Research Field: Human Computer Interaction

This master’s thesis aims to enhance educational outcomes on Artemis by reducing tutor workload and improving the student learning experience. We’ll build on Athena’s preliminary work that deployed Large Language Models (LLMs) like GPT-4 to provide automatic feedback on text-based exercises. The focus is to employ more advanced LLM techniques such as Retrieval Augmented Generation (RAG) and fine-tuning Llama 3 with historical feedback data. The project will proceed in two phases: refining the feedback system for more context-sensitive, automated responses, and then evaluating and comparing the system’s accuracy, efficiency, and educational impact.

Communication on Artemis already provides context-sensitive interactions between students and instructors. In this thesis, communication should be enhanced based on new functionality. The user experience should be further enhanced so that it makes more fun and is comparable to platforms such as Slack and Discord. This can include the enhancements of the new mobile Artemis apps for iOS and Android. Artemis is open source and available on https://github.com/ls1intum/Artemis

Artemis supports local VCS and CI using Docker containers. This provides many benefits like better performance, easier setup and reduce maintenance effort. However, the implementation is still preliminary and some features of programming exercises are missing. In this thesis, you extend the capabilities and implement a more flexible solution for CI taking scalability with build agents and customizable build plans into account. Artemis is open source and available on https://github.com/ls1intum/Artemis

Artemis supports any programming language for programming exercises. Currently, it includes templates for many popular languages, however templates for a couple of languages is still missing. In this thesis, new templates for programming exercises will be developed and tested with the Bitbucket/Bamboo and Gitlab/Jenkins and LocalVCS/CI. In addition, static code analysis should be integrated into more programming exercise templates. Artemis is open source and available on https://github.com/ls1intum/Artemis

This thesis contributes to the enhancement of the Team Allocator for Software Engineering courses, an open-source tool for forming student teams. The focus is on optimizing data import and export functionalities, refining the matching algorithm, improving the user interface and experience, and implementing a serverside component to enable synchronous multi-user collaboration. The methodology employed is agile development with 2-3 week product increments, allowing for regular testing and release cycles. The main contribution of this work lies in providing significant upgrades to the TEASE project, enhancing its usability and efficiency in project-based educational settings.

This thesis proposal addresses communication challenges in Artemis. The aim is to enhance Artemis by introducing a refined communication module. The identified problems include duplication in communication sections, ab- sence of document-sharing, and limitations in threaded conversations. Moti- vated by the need for a robust communication platform, this thesis proposal suggests improvements guided by Nielsen’s Heuristics. Key objectives involve a unified messaging system, enhanced message thread functionality, collabo- rative document sharing, and improved UI design.

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.

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.