Conference Agenda

The COVID-19 pandemic and the ongoing energy crisis in Germany have challenged educational institutions in providing hands-on training for computer science and engineering students. In response, the University of Applied Sciences Mittelhessen developed the "MICRO" platform, enabling remote access to embedded systems experiments. MICRO allows students to interact with microcontroller-based assemblies containing sensors and actuators through a web interface, offering real-time feedback and hands-on experience even in a remote setting.

This study aims to enhance the MICRO platform by integrating a low-cost multi-function instrument that extends the system's interactivity. The instrument enables students to remotely generate and analyze analog and digital signals while interacting with embedded system components, such as sensors and actuators. This integration allows for real-time signal processing, improving the overall educational experience by closely replicating physical lab environments.

The architecture of the system is designed for flexibility and scalability. The existing database has been expanded to store granular details of each assembly, from individual pins to entire component connections. A RESTful API developed with a Spring back end in Kotlin supports dynamic retrieval of data and communication between the database and the emulation service. The Python-based emulation service processes user-triggered events from the web-based interface, allowing real-time control over the experimental setups through WebSocket connections. This modular approach ensures that the system can easily adapt to future changes, additional components, and more stations without extensive reconfiguration.

The study's outcomes include improved usability and interactivity within the MICRO platform. The enhanced database provides a comprehensive view of the system, while the emulation service enables students to manipulate components like potentiometers and servo motors remotely. The service also supports communication protocols such as I2C, SPI, CAN, and UART, offering flexibility in experiment design and execution. Anticipated future developments include adding more specialized database entities, creating templates for easier assembly creation, and improving the emulation service to handle a broader range of functionalities.

The integration of the low-cost instrument significantly improves students' ability to perform meaningful remote experiments, offering an immersive experience that parallels traditional lab settings. MICRO's unique capability to emulate hardware interactions in real-time sets it apart from other remote lab platforms. Future improvements, such as real-time data visualization and expanded protocol support, could further enhance the educational experience. Overall, these enhancements position MICRO as a scalable, flexible, and valuable tool for remote learning in embedded systems education.

The integration of Large Language Models (LLMs) holds significant promise for enhancing programming education by providing personalized, immediate feedback and fostering student engagement through adaptive learning. Existing research demonstrates that LLMs can offer meaningful learning support when designed with pedagogical considerations. However, challenges such as hallucinations in feedback and learners' negative attitudes towards automated instructional solutions hinder broader adoption in educational contexts. Many existing solutions fail to promote critical thinking or address diverse learner needs.

This paper proposes design of an interactive story-based environment for teaching programming languages, utilizing LLMs and automated assignment grading through a plug-in for Visual Studio Code (VSC). The goal is to provide contextual, pedagogically relevant tasks and feedback to students, employing Socratic questioning to encourage active participation and critical thinking.

We adapt an existing VSC plug-in framework to support PHP and other common languages, designing middleware that enriches student prompts and redirects them to a custom-tuned curriculum-driven Swedish LLM. This architecture integrates meta-prompts based on pedagogical strategies into LLM interactions, employing a Socratic dialogue approach rather than providing direct answers. Anticipated outcomes include increased student engagement through storyline-based tasks and personalized feedback within the VSC environment, alongside better alignment of LLM interactions with pedagogical objectives.

By presenting the underlying architecture of the prototype, we contribute to the use of generative AI in software engineering education. Our work highlights the potential of AI-powered tools in education to improve learning while addressing ethical considerations and ensuring need for thoughtful implementation to avoid amplifying biases or diminishing the role of teachers. Further studies are recommended to evaluate the impact of LLM interactions on student learning outcomes and to explore adaptability in real-life educational simulations.

Many people are getting interested in bowling, but they tend to stop playing after a few times. One of the most prominent reasons is that they do not have the op-portunity to deepen in their bowling technique and practice accordingly. Thus, they do not feel any improvement in their technique of bowling, so they are disappointed, and they are dropping out. STAYinBowling is a project funded with support from the European Commission, created to help bowling athletes improve and stay engaged with the sport. An online questionnaire was used, answered by both bowling athletes and coaches, to identify their needs and specify the areas that some digital tools might be useful. Based on the participants’ questionnaires a Massive Open Online Course was created. Moreover, an internet of things toolbox was implemented, that consists of two applications for tracking the movement of the hand and the steps during an athlete’s throw. Finally, unified bowlers’ metrics information is supported by a dashboard presenting gathered data by the applications of the IoT toolbox. The purpose of the study is to present the process of collecting the needs, designing and creating the digital tools, as well as to present some of the feedback we got from the experts on a formative evaluation.

CONTEXT

In recent years, addressing the issue of harassment in sports, particularly for children, has gained increased attention. Research shows that children, especially in competitive environments like sports, are vulnerable to different forms of harassment, which can severely impact their well-being, and the incident often goes unreported due to fear or lack of communication channels (Fortier, Parent, & Lessard, 2020).

PURPOSE OR GOAL

This work is presented as an output of HALT4Kidds (Halting Harassment and Abuse in Sports using Learning Technologies for Kids) European Erasmus+ Sport project (https://halt4kids.phed.auth.gr/). HALT4Kids project aims to create digital tools (namely mobile apps, serious games and digital stories) for self-paced personalised education and support of kids in a direct and transparent manner. The digital tools have been designed and developed in a co-creation manner by engaging main stakeholders like Physical Education experts, psychologists, computer designers and engineers.

This paper will present an online and mobile based alert ecosystem developed in the context of HALT4Kids project for Child Safety in Sports.

(NOTE: please find more in the abstract submission file)

The integration of Large Language Models (LLMs) into education, particularly in software engineering and IT, presents opportunities and challenges. While LLMs support problem-solving and code generation following the specifications, their effectiveness often depends on students' ability to formulate precise and effective prompts. To address this, frameworks known as meta-prompts have been proposed. However, the impact of specific frameworks on students' prompt-writing skills and learning outcomes remains underexplored.

This study examines two didactic frameworks—Iterative Feedback and Reflection (IFR) and Adaptive Learning Progression (ALP)—to assess their effectiveness in enhancing prompt-writing skills and learning engagement. We propose complementary metrics within the Overall Prompting Effectiveness (OPE) framework, defined through three key dimensions: Adaptability, Relevance, and Efficiency. These dimensions encapsulate essential components for effective interaction with LLMs in educational contexts.

The design of controlled experiment involves IT-engineering students divided into two groups, each using one of the two different didactical meta-prompt-enhanced frameworks. The IFR group engages in iterative cycles of prompt refinement and self-reflection, while the ALP group utilizes adaptive meta-prompts that dynamically adjust task complexity based on performance. Data collection focuses on OPE-aligned metrics, including the number of prompt iterations, time efficiency, response alignment, and learning progress self-rating, allowing for a comparative analysis of the frameworks' impacts on learning outcomes.

Our work establishes and evaluates these metrics, contributing to research in LLM-assisted learning. It addresses gaps in prompt engineering by showing how IFR and ALP frameworks can be utilized to enhance skill development and offers guidance on integrating LLMs into educational contexts for better interactive learning.