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Satzung der Studierendenschaft der Universität Koblenz zur Änderung
von Vorschriften der Studierendenschaft der Universität Koblenz
Fünfundzwanzigste Ordnung zur Änderung der Prüfungsordnung
für die Prüfung im Zwei-Fach-Bachelorstudiengang an der Universität Koblenz
Erste Ordnung zur Änderung der Masterprüfungsordnung für
den weiterbildenden Fernstudiengang „Master of Business Administration“
des Fachbereichs 4: Informatik der Universität Koblenz
Erste Ordnung zur Änderung der Masterprüfungsordnung für
den weiterbildenden Fernstudiengang Energiemanagement
des Fachbereichs 3: Mathematik / Naturwissenschaften der
Universität Koblenz
Prüfungsordnung für den Bachelorstudiengang „Angewandte
Naturwissenschaften“ und den Masterstudiengang „Material
Science“ an der Universität Koblenz (Studiengangs-PO Angewandte
Naturwissenschaften / Material Science)
Prüfungsordnung für das Studienmodell uk-Master an der
Universität Koblenz (Studiengangs-PO uk-Master)
Einschreibeordnung der Universität Koblenz
This thesis tackles a common bottleneck in data-science courses: students struggle
to turn a broad interest into a focused, workable project idea. This thesis set out to
design and evaluate a compact assistant—EduIDEAtor—that makes this first mile
simpler and more intentional. The tool uses a text-first interface with plain inputs,
a small set of clearly different directions, and quick, reversible edits so students can
steer ideas without losing momentum. After building and iterating the web appli-
cation, The thesis evaluated how students experienced it and how it compared with
familiar, non-AI brainstorming. The findings are consistent: navigation and input
clarity were strong; students felt more able to generate and shape ideas; overall sat-
isfaction and willingness to continue using the tool were high. Two practical refine-
ments emerged—make back navigation clearly visible and give users finer control
over how broad or specific the suggestions are both achievable without changing
the core design. The contribution is a concrete pattern for first-mile ideation and a
set of actionable guidelines for course-level adoption.
Globally billions of dollars are invested on information systems and technology (IS/IT) to achieve business change. Understanding how value is generated and captured from these investments has been a key theme in information systems (IS) research for over 25 years. However, despite significant theoretical progress, organisations are still failing to achieve the full value of their investments and identifying and realising the benefits of IS/IT-enabled business change remains a challenge for both research and practice.
Our research is concerned with the business change associated with the introduction and use of new forms of enterprise collaboration system (ECS) that incorporate social software functionality (e.g. social profiles, blogs, wikis, activity streams, collaborative tagging etc). ECS represent a significant business investment; however, there remains uncertainty around the benefits and value arising from the introduction of these new types of ECS. Existing research studies on IS/IT benefits are focused primarily on traditional enterprise systems such as ERP systems. This article summarises the existing work that directly, or indirectly addresses IS benefits, to reveal four broad themes (i) evaluations of IS/IT investments (ii) measuring IS success (iii) classifying and measuring IS benefits and (iv) benefits realisation management.
The article concludes with an overview of the research on benefits management conducted in the Center for Enterpise Information Research at the University of Koblenz and the current research project investigating the benefit of enterprise collaboration systems (BECS).
The BECS project investigates the benefits arising from the adoption and use of Enterprise Collaboration Systems (ECS).
ECS are large-scale collaboration technology infrastructures that provide the software functionality to enable workgroups to organise online team meetings, to create and share information, to coordinate workflows and to collaborate on joint projects, regardless of the location and timing of work activities.
When ECS are introduced into organisations there are initial expectations about what can be gained from the system, e.g. improved collaboration, improved communication across silos, etc. Over time, as users gain experience using the system, ideas about what can be achieved change and the ECS become embedded into organisational work practices. However, identifying and understanding the expected benefits of ECS, how they evolve over time, and how they contribute to organisational performance is challenging due to a lack of suitable methods and tools to describe (profile), measure and monitor ECS benefits.
The BECS project addresses this challenge; the primary focus is on identifying, measuring and monitoring the benefits that arise from ECS implementation and use over time. Through the development of in-depth longitudinal case studies of ECS adoption in leading organisations in the DACH region and empirical analyses of collaboration system use, the research:
i) developed practical tools and methods for the measurement of ECS benefits and benefits profiling;
ii) provides greater insights into how benefits management is experienced and constituted in practice; and
iii) developed a novel and integrated framework that assists researchers and practitioners to coordinate their efforts in developing, implementing and evaluating ECS benefits.
The project delivered both practical and theoretical outcomes. The methods and tools developed in the BECS project have been applied in organisations and delivered useful and useable results enabling organisations to understand and monitor the evolving benefits of their ECS. Following the COVID-19 pandemic, this work became of even greater importance as new uses of ECS emerged when organisations adopted large-scale support for hybrid and remote working initiatives.
The research findings also provide key theoretical concepts and analytical methods, including the MoBeC framework, Social Collaboration Analytics and Benefits Scorecards. These provide the foundation for subse-quent research projects to examine transformation to digital work and the development of a new stream of research into trace analysis and collaboration analytics more broadly.
The rapid evolution of wireless communication technologies, particularly the introduction
of Fifth-Generation (5G) networks and the anticipated transition to Sixth-Generation (6G)
systems, ushers in a new era of connectivity, enabling transformative applications across
industrial automation, the Internet of Everything (IoE), and the Industrial Internet of Things
(IIoT). However, the exponential growth in the number of connected devices, stringent reliability
requirements, and increasing security challenges pose significant hurdles for current network
architectures. This dissertation addresses these challenges by proposing innovative frameworks
and mechanisms that enhance reliability, optimize resource utilization, and strengthen security
and trust management in next-generation mobile networks.
The first contribution of this dissertation focuses on reliability enhancements in 5G networks.
While existing mechanisms, such as Dual Connectivity (DC) and Network Function (NF)
redundancy, provide partial solutions, they do not fully resolve application-layer reliability
and dynamic server failover. To bridge this gap, this work introduces the Make-Before-Break-
Reliability (MBBR) and enhanced Make-Before-Break-Reliability (eMBBR) mechanisms. These
frameworks proactively establish redundant communication paths, ensuring seamless failovers
with minimal latency and service disruption. By extending reliability to the application layer
and integrating adaptive path selection and dynamic failover capabilities, these mechanisms
offer robust solutions for latency-sensitive and mission-critical applications.
The second major contribution addresses bandwidth optimization for industrial networks.
The black channel paradigm, widely adopted for industrial safety applications, relies heavily on
cyclic keep-alive messages to detect connection loss, leading to significant signaling overhead.
This dissertation proposes a novel solution leveraging 5G Channel State Information (CSI)
to replace cyclic messaging with real-time connection quality monitoring. By exposing CSI
metrics, such as Signal-to-Noise Ratio (SNR) and Channel Quality Indicator (CQI), to the
application layer, the proposed mechanism reduces bandwidth consumption while maintaining
the safety and reliability requirements of industrial networks.
Addressing the growing complexity of security requirements in IIoT, the third contribution
introduces the AF-based Security Framework (AERO) framework. This framework empowers
application providers to dynamically apply cryptographic mechanisms to the user plane,
overcoming the limitations of legacy protocols and eliminating the need for redundant security
layers. By ensuring backward compatibility and enabling both static and dynamic configuration
of user plane encryption, AERO enhances security while minimizing computational overhead
and reducing transmission delays.
The fourth and final contribution redefines trust management in mobile networks through
the SecUre deleGAtion of tRust (SUGAR) framework. Traditional trust models, which rely
on identity chips for each connected device, are becoming increasingly impractical in the
IoE era, where billions of devices require connectivity. The SUGAR framework introduces a
delegation-based trust model, allowing Parent Devices (PaDs) to delegate trust to multiple
Child Devices (ChDs) securely. This approach eliminates the need for individual identity chips,
significantly reducing costs and enhancing scalability. Integration with System-on-a-Chip
(SoC)-based identity enclaves further strengthens the security of trust credentials.
The findings of this dissertation offer substantial contributions to both academia and
industry. The proposed frameworks effectively address critical gaps in current 5G standards
and provide valuable contributions for developing the 6G framework. By enhancing reliability,
optimizing bandwidth, and redefining security and trust management, this dissertation provides
a comprehensive foundation for the design and deployment of next-generation mobile networks.
Furthermore, the solutions presented are adaptable to a wide range of applications, including
industrial automation, autonomous systems, and smart city infrastructures.
In conclusion, this dissertation represents a significant step toward realizing the full
potential of next-generation mobile networks. By addressing key challenges in reliability,
resource optimization, security, and trust management, the proposed frameworks pave the way
for scalable, secure, and efficient mobile ecosystems that are essential for the dynamic and
interconnected world of the future.
This thesis investigates the potential of LLMs to provide personalized and context aware feedback in data science education. Traditional automated feedback systems often face challenges related to adaptiveness, scalability, and pedagogical alignment. To address these limitations, an experimental study was conducted using a custom-built AI tutor based on GPT-4o, which guided students through six clustering assignments designed around k-means and DBSCAN concepts. Data were collected from pre- and post experiment questionnaires and 516 dialogue exchanges recorded across ten individual tutoring sessions. A mixed-methods approach was adopted. Quantitative analysis compared pre and post-survey results to measure normalized learning gain (g = 0.375), effect size (Cohen’s d = 0.321), and statistical significance (t(9) = 0.811, p > 0.05). Qualitative analysis involved manual coding of AI responses for feedback type, adaptiveness, and student engagement. Results showed that students generally perceived the AI tutor positively, emphasizing its clear explanations, step-by-step guidance, and timely feedback. While moderate conceptual improvement was observed, statistical effects remained small, suggesting that perceived learning gains may exceed measured performance improvements. Conversational analysis revealed that adaptive responses and interactive questioning supported engagement, though occasional inconsistencies and reliance on predefined solutions limited deeper adaptiveness. The study contributes to educational technology research by providing empirical insight into both the capabilities and current constraints of LLM-based tutoring. Although student satisfaction was high, findings highlight the need for more sophisticated scaffolding, enhanced contextual adaptiveness, and hybrid human-AI feedback frameworks. Overall, this research demonstrates the promise of LLMs in delivering scalable, personalized support in data science education, while emphasizing the importance of continued evaluation to ensure pedagogical reliability and meaningful learning outcomes.
This study examines student housing experiences in Koblenz through a mixed-methods approach that integrates surveys, geospatial analysis, and quantitative modeling to explore affordability, accessibility, satisfaction, and equity. By analyzing both objective factors—like rent, distance to campus, and travel times—and subjective measures such as satisfaction and sentiment, it identifies disparities across student groups, especially affecting international students. The findings suggest that housing outcomes stem from both structural conditions and lived experiences, revealing possible biases within the housing system. The study advocates for targeted interventions, including expanding affordable residences, enhancing transport connectivity, and promoting transparency in housing allocation to ensure equitable access in Germany’s higher education context.
Improving patient care is an ongoing process, evolving from early evidence-based practices to modern AI-driven approaches. This thesis explores three key research directions aimed at improving clinical decisionmaking through AI. Adverse events, defined as negative and harmful outcomes that occur during medical care, present major challenges for hospitals. Most data-driven research using electronic health records relies on data from tertiary referral hospitals, but their patient population differs from those in hospitals of medium level of care. The first major contribution of this thesis is a data-driven Trigger Tool for predicting adverse events trained on data from a hospital of medium level of care. This tool uses a concise set of laboratory values measured within the first 24 hours of hospitalization. In addition to models using numerical features, we devised models using dichotomized features that indicate whether a laboratory value falls below or above a reference threshold. Our findings show that models using numerical features achieve high accuracy in predicting acute kidney injury and the COVID-19 associated adverse events in-hospital mortality and transfer to the ICU. Models using dichotomous features performonly slightly worse but offer better interpretability.
The second major contribution is the online-updateable AI model OptAB for selecting optimal antibiotics in sepsis patients. OptAB aims to minimize the sepsis-related organ failure score (SOFA-Score) while accounting for nephrotoxic and hepatotoxic side effects. OptAB relies on a hybrid neural network differential equation algorithm tailored to the special properties of patient data, including irregular measurements, missing values, and time-dependent confounding. Time-dependent confounding describes a dependence between time-varying covariates and treatment decisionsmade by physicians, often leading to biased treatment effect estimates. OptAB generates disease course forecasts for (combinations of ) the antibiotics vancomycin, ceftriaxone, and piperacillin/tazobactam and learns realistic treatment effects on the SOFA-Score and side effect indicative laboratory values. Results indicate that OptAB’s recommendations achieve faster efficacy than the administered antibiotics while reducing side effects.
The third major contribution is DoseAI, an online-updateable AI model that extends OptAB to optimize dosing regimens. DoseAI mitigates time-dependent confounding in dosage selection by minimizing the absolute spearman correlation between predicted and future treatment dosages. It forecasts disease progression under alternative dosing regimens and proposes optimal chemotherapy and radiotherapy dosing regimens for synthetic cancer patients. These regimens effectively reduce the tumor volume while adhering to varying maximum allowed weight loss constraints, used as a measure of toxicity.
Mathematiklehrkräfte sind bisher unzureichend auf die Digitalisierung des Mathematikunterrichts vorbereitet. Daher ist es notwendig bereits im Studium passende Lernangebote zur Entwicklung professioneller Kompetenzen für den Einsatz digitaler Mathematikwerkzeuge zu schaffen. Im Rahmen der vorliegenden Arbeit wird eine fachdidaktische Lehrveranstaltung für angehende Mathematiklehrkräfte der Sekundarstufen
konzipiert, um deren professionellen Kompetenzen mit Blick auf den Einsatz digitaler Mathematikwerkzeuge im Kontext der Leitidee Strukturen und funktionaler Zusammenhang zu fördern. Neben Werkzeugkompetenzen zur Nutzung der digitalen Mathematikwerkzeuge sollen die Kompetenzen zur Planung und Gestaltung von Mathematikunterricht entwickelt werden. Für das Themenfeld funktionale Zusammenhänge sind die beiden digitalen Mathematikwerkzeuge GeoGebra und Tabellenkalkulationsprogramm besonders relevant. Deren Potentiale für das funktionale Denken werden insbesondere in Bezug auf Darstellungsformen und Repräsentationswechsel sowie Lernschwierigkeiten thematisiert. Ergänzend zu den genannten Kompetenzen werden zudem Überzeugungen zum Einsatz digitaler Mathematikwerkzeuge in den Blick genommen. Diese haben einen erheblichen Einfluss darauf, ob die erworbenen Kompetenzen im eigenen Unterricht eingesetzt werden oder nicht.
Zur Ermittlung des Ist-Zustands bei Kompetenzen und Überzeugungen der angehenden
Mathematiklehrkräfte vor dem Besuch der Lehrveranstaltung wurden zwei Erhebungsinstrumente entwickelt und eingesetzt. Neben einem Fragebogen zur Selbsteinschätzung erfolgte die Datenerhebung mittels eines neu entwickelten Kompetenztests
im Sinne eines Leistungstests. Es zeigte sich, dass die professionellen Kompetenzen und Vorerfahrungen bei den Studierenden äußerst heterogen sind.
Um Rückschlüsse auf die Wirksamkeit der Lehrveranstaltung ziehen zu können, wurden die beiden Erhebungsinstrumente im Pre-Post-Design eingesetzt. Die Ergebnisse nach Besuch der Lehrveranstaltung lassen auf positive Effekte schließen. Gleichzeitig wird deutlich, dass eine einzelne Lehrveranstaltungen im Studium nicht ausreicht. Zudem besteht Bedarf an weiterer Forschung in diesem Bereich, insbesondere was die Untersuchung langfristiger Effekte und die Zusammenhänge zwischen den verschiedenen Kompetenzfacetten und Überzeugungen betrifft.