TY - CHAP A1 - Fuhrmann, Thomas T1 - Motivation Centered Learning T2 - 2018 IEEE Frontiers in Education Conference (FIE), 3-6 Oct. 2018, San Jose, CA, USA N2 - This Research Work in Progress Paper evaluates students’ motivation sources and shows the high impact that work of professors has on students’ motivation. Common goal of most professors is to help students acquiring knowledge and competencies that are relevant for their further life. From the beginning of Universities’ history, lectures are usually chosen to reach this goal but it is seen in recent years that they are in many cases not the optimal choice. In the last years, many efforts were made to increase students knowledge gain. From learning and motivation psychology research, many details are known how humans remember and transfer knowledge. These research results are used to create new lecture formats to activate students. Research based learning, project based lab courses, problem based learning, feedback systems, flipped classroom, blended learning and gamification in lectures are only some examples for new formats. Common goal of all these new types of learning formats is to increasing students’ motivation and to enhance learning success. In this paper, evaluations using a questionnaire with open questions were done among first year students, bachelor students before graduation and alumni, about their sources of motivation and demotivation. Interesting curricula and lectures with application related topics and possibilities for own work are the main sources of motivation. Enthusiastic professors with high competences and good lecture didactics also contribute to students’ motivation. On the other side, demotivated professors with boring lectures play a much higher role for demotivating students. Therefore, it is necessary to integrate aspects of student motivation into curriculum and lecture design and professors should become aware that they are important role-models for motivating or demotivating students. KW - Computational modeling KW - Electrical engineering KW - Information technology KW - Psychology KW - Social networking (online) KW - Task analysis KW - Urban areas Y1 - 2018 U6 - https://doi.org/10.1109/FIE.2018.8658436 SP - 1 EP - 5 PB - IEEE ER - TY - CHAP A1 - Fuhrmann, Thomas T1 - Course Preparation Time Optimization System for Improved Didactic Outcome T2 - 2022 20th International Conference on Information Technology Based Higher Education and Training (ITHET), 07-09 November 2022, Antalya, Turkey N2 - Due to the various demands for lecturers, there is only a limited time to prepare lectures and lab courses. Therefore, it is necessary to invest the time target-oriented for optimal student learning success. A theoretic model is developed to structure course preparation work regarding scientific content, didactic preparation, and course presentation. Model parameters have to be chosen for each course depending on topic complexity, the lecturer’s prior knowledge, and the already available preparation from the prior semesters. With these parameters, a course preparation model for a complete semester is developed. Analytic models for different optimization strategies are introduced according to the overall goal of the lecturer. Numerical optimization is done to find the appropriate course preparation times to reach an optimal course preparation for high student learning success. It is seen that due to the different optimization strategies, the preparation time results vary and no single truth is given. But this optimization system gives hints on how to invest preparation time target-oriented for high student learning success. KW - Higher education KW - Engineering education KW - Course preparation KW - Curriculum development KW - Optimization Y1 - 2022 SN - 978-1-6654-8908-9 U6 - https://doi.org/10.1109/ITHET56107.2022.10031642 PB - IEEE ER - TY - CHAP A1 - Fuhrmann, Thomas A1 - Niemetz, Michael T1 - Ideas on Digitally Supported Individualization of Teaching and Learning for Evolving Competency Requirements T2 - Ninth International Conference on Higher Education Advances (HEAd 23), València, June 19, 2023 – June 22, 2023 N2 - The world is changing rapidly, mainly due to the digitalization of all areas of living. A huge amount of information is accessible via the Internet, and since it is no longer possible for individual humans to keep track of it, artificial intelligence (AI) is analyzing this data. In this rapidly changing world, students have to be educated for a successful career during their whole working life. These boundary conditions lead to completely new challenges for the education of students that are unprecedented in this form. Digitalization in education can help to cope with these challenges but can only be a means, not a goal. Personal interaction with students remains the most important task in education to address individual weaknesses and further develop strengths and talents. With the increasing amount of openly available information and the consequently increasing diversity of experiences within the group of students, differentiation is advancing to become the key to successful education. Digitization can help with this challenging task and support communication between students and their experienced instructors. But computers cannot replace human interaction and attempts to improve teaching efficiency by replacing this communication with electronic means endangers the learning success for complex concepts. This article analyzes education demands and possibilities for digitally supported teaching and learning. KW - Digitalization KW - higher education KW - student competencies KW - differentiation Y1 - 2023 U6 - https://doi.org/10.4995/HEAd23.2023.16232 SN - 2603-5871 SP - 1399 EP - 1406 PB - Universitat Polit`ecnica de Val`encia ER - TY - CHAP A1 - Fuhrmann, Thomas T1 - Semi-Structured Lab Projects in Communication Engineering Education T2 - 2023 IEEE Global Engineering Education Conference (EDUCON), 01-04 May 2023, Kuwait N2 - It is generally known that project-based learning is a very important part of engineering education to connect theoretical knowledge with practical work. Students learn to apply their knowledge to real-world challenges as it is the case in their later professional life. If students are not used to project work or the scientific topic is new and relatively complex, they may be overwhelmed. The consequence is that students achieve poor results, are frustrated, and therefore learning success is low. Semi-structured projects are introduced that combine the advantages of structured experiments with projects. The project work is structured into several parts with detailed descriptions of the tasks. In the end, students get similar results to doing a free project, but the success rate is higher due to higher guidance. Therefore, these semi-structured projects are seen to be an appropriate method to guide students to learn how to do project work. The feedback from most students is very positive. Some students with no previous lab experience complained about the project work and wished for more guidance to become familiar with lab work. In sum, the student feedback is encouraging to develop semi-structured projects further. KW - Knowledge engineering KW - Task analysis KW - Communication engineering education Y1 - 2023 U6 - https://doi.org/10.1109/EDUCON54358.2023.10125244 SP - 1 EP - 5 PB - IEEE ER - TY - CHAP A1 - Fuhrmann, Thomas A1 - Niemetz, Michael T1 - Analysis and Improvement of Engineering Exams Toward Competence Orientation by Using an AI Chatbot T2 - Towards a Hybrid, Flexible and Socially Engaged Higher Education: Proceedings of the 26th International Conference on Interactive Collaborative Learning (ICL2023), Volume 1 N2 - ChatGPT is currently one of the most advanced general chatbots. This development leads to diverse challenges in higher education, like new forms of teaching and learning, additional exam methods, new possibilities for plagiarism, and many more topics. On the other side with the development of advanced AI tools, pure knowledge will be less and less important, and demands from industry will change toward graduates with higher competencies. Education has therefore to be changed from knowledge-centered toward competence centered. The goal of this article is to use ChatGPT for analyzing and improving the competence orientation of exams in engineering education. The authors use ChatGPT to analyze exams from different engineering subjects to evaluate the performance of this chatbot and draw conclusions about the competence orientation of the tested exams. The obtained information is used to develop ideas for increasing the competence orientation of exams. From this analysis, it is visible that ChatGPT gives good performance mainly where knowledge is tested. It has, however, much more problems with transfer questions or tasks where students need creativity or complex insights for finding new solutions. Based on this result, exams and also lectures can be optimized toward competence orientation. KW - Engineering education KW - Competence orientation KW - Exam analysis KW - AI chatbot KW - ChatGPT Y1 - 2024 SN - 9783031519789 U6 - https://doi.org/10.1007/978-3-031-51979-6_42 SN - 2367-3370 SP - 403 EP - 411 PB - Springer Nature CY - Cham ER - TY - JOUR A1 - Johnson, Ebin M. A1 - Anwer, Shakil A1 - Xu, Yongchao A1 - Fuhrmann, Thomas A1 - Mandl, Roland T1 - Contactless Low-Cost Position Sensor using a D-Shaped Polymer Optical Fiber JF - IEEE Sensors Journal N2 - This article describes a contactless fiber-optic position sensor. It comprises a Polymer Optical Fiber that is grinded to form a D-shaped cross-section with an exposed fiber core. This sensor has two photodiodes at both fiber ends to measure the emitted light intensity. Light is coupled using a red LED at the side face into the exposed core of the fiber at a defined position. The position of the LED at the length of the fiber is measured by calculating the optical power quotient measured by both photodiodes. To test this sensor, Polymer Optical Fibers with different side surface roughness are produced and qualified. Measurements show that the optical power quotient is reproducible and nearly linear over the length of the fiber. It is also seen that the fiber attenuation increases when grinding the fiber side-face with rougher sandpaper. Position measurements show an absolute position error of this sensor in the range of a few millimeters. Microscope images show surface defects along the polished side face of the fiber that are expected to lead to a nonuniform attenuation along the fiber and cause the position errors. Overall, it is proven that this sensor principle works as a contactless low-cost position sensor for short distances with an absolute position standard deviation error lower than 1 mm. KW - Optical fibers KW - Optical fiber sensors KW - Sensors KW - Photodiodes KW - Optical surface waves KW - Surface roughness KW - Rough surfaces KW - Fiber-optic sensor KW - Position measurement KW - Polymer Optical Fiber Y1 - 2024 U6 - https://doi.org/10.1109/JSEN.2023.3348127 PB - IEEE ER - TY - CHAP A1 - Fuhrmann, Thomas T1 - Teaching Digital Electronics Principles by Connecting Diverse Technologies T2 - 2024 IEEE International Conference on Teaching, Assessment and Learning for Engineering (TALE), 09-12 December 2024, Bengaluru, India N2 - A lab course for digital electronics is developed and implemented in the second semester of a bachelor of engineering study program. This lab course runs parallel to a lecture about the basics of digital electronics. The goal of this lab course is that students should acquire the competence to develop digital circuits, no matter which design technique or circuit technology they use. Therefore, students have to learn abstract thinking without sticking to a certain digital design technique or circuit technology. For this reason, digital circuits with identical functions are developed by students, using different digital design techniques. A SPICE simulation software, the Analog Discovery board, VHDL simulation and implementation using an FPGA board are used for the five experiments with increasing complexity that are carried out during this lab course. The last task is a small project where students can carry out own ideas. The lecturer sees that students are very engaged during this lab course and also their feedback is very positive. They answered that the time frame and complexity of the course was appropriate, the usage of the Analog Discovery board was very instructive, and they learned much. In sum, the students are very satisfied with the course. This success is encouraging to retain the overall structure of this lab course and develop the details further. Y1 - 2024 SN - 979-8-3503-7623-4 U6 - https://doi.org/10.1109/TALE62452.2024.10834379 SP - 1 EP - 5 PB - IEEE ER - TY - CHAP A1 - Fuhrmann, Thomas A1 - Niemetz, Michael T1 - Fostering democratic attitude within engineering education T2 - 11th International Conference on Higher Education Advances (HEAd’25) N2 - According to the education ideal advocated by Wilhelm von Humboldt, education should be a holistic process to develop the whole human being and should not be narrowed toward job training. For example, in democratic societies, education should be directed to fostering a democratic mindset of students to strengthen pluralistic democracy. It is always possible to introduce a general course into a study program that deals with democratic education from a theoretical point of view. But by introducing such a course, the time spent on technical subjects will be reduced. The other major disadvantage of such solutions is that democratic education is seen to be separated from technical education. But in the contrary, democracy should be included in the whole life as a general way of thinking and cooperation between individuals. Therefore, ideas and lecture experiences are presented in this article on how democratic education can be integrated into all engineering subjects as an elementary educational design concept. Students should be led toward a general democratic attitude by practical application. To achieve this, democratic learning environments can be established in all types of courses like lectures, seminars, and labs. This includes, for example, the design of exercises, the presentation of the theoretical content, and the interaction between professors and students as well as the interaction between the students themselves as induced by the teaching activities. Also in exams, elements to promote democratic thinking are possible: Exams should be not directed to learning only content but toward understanding, creativity, and problem-solving competence. They should include, where possible, the fact that correct technical solutions are not unique but that a multiverse of solutions exists, with each solution featuring various strengths and weaknesses. This not only sensitizes the students for the need for (e.g. socially or environmentally) responsible technical design choices but also allows them to find optimized solutions for the engineering challenges at hand. In sum, lecturers should be aware that a democratic learning environment is a demanding process, as students must be taken as serious partners in a joint learning process and surprises are to be expected. KW - Engineering Education KW - Democratic Education Y1 - 2025 U6 - https://doi.org/10.4995/HEAd25.2025.20093 SP - 879 EP - 886 PB - Editorial Universitat Politècnica de València (edUPV) CY - Valencia ER -