Software Engineering

Competency-based education(CBE) enables flexible, skill-centered learning by allowing students to progress based on mastery rather than fixed schedules. Atlas already utilizes machine learning techniques to support educators; nevertheless, further improvements are required to accelerate adoption and improve quality.

This thesis proposes an interactive AI-powered agent that assists instructors in creating, refining, and maintaining competency networks through natural language. Rather than relying on static, one-shot inputs, the agent engages in a dynamic conversation—asking clarifying questions, gathering relevant context, and presenting interactive proposals. This interaction model is designed to reduce redundant AI calls, avoid naive prompting, and ensure that the resulting competency networks align closely with course objectives.

Doctoral application portals manage admissions and shape candidate experience. Many systems create barriers through complex interfaces and inconsistent design. TUMApply at the Technical University of Munich replaces an earlier system but lacks a clear, inclusive, and consistent interface that supports efficient task completion and equitable participation.

This thesis applies Nielsen’s usability heuristics with gender-sensitive and accessible design practices to identify and address usability barriers and inclusivity weaknesses. The primary contribution is a redesigned interface that demonstrates how doctoral application portals can better serve diverse user needs while maintaining efficiency. This work offers practical insights for building academic platforms that support usability and inclusivity, grounded in systematic analysis, iterative design, and implementation.

This thesis develops an analog story-coding activity using Ozobot for German elementary school education. The activity combines storytelling with hands-on robotics to introduce fundamental programming concepts and computational thinking skills. Pupils guide the robot through stories using color-coded cards, without the need for text- or codeblock-based programming. Through iterative testing, the thesis examines the impact of this approach on pupils’ motivation, the activity’s usability and it’s feasibility in real teaching conditions. The thesis summarizes the findings to provide recommendations for designing story-driven analog robotics activities for elementary schools.

Competency-aware educational recommender systems enable personalized learning, but the absence of standardized benchmark datasets prevents systematic algorithmic comparison and limits progress. This thesis develops a collaborative platform to standardize the collection of ground-truth competency networks across institutions. Contributors map competency relations and link them to learning resources, producing an open benchmark dataset for the performance evaluation of recommenders. The initial release focuses on computer science to seed the dataset and integrates guided onboarding and light gamification to support sustained contributions.

This Master’s thesis presents the design and implementation of a digital story-coding activity for elementary education using Ozobots. It combines narrative elements with block-based programming to introduce core computational thinking concepts in an engaging and accessible way. The study develops a customized Ozoblockly environment in German, reducing technical barriers and preparation effort for teachers. The story-driven approach makes programming tangible and expressive, fostering creativity, motivation, and inclusive participation. User testing in classrooms examines pupils’ engagement, intuitive use of programming concepts, and classroom feasibility, providing formative insights to guide iterative improvements. The results inform the design of low-threshold, inclusive computational thinking materials for early education.

This bachelor’s thesis aims to advance the Ozobot Board Game by transforming an existing prototype of an educational board game into a sustainable and classroom-ready product. The project focuses on developing a durable, engaging, and easy-to-use tool for teaching basic programming concepts. It seeks to enhance the game’s replayability while promoting equal participation and interest in computing education among all children, regardless of gender or cultural background. The design includes differentiated learning tasks, dynamic difficulty adjustment, and iterative refinement through user feedback and classroom testing. The final outcome will be a classroom-ready board game package, including adaptable learning resources and a teacher’s manual, designed for inclusive and flexible use in diverse educational settings.

Competency-based educational recommender systems enable personalized learning, but the absence of standardized benchmark datasets prevents systematic algorithmic comparison. This thesis develops a platform for collaborative competency relationship data collection with four objectives: a generalized database model, authentication systems for credential verification, administrative interfaces for dataset management and export, and production deployment infrastructure. The system enables systematic collection of high-quality competency mapping data for creating reliable benchmarks.

This thesis focuses on transitioning an experimental prototype to a production-ready Open Educational Resources (OER) platform. LEARN-Hub aims to distribute standardized teaching materials for computer science (CS) education. The existing system requires increased maintenance effort and offers limited support for processing teaching materials at scale.

The work migrates the server from Flask to Spring Boot to align the platform with institutional standards. It designs a pipeline to transform unstructured PDFs into structured data for consistent visualization. In addition, the work refines the React client to adhere to established usability heuristics and to ensure coherent presentation of the generated content.