Driven by technological advancements in generative AI and the shortage of data professionals in the European labour market, a growing interest in data science education has led to the development of numerous data science curricula. However, a standardized competency framework for data scientists has not yet been established. Moreover, data scientists have shifted from a generalist approach to focusing on specialised roles within the data ecosystem. As a result, data science curricula have become more specialised, often including a comprehensive introductory phase followed by in-depth studies in specific areas. However, many students struggle to combine the diverse competencies and knowledge elements a data science degree teaches. To address this challenge, this research project focuses on developing a data science framework that identifies interdependencies between competencies and knowledge elements, enabling students to choose personalized learning paths based on their individual goals and prior knowledge. This paper introduces a competency network to create personalized learning paths for an introductory data science course. It will be based on professionally logical interdependencies, which will be evaluated and optimised through the analysis of expert interviews. The goal is to positively impact students' self-efficacy, motivation, and learning outcomes by providing a structured and adaptable learning experience.

Academic libraries are increasingly challenged by the need to efficiently manage and analyse vast collections of data and knowledge. The divers formats and organisation methods of these collections, ranging from traditional print media to digital archives and multimedia assets, can hinder researchers’ ability to easily access and retrieve relevant information. This paper introduces an ontology-driven knowledge base to address this issue by enabling the efficient access to knowledge in the application domain and enhancing the semantic search capabilities in the field of Digital Humanities. Our approach focuses on the development of an ontology-drive knowledge base for semantic search in academic libraries by the example of the library of the Folkwang University of Arts that captures the knowledge concepts present in the library’s archival collections. The resulting ontology framework provides a structured representation of domain knowledge, facilitating the integration of diverse data sources, including structured, semi-structured, and unstructured data from the application domain into a triple store knowledge base. By leveraging SPARQL queries generated from Large Language Model (LLM) prompts, we aim to facilitate more intuitive and effective knowledge retrieval. This approach allows users to express their information needs in a more natural and flexible way, leading to more accurate and relevant search results. We evaluate the proposed ontology-driven knowledge base in terms of its integrity, consistency, flexibility, relevance, and scalability. Our evaluation methodology includes a combination of verification and validation techniques, including automated reasoners and query results based on competence questions. Our findings demonstrate the potential of ontology engineering to enhance complex information retrieval in academic libraries. However, we also identify limitations related to processing speed for complex queries and the quality of search results. This research contributes to the field of computational archival science by providing a novel approach to semantic search in academic libraries. By enabling more precise and efficient access to knowledge, our ontology-driven knowledge base has the potential to enrich the academic and Digital Humanities landscape, empowering researchers to delve deeper into the vast resources available within these institutions.

The project Knowledge Graphs for competency-based Education (KG4CBE) conducts educational data science research to establish competency-based instruction in higher-education programs. In this paper, we propose the design of a knowledge graph to examine the impact of instructional design on student-teacher interaction to facilitate complex learning. For this purpose, the knowledge graph will incorporate the components of an online introductory data science course, including educational materials and learning tasks created with the Four Component Instructional Design (4C/ID) model. Furthermore, the knowledge graph will incorporate data recording the behaviors, interactions and assessments of participating students. To study the competency-based instruction process, the proposed knowledge graph must be scalable to the big data quantities common in educational settings. Therefore, the knowledge graph will be deployed as a tool with accompanying routines to acquire, simulate and load educational data. Furthermore, this tool will provide methods to interact with and visualize the stored information. As future research, we aim to evaluate the proposed knowledge graph in a large-scale educational design research study, to examine the impact of monitoring, forecasting and recommendations in complex learning settings.