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A written exam in measurement technologies usually includes problems for operational amplifier circuits, error propagation, signal analysis and analog to digital conversion. During corona, teaching changed from presence lectures to remote lectures and examination formats changed, too. Instead of a written exam, student project exams were proposed. The projects included a computer aided temperature measurement with operational amplifier circuits, a calibration procedure, error propagation and a written report. The student project exams were successful and the students’ feedback was so positive that the student project exams are going to be continued.
In Tampere UAS the physics and mathematics education will be organized in a new way. The new model increases flexibility for students to choose a learning method suitable for them. The new model also offers some strong support for those students that are at the greatest risk to underperform or drop out their studies. The flexibility includes a possibility for an asynchronous online implementation in every physics and mathematics course. The stronger support means some extra workshop lessons for those who are in danger of dropping out. These workshop lessons concentrate on the very basics of the subject matter.
This paper delves into the impact of artificial intelligence (AI) on learning and teaching engineering physics, focusing on the use of AI chatbots. The study explores various issues encountered by educators when students utilize AI to solve physics problems, highlighting potential pitfalls and opportunities. Through testing AI chatbots' capabilities in physics learning, the paper evaluates their effectiveness in providing accurate and insightful responses. Results indicate that while AI, especially ChatGPT 4, shows promise in aiding students' understanding of physics concepts, there are limitations and challenges to consider, such as over-reliance on technology and the potential for AI to produce misleading solutions. The implications and discussions underscore the need for a balanced approach in integrating AI into education, emphasizing authentic understanding over merely obtaining correct answers. Using AI in creating teaching materials is not evaluated here.
This paper describes the transformation of engineering physics education through activating teaching methods for better learning outcomes and the redesign of learning spaces to support these pedagogical approaches. The transformation of a traditional physics laboratory into a versatile group work and multipurpose space known as the “STEM-Center,” at Tampere University of Applied Sciences is presented. The transformation was inspired by active pedagogical methods such as "Peer Instruction," "PDEODE," and the "SCALE-UP" method and its associated spaces. After the transformation, the space remains suitable for physics laboratory work. Across all courses, it encourages students to be active participants rather than passive listeners, which in turn improves learning outcomes. Most students express high satisfaction with the pedagogical methods and the learning environment as well as a preference for these engaging and activating methods over traditional lecture-based teaching, highlighting an enhanced learning experience and a stronger sense of ownership over their learning.
The Quantum Mechanics Venue is a learning teaching laboratory for high school students at TU Dortmund University. In this context the lab is continuously used and developed by university students. The lab itself focusses on conducting key experiments in quantum mechanics and the visualization of quantum mechanical effects. By exploiting the contradiction between classical expectation and experimental observation, cognitive dissonances are stimulated and later resolved, which leads to an increased learning outcome. Conducting existing experiments is, however, in some cases hindered by a complicated alignment, which does not aid the quantum mechanical understanding. The desired learning outcome can thus not be achieved in all cases.
In order to address these challenges and to allow for a future remote use of the laboratory, three pilot experiments were transformed into cross reality labs. This allows to decouple the students’ learning experience from the location of the lab. This paper provides an overview over the digitized experiments and gives an outlook on the future of the learning teaching laboratory.
The lecture Basic Principles in Physics is a compulsory subject in the education of primary school teachers at TU Dortmund University and actively attended by 100 to 150 students. Previously the lecture was held as a classic lecture utilizing mainly power point slides and experimental demonstrations. For the winter term 23/24 the lecture format was shifted towards a more interactive format, aiming at specific physics problems to be solved as part of the lecture. In order to free up the necessary time in the lecture itself , the conveyance of knowledge was shifted to the students’ preparation for the individual lectures, by providing texts and videos, which were aided by a short quiz. The lectures as well as the preparation were accessed over the entire semester. This paper provides an overview over the results obtained from the assessments and the final exams, as well as the lessons learned.
Lithography is the workhorse for the global semiconductor industry. At the Applied Physics department of our institute, we have developed course-based projects in which we train physics students lithography skills that are valuable for a career in Europe’s growing semiconductor industry. In this article, I will present the lithography facility at our institute, where students develop lithography processes for the fabrication of microstructures. I will show two examples of lithography projects that provided students the opportunity to grow towards competent, skillful lithographers. I will stress that universities of applied sciences in Europe need microtechnology facilities and competence centres that are embedded in Europe’s semiconductor ecosystem to power the workforce for its industry growth.
Physics lectures for engineering students are often given in a canonical order and using a traditional lecture format which might not always be the best choice. In this article we present a restructured curriculum that focuses on the understanding of oscillations and waves and enriches lectures with hands-on experiments and tutorials.
Research on the teaching and learning of physics has identified specific conceptual and reasoning difficulties that often prevent students from developing a functional understanding of various topics taught in introductory physics courses. There is evidence that instructional materials that take into account such difficulties and prompt students to critically assess their own understanding can improve student learning. In this paper Tutorials in Introductory Physics are introduced, a set of materials intended to supplement the lecture, textbook, and laboratory of standard introductory physics courses for students in physics, engineering, and other fields. The Tutorials in Introductory Physics were originally developed by Lillian McDermott and the Physics Education Group at the University of Washington. They have been translated into other languages, including Spanish, Greek and German. In addition, they have served (and continue to serve) as a model for the development of instructional materials for other STEM subjects, such as electric circuits and engineering mechanics. We discuss various aspects of incorporating the Tutorials into a typical course and possible issues associated with their implementation.
For many years, a team of physicists at the HTWG Konstanz has been shifting the focus of physics education from teaching “content” to teaching “methods”. An overview is given on the derived state. Several questions are addressed, such as: What are the advantages? Are there constraints and limits? How can a shift be achieved? What are student reactions and how does other teaching staff deal with the new situation.
We present how a teaching unit can be designed to teach the scientific method.
To understand how scientific progress happens, it is necessary to understand the fundamental process by which science generates new knowledge. Teaching the process from observation, hypothesis, prediction, experiment/thought experiment, testing and possibly revising the hypothesis to a theory poses several challenges for the teacher. We will show how we let our students carry out this process themselves in a teaching unit of 1.5 hours.
The benefits of activating teaching methods are promoted by many colleagues and have been proven by numerous studies. Nevertheless, experienced instructors often do not switch to those methods in their teaching, and first-time lecturers start with traditional instead of activating methods. This article is aimed at (future) educators who are thinking about implementing such methods, as well as experienced ones and educational support staff who wish to provide support for their colleagues. This report is based on personal experience, (free-response) evaluations, standardized tests, and literature. It shows, which factors supported the introduction of and transformation to activating teaching methods, which made it difficult, and which would have been helpful. It is demonstrated why a changeover is worthwhile for physics educators despite possible risks and setbacks.
Herein we outline the design and construction of an instructional laboratory demonstration of the conservation of linear momentum, by way of the Impulse-Momentum Theorem, utilizing small, commercially available accelerometers attached to microcontrollers. The set-up is equipped with small, OLED screens which are able to display real-time acceleration graphs. Furthermore, a template for an experiment involving this set-up is provided. We also explore various data analysis options for different levels of student sophistication.
Electricity and Magnetism is a 1st-year course for about 250 students in the initial stages of the BSc in Applied Physics at TU Delft. For this subject, we created an open, interactive textbook consisting of short videos with descriptive texts and python calculations as illustrations. In this article, we describe how we arrived at this textbook, what its content is and how we now use it in our teaching.
Manufacturing industries require the efficient and voluminous production of high-quality finished goods. In the context of Industry 4.0, visual anomaly detection poses an optimistic solution for automatically controlled product quality with high precision. In general, automation based on computer vision is a promising solution to prevent bottlenecks at the product quality checkpoint. We considered recent advancements in machine learning to improve visual defect localization, but challenges persist in obtaining a balanced feature set and database of the wide variety of defects occurring in the production line. Hence, this paper proposes a defect localizing autoencoder with unsupervised class selection by clustering with k-means the features extracted from a pretrained VGG16 network. Moreover, the selected classes of defects are augmented with natural wild textures to simulate artificial defects. The study demonstrates the effectiveness of the defect localizing autoencoder with unsupervised class selection for improving defect detection in manufacturing industries. The proposed methodology shows promising results with precise and accurate localization of quality defects on melamine-faced boards for the furniture industry. Incorporating artificial defects into the training data shows significant potential for practical implementation in real-world quality control scenarios.
The scheduling of production resources (such as associating jobs to machines) plays a vital role for the manufacturing industry not only for saving energy, but also for increasing the overall efficiency. Among the different job scheduling problems, the Job Shop Scheduling Problem (JSSP) is addressed in this work. JSSP falls into the category of NP-hard Combinatorial Optimization Problem (COP), in which solving the problem through exhaustive search becomes unfeasible. Simple heuristics such as First-In, First-Out, Largest Processing Time First and metaheuristics such as taboo search are often adopted to solve the problem by truncating the search space. The viability of the methods becomes inefficient for large problem sizes as it is either far from the optimum or time consuming. In recent years, the research towards using Deep Reinforcement Learning (DRL) to solve COPs has gained interest and has shown promising results in terms of solution quality and computational efficiency. In this work, we provide an novel approach to solve the JSSP examining the objectives generalization and solution effectiveness using DRL. In particular, we employ the Proximal Policy Optimization (PPO) algorithm that adopts the policy-gradient paradigm that is found to perform well in the constrained dispatching of jobs. We incorporated a new method called Order Swapping Mechanism (OSM) in the environment to achieve better generalized learning of the problem. The performance of the presented approach is analyzed in depth by using a set of available benchmark instances and comparing our results with the work of other groups.
Randomization is currently a widely used approach in Sim2Real transfer for data-driven learning algorithms in robotics. Still, most Sim2Real studies report results for a specific randomization technique and often on a highly customized robotic system, making it difficult to evaluate different randomization approaches systematically. To address this problem, we define an easy-to-reproduce experimental setup for a robotic reach-and-balance manipulator task, which can serve as a benchmark for comparison. We compare four randomization strategies with three randomized parameters both in simulation and on a real robot. Our results show that more randomization helps in Sim2Real transfer, yet it can also harm the ability of the algorithm to find a good policy in simulation. Fully randomized simulations and fine-tuning show differentiated results and translate better to the real robot than the other approaches tested
The Proceedings of the 12th PTEE conference 2024 in Rosenheim present 15 articles from a wide range of actual topics in physics teaching – from artificial intelligence, innovative teaching and examination methods, lab experiments, tutorials to thoughts on what would be desirable to know, when you start teaching (activating) physics.
Labels inform smart home users about the privacy of devices before purchase and during use. Yet, current privacy labels fail to fully reflect the impact of advanced device configuration options like sensor state control. Based on the successful implementation of related privacy and security labels, we designed extended static and interactive labels that reflect sensor states and device connectivity. We first did expert interviews (N = 10) that informed the final label design. Second, we ran an online survey (N = 160) to assess the interpretation and usability of the novel interactive privacy label. Lastly, we conducted a second survey (N = 120) to investigate how well our interactive labels educate users about sensor configuration. We found that most participants successfully used the interactive label and retrieved sensor information more efficiently and correctly. We discuss our findings in the context of a potential shift in label use toward control and use-case-based interaction.