mobile robotic vehicle controlled by an algorithm (see for example Fig. 1a). The circuit with the self-driving vehicle is also mapped virtually in a digital twin based on the Unity game engine (see Fig. 1b). The test and component control is carried out via a cloud environment, to which the physical and virtual environment are connected. All programming and customization of the underlying code is done in the Python programming language, which is also readily accessible for classroom use. It also provides an interface is provided to control the environment in order to map appropriate test scenarios. In the first training session, test cases will be designed to demonstrate correct driving on the circuit as well as the automatic stopping and continuing on the circuit. The scenario can later be extended as required, for example by adding a second vehicle and an intersection.

Initiatives in the field of Educational Robotics

Five years ago, after organizing the RoboCup Junior Austrian Open 2016 in Innsbruck, it was clear how relatively underdeveloped the Educational Robotics community in Western Austria was. Moreover, it looked like, that very few computer science and physics teachers were using educational robotics in the classroom. In aware of this challenge, we developed complementary approaches and strategies to introduce more educational robotics into schools. Educational Robotics into schools: On the one hand, in direct contact with schools, we regularly offer various courses and workshops specially designed for teachers. and workshops with a focus on educational robotics. This is done in different formats: e.g. as part of the InDay Teachers as well as for different specialized groups: Informatik LehrerInnen Arbeitsgemeinschaft (ARGE), E-Future Day Tirol, Pädagogische Hochschule Tirol (PHT) and also for the Open Source Open School(OSOS) initiative. In these 1.5 to 3 hour courses and workshops teachers get and workshops teachers get ideas and impulses for their lessons. Additionally we address school classes directly: In the Campus Days format, we invite classes from elementary and middle schools in the Tyrol, who visit our department with their teachers for two- to three-hour workshops. On the other hand, there were two courses for student teachers in which these different concepts of educational robotics in a total of nine schools (18 classes in total). In addition, we have further developed tools that allow teachers to more easily introduce their students to robotics. more easily introduce robotics to their students [RiE2020b]. The worked out example in the area of Swarm Robotics shows how complex topics can be easily taught using simple block-type programming. can be taught. With the initiative ROSSINI [RiE2020a] we have implemented strategies to introduce children to robotics. robotics. The ROSSINI robotics workshops for children, are designed not only to inspire STEM, but to improve collaboration, communication and problem solving skills of the participants. participants. ROSSINI is built around five core concepts: Design Thinking, Computational Thinking, Upcycling and Waste management (3Rs: reduce, reuse, recycle), Free Software and Open Hardware and DiY Rapid Prototyping. ROSSINI’s robot components are designed to reduce the cost of a robot with all necessary sensors, actuators including microcontroller is less than 30 Euro.

Referenzen

[OS2019] Thomas Auer, Michael Felderer: LEGO-MINDSTORMS-Lernlabor für Internet of Things Testing. ObjektSPEKTRUM, Online Themen Special IoT und Industrie 4.0, 2019.

[ICST2020] Thomas Auer, Michael Felderer: Towards a Learning Environment for Internet of Things Testing with LEGO MINDSTORMS. ICST Workshops 2020: 457-460.

[RiE2020a] Simon Haller-Seeber, Erwan Renaudo, Philipp Zech, Florian Westreicher, Markus Walzthöni, Cornelia Vidovic, Justus Piater, ROSSINI: RobOt kidS deSIgn thiNkIng. 11th International Conference on Robotics in Education, 2021.

[RiE2020b] Patrick Lamprecht, Simon Haller-Seeber, Justus Piater, A Block–based IDE Extension for the ESP32. 11th International Conference on Robotics in Education, 2021.