Theses

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.

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 project also aims to improve the student experience by enabling direct browser viewing of lecture slides, moving away from traditional PDF downloads. This approach will provide a more seamless and integrated learning experience, with functionalities like slide hiding synchronized with exercise deadlines. The in-browser viewing experience will be interactive, allowing students to engage with the material dynamically. Additional features to enhance user experience include interactive annotations, real-time communication integration on lecture slides, and a dark mode option for PDF viewing in the browser. This project will significantly improve the usability and functionality of the IRIS learning platform, fostering a more structured and paced learning environment.

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 quantitative evaluation will be conducted through a comprehensive assessment of the chatbot’s performance both in real course and experimental settings. The qualitative evaluation will be conducted through a survey of students and instructors and expert interviews. The results of the evaluation will be used to identify the strengths and weaknesses of the chatbot and to provide recommendations for future improvements.

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.

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. This research offers valuable contributions to the field of adaptive learning technologies and the advancement of personalized education.

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 research provides a comprehensive guide for developers and engineers aiming to modernize legacy monolithic applications in the educational technology domain.

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. The project will evaluate different approaches for automated grading, comparing image-based and text-based assessments of UML diagrams.

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. Each iteration will be evaluated for accuracy, efficiency, and educational impact to ensure continuous improvement. By adopting an agile approach, we aim to deliver automated, personalized feedback that enhances student learning and reduces tutors’ workload, creating a more efficient and supportive educational environment.

Artemis needs comprehensive sorting, categorizing, and visualizing of test case failures during grading, hindering instructors’ ability to gain a high-level overview of feedback and understand the cause of errors in student submissions. To ensure a high-level overview, this thesis aims to enhance Artemis by improving the feedback sorting, categorizing, and visualization functionalities.

The methodology involves collecting and sorting various types of errors encountered after running test cases, then categorizing and presenting them visually within the Artemis platform. Presenting this data visually allows for planning strategies to improve the quality of feedback from test cases. Improved feedback quality helps instructors inform students of specific error types, reducing complaints and enabling timely tracking of student progress through a transparent, categorized overview of common errors. The objective is to streamline the error analysis process within Artemis, ensuring its functionality and usability meet the evolving needs of instructors and students.

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. To evaluate the system’s effectiveness, we will conduct a comprehensive user study involving multiple development teams. This study will use both quantitative metrics, such as participation rates and review quality scores, as well as qualitative feedback from user surveys and interviews. The results will provide detailed insights into the impact of gamification on code review practices, demonstrating its potential to enhance software development processes and other collaborative environments.

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. Additionally, we will introduce version management, which will enable users to capture new versions, preview changes, and revert to previous versions. Furthermore, a new structured and readable textual format for diagrams, integrated with VSCode, will facilitate developers in versioning their UML diagrams with Git for seamless documentation integration. These enhancements will improve Apollon’s usability, encouraging its adoption in development workflows.

Abstract TBA

Abstract TBA

Computer science education in schools often fails to engage school students with diverse prior knowledge. Many existing educational tools also lack gender-sensitive design, particularly at the foundational level when girls are making critical future career decisions. This thesis will evaluate digital and unplugged games in computer science education to enhance computational thinking and address adaptive learning needs. The study will identify the strengths and limitations of these tools, focusing on their adaptability and ability to engage young girls. Based on these findings, the research will provide evidence-based recommendations and best practices for integrating gender-sensitive and adaptive strategies into computing education. In addition, the study will include the development and testing of an interactive game with multiple levels of difficulty designed to engage all genders. The results will provide valuable insights for improving computer science education, fostering inclusive learning environments and promoting greater gender balance.