Research Field: Education Technologies

In this thesis, the goal is to enhance the contextual awareness of a GPT-based educational chatbot, named IRIS, on the Artemis Learning Platform by incorporating lecture content. To do this, the lecture slides should be embedded into a vector database, and the chatbot should be able to retrieve the most relevant slides based on the user’s query in order to provide the most relevant answer. Artemis is open source and available on https://github.

The challenges encountered by students during the completion of exercises necessitate the implementation of a proactive assistance mechanism within Artemis. This could potentially be achieved through the integration of generative AI technologies such as ChatGPT. The objective of this thesis is to augment Artemis with the capability to provide automatic and proactive assistance to students when they encounter difficulties. The effectiveness and impact of this approach on the learning experience will be evaluated through a comprehensive assessment.

The thesis aims to enhance the instructor interface and student experience in the IRIS learning platform. The project will introduce a thumbnail grid for instructors to manage slides intuitively, ensuring persistent configurations even when slides are updated. The system will adapt to changes, such as the addition of new slides, by automatically adjusting the selection and maintaining the overall structure. Advanced features like automatic slide number extraction and adaptive reference management will be implemented to maintain continuity when slides are updated.

The goal of this thesis is to evaluate the effectiveness of IRIS, a GPT-based chatbot for higher education. The chatbot is integrated into the Artemis learning platform and is designed to provide assistance to students when they encounter difficulties. In addition, the chatbot is capable of answering questions related to the course content. Instructors can benefit from IRIS through assistance in exercise generation. In this thesis, the effectiveness of IRIS will be evaluated through a combination of quantitative and qualitative methods.

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 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 thesis explores the use of multimodal large language models (LLMs) to improve the grading of UML diagrams and provide assistance to students in software engineering courses. The research focuses on two main areas: developing and enhancing an automated grading system for UML diagrams and creating a preliminary feedback system to support students while they create and refine their diagrams. By implementing these features within an existing learning management system, this thesis aims to address challenges related to grading consistency, tutor workload, and timely student support in large-scale university courses.

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.

Abstract TBA

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