Field-Programmable Gate Arrays (FPGAs) have received significant attention in academic and industrial research due to their reconfigurability, power efficiency, and ability for real-time high-performance computation. The growing demand for FPGA-based systems across various applications has necessitated the development of robust design and verification platforms to support academic and research objectives. In pursuit of this long-term goal, numerous FPGA-based remote laboratory platforms have been proposed, emphasizing didactic needs. However, more attention must be paid to requirement analysis and evaluation criteria specific to FPGA-based remote laboratory platforms. This paper addresses this gap by investigating state-of-the-art remote laboratory platforms alongside a comprehensive requirement analysis. A didactic evaluation has been conducted to assess qualitative and quantitative data to effectively align requirements with outcomes, providing insights into students' perceptions of the platform and its efficacy in meeting learning objectives. Furthermore, the evaluation highlights students' engagement with laboratory tasks across different time intervals throughout the day. However, this requirement analysis and evaluation serve as a foundation for effectively designing an efficient FPGA design and verification platform, fostering enhanced learning experiences in remote laboratory settings.

Generative artificial intelligence (GenAI) offers new possibilities for generating personalized programming exercises, addressing the need for individual practice. However, the task quality along with the student perspective on such generated tasks remains largely unexplored. Therefore, this paper introduces and evaluates a new feature of the so-called Tutor Kai for generating comprehensive programming tasks, including problem descriptions, code skeletons, unit tests, and model solutions. The presented system allows students to freely choose programming concepts and contextual themes for their tasks. To evaluate the system, we conducted a two-phase mixed-methods study comprising (1) an expert rating of 200 automatically generated programming tasks w.r.t. task quality, and (2) a study with 26 computer science students who solved and rated the personalized programming tasks. Results show that experts classified 89.5% of the generated tasks as functional and 92.5% as solvable. However, the system's rate for implementing all requested programming concepts decreased from 94% for single-concept tasks to 40% for tasks addressing three concepts. The student evaluation further revealed high satisfaction with the personalization. Students also reported perceived benefits for learning. The results imply that the new feature has the potential to offer students individual tasks aligned with their context and need for exercise. Tool developers, educators, and, above all, students can benefit from these insights and the system itself.

This paper presents the use of Retrieval Augmented Generation (RAG) to improve the feedback generated by Large Language Models for programming tasks. For this purpose, corresponding lecture recordings were transcribed and made available to the Large Language Model GPT-4 as external knowledge source together with timestamps as metainformation by using RAG. The purpose of this is to prevent hallucinations and to enforce the use of the technical terms and phrases from the lecture. In an exercise platform developed to solve programming problems for an introductory programming lecture, students can request feedback on their solutions generated by GPT-4. For this task GPT-4 receives the students' code solution, the compiler output, the result of unit tests and the relevant passages from the lecture notes available through the use of RAG as additional context. The feedback generated by GPT-4 should guide students to solve problems independently and link to the lecture content, using the time stamps of the transcript as meta-information. In this way, the corresponding lecture videos can be viewed immediately at the corresponding positions. For the evaluation, students worked with the tool in a workshop and decided for each feedback whether it should be extended by RAG or not. First results based on a questionnaire and the collected usage data show that the use of RAG can improve feedback generation and is preferred by students in some situations. Due to the slower speed of feedback generation, the benefits are situation dependent.

This work-in-progress paper presents the current development of a new collaborative remote laboratory platform. The results are intended to serve as a foundation for future research on collaborative work in remote laboratories. Our platform, standing out with its adaptive and collaborative capabilities, integrates a distributed web-application for streamlined management and engagement in diverse remote educational environments.

This study investigates the application of large language models, specifically GPT-4, to enhance programming education. The research outlines the design of a web application that uses GPT-4 to provide feedback on programming tasks, without giving away the solution. A web application for working on programming tasks was developed for the study and evaluated with 51 students over the course of one semester. The results show that most of the feedback generated by GPT-4 effectively addressed code errors. However, challenges with incorrect suggestions and hallucinated issues indicate the need for further improvements.

For a variety of reasons, Augmented reality (AR) is used to support teaching, learning and working processes. On the operational side, considerable potential is seen, e.g. the possibility of training unskilled and non-specialist employees in an action-oriented manner within the work processes. The success of AR technology in learning scenarios depends largely on the didactic designs. The development of these has not yet been researched to any great extent. Herein the LAARA research project investigates the design criteria for learning, informing and acting with augmented reality in the work process. The project examines the technological and ergonomic perspectives on the one hand and the media-didactic and learning-psychological perspectives on the other. The project focuses on an operationally relevant work process in the metal industry, the set-up of a bending machine. The workplace is enhanced by an AR environment and can thus be used as a learning scenario. A transportable model (demonstrator) of the complex bending machine has been created for the project. This demonstrator enables all relevant set-up steps to be carried out and can be used flexibly in various educational institutions. In the study test persons are given learning and work tasks that they carry out on the demonstrator with the help of the AR environment. Following the designed based research approach, qualitative and quantitative research methods are used to determine the criteria that promote and inhibit the use of AR. The research objectives are to identify the central parameters that have an influence on learning, informing and acting in the workplace in the context of a semi-virtual space and to determine criteria for the use of AR in companies, vocational colleges and inter-company organizations that promote learning.

This paper introduces the web application SQheLper, which makes it possible to create SQL statements by assembling blocks. This should reduce the number of syntax errors made by students and thus create more time for the correction of semantic errors. In addition, a dynamic visual representation of the database uploaded by students is displayed. This can be used to browse the database and open individual tables. Depending on the blocks created, a formatted version of the SQL statement is shown. SQL statements can be executed and students receive the result set or an error message as feedback.

In this paper, an innovative environment to foster students' preparation for embedded systems laboratory is presented. A collection of tools is assembled into a virtual-machine-based environment on a USB flash drive to support the students work outside of the laboratory. Thus, students are enabled to develop applications without the corresponding hardware at a place and time of their choice. Targeted hardware platforms range from Atmel microcontrollers to Field Programmable Gate Arrays (FPGAs) and common Android smartphones. The laboratory, developed by the KOMINA project, represents the application area for our approach. It is accompanied by theoretical didactic research which focuses on higher education, too. The proposed approach is discussed in terms of benefits and limitations. An evaluation confirms the students' satisfaction.

In this paper, we describe the results of an expert survey conducted within the project “Competence development with embedded micro- and nanosystems”. This survey was designed in order to refine our previously derived normative competence structure model for developers of embedded micro-and nanosystems towards an empirically refined competence structure model. Today the size of structural parts of nanosystems has scaled down to as few as a couple of molecules. This results in a lot of challenges regarding permanent and transient faults. Therefore bottom-up development techniques for nanostructured systems are now included into our competence model to prepare future developers to the specific challenges of designing at the nano scale. The evaluation of the content validity of the empirically refined competence structure model was accomplished by the presented expert rating.

Teaching the development of embedded micro- and nanosystems needs a well structured foundation. In this paper, we show how we refined our normative competence structure model (NCSM) to an empirical competence structure model (ECSM) by incorporating the results of a survey of German experts in the field of embedded systems. In addition, we introduce a course concept for a new internship which takes the results of the ECSM into account.

In our poster, we present the research project "Competence development with embedded micro- and nanosystems (KOMINA)" promoted by the German Research Foundation and its first step, the development of a normative competence structure model. As a result, we clustered the derived competencies in the following dimensions: (C1) Competencies as preconditions (Basis), (C2) development competencies of embedded micro- and nanosystems (EMNS), (C3) competencies for multi-level development of EMNS and (C4) non-cognitive competencies. While we chose a purely thematic division for braking down C1 and C2 to sub dimensions, we recommend dividing C3 into solution approaches. We exemplify the deduction from a sub dimension to a concrete task of a practical course.