Research Field: Software Engineering

The number of computer science students is rising significantly each semester. To meet the demand for prompt and effective feedback, particularly in programming tasks, it is essential to enhance Athena, a platform for (semi-)automated text and programming assessments, to facilitate a higher volume of (semi-)automated feedback. This thesis aims to develop and integrate two new modules into Athena, evaluating them based on resource consumption, including computational capacity and memory usage, and the relevance of their feedback to specific programming tasks.

The rapid evolution of information and communication technologies led to widespread adoption of E-learning systems, particularly during the pandemic when traditional in-person education was not possible. This shift underlined the importance of diverse learning rhythms and paces among students, a challenge particularly in larger classes. To address this, Artemis, an open-source interactive learning platform, introduced learning paths tailored to individual learner goals and capacities. However, despite the functionality of learning paths, Artemis currently lacks an intuitive user interface, hindering the adoption and usability of the feature.

This master’s thesis aims to refine an LLM-based feedback system for program- ming assignments in the Artemis Learning Management System. Despite previous advancements, challenges remain in providing accurate, adaptive, and scalable feedback. The proposed enhancements include the integration of model vector databases, specialized models like Code Llama 2, and an agentic-based approach to address issues of language compatibility and feedback precision. The objective is to reduce manual workload for educators and provide immediate, tailored feed- back to students, thereby enhancing the educational experience and efficiency in learning programming skills.

The emergence of Kubernetes has transformed the practices surrounding the deployment and management of containerized applications by introducing advanced automation, scalability, and operational ease. This thesis focuses on enhancing the Kubernetes deployment at the Technical University of Munich. We identify key issues within the current deployment architecture, including the lack of persistent storage in the Kubernetes cluster, making it unsuitable for stateful applications like Artemis, the cumbersome quality assurance workflow for Artemis, and lacking access control hindering educational use of the Kubernetes cluster.

Artemis is an online learning platform that offers students and instructors a wide range of interactive learning experiences. However, the platform contains design inconsistencies as well as confusing and complex layout elements in the exam mode, course overview and tutorial groups from the student’s point of view. As a result, the complexity and distractiveness of the layouts hamper the user ex- perience, making it difficult for students to navigate and utilize the platform ef- fectively.

Artemis is a learning management platform that offers students different exercise types as well as lecture materials. To help students navigate the exercises and lectures Artemis offers learning paths and makes suggestions about the order of the resources. This thesis aims to optimize the learning path recommendations within Artemis by integrating repeated testing and improving the accuracy and transparency of progress metrics. The expected outcome is a more efficient and engaging learning experience for students, with personalized suggestions that adapt to their learning pace and needs.

The interactive learning platform Artemis offers students a unique opportunity to learn and train software engineering skills online. Especially for advanced courses and topics, students need to set up a local integrated development environment (IDE) to work on their exercises which can be a daunting task. The main objectives of the thesis include the integration of the customized online IDE Theia within Artemis and the setup of a performant execution environment on Kubernetes.

Personalized learning experiences are pivotal in enhancing student engagement and academic success. This thesis investigates the development of dynamic learner profiles within Artemis, an open-source Learning Management System (LMS). By leveraging data analytics and machine learning techniques, we aim to generate comprehensive learner profiles that capture individual learning behaviors, preferences, and progress. These profiles will enable tailored educational experiences, providing insights for both educators and learners. The study covers the methodology for data collection and analysis, the design and implementation of the profiling system, and the impact of personalized learning on student outcomes.

In the rapidly evolving landscape of educational technology, scalable and maintainable software architectures are crucial for the success of learning management systems (LMS). This thesis explores the transformation of Artemis, an open-source LMS, from a monolithic architecture into a modular system using Java Modules. By modularizing Artemis, we aim to enhance the system’s scalability, maintainability, and development efficiency. The thesis delves into the challenges of decomposing a large monolithic codebase, the strategies employed for effective module separation, and the benefits realized post-modularization.

This Master’s thesis aims to enhance the educational platform Artemis by implementing automatic formative feedback for students and improving semi-automatic assessment for tutors. Building on the foundational work of Athena and CoFee, which use NLP and LLMs for text-based feedback, this project focuses on refining these techniques for greater accuracy, reliability, and consistency. The initial phase will prioritize developing immediate feedback mechanisms to support students before deadlines. Following this, we will explore advanced LLM techniques like RAG, CoT prompting, self-consistency, and fine-tuning models like Llama 3 and GPT-4 with historical feedback data.

In this thesis, we develop a gamification-based system to enhance the code review process by increasing participation and improving code quality. By integrating gamification elements such as points, levels, badges, and leaderboards into existing code review platforms, we aim to create a more engaging and motivating environment for developers. Our system provides real-time feedback through a user-friendly web interface, and seamlessly integrates with popular development tools to support continuous workflows. The primary contribution is a versatile framework adaptable to various code review systems, fostering a culture of continuous improvement and collaboration among developers.

In modern development workflows, the seamless integration of UML diagrams into documentation, issues, and pull requests on Git hosting services is crucial for clarity and collaboration. We can significantly enhance Apollon, a web-based UML editor, to meet these needs. This project aims to implement efficient embedding capabilities, allowing developers to embed their UML diagrams with live updates in issues, pull requests, and more. When users make changes to diagrams in Apollon, the system will automatically reflect these changes in their embedded versions, eliminating the need for manual updates.

Abstract TBA

Abstract TBA Artemis is open source and available on https://github.com/ls1intum/Artemis