Previously known as Department of Intelligent Computer Systems /library/oar/handle/123456789/8368 2025-11-01T05:00:01Z 2025-11-01T05:00:01Z /library/oar/handle/123456789/140713 2025-10-29T14:14:52Z 2025-01-01T00:00:00Z

Title: Blockchain technologies and their impact on game-based education and learning assessment Abstract: Traditional education systems are characterised by inefficiencies, outdated certification methods and susceptibility to fraud, undermining confidence in academic qualifications. The lack of global standardisation also hinders the cross-border recognition of qualifications, limiting student mobility and employability. Blockchain technology offers a potentially transformative solution to these challenges, but misconceptions and technical and privacy concerns are slowing its adoption in education. This dissertation explores the potential of blockchain to transform educational processes, particularly in game-based learning (GBL) and game-based assessment (GBA), while addressing the challenges. Using a qualitative mixed methods approach - including an initial focus group, prototype testing, surveys and problem-centred expert interviews - several innovative frameworks were validated. A blockchain-based framework for GBL and GBA, which integrates smart contracts to securely track performance and automate grading, was proposed and validated through the 'Gallery Defender' game and quiz prototypes. These prototypes demonstrated improved learner engagement and transparent grading processes, with two technical approaches explored for integrating blockchain tokens and smart contracts. To address privacy concerns, this dissertation presents education-specific solutions such as encrypted non-fungible tokens (NFTs), zero-knowledge proofs (ZKPs), and anchoring off-chain data using one-way hashes to ensure GDPR compliance. In addition, a self-sovereign identity (SSI) eID wallet framework is proposed to securely store educational credentials and integrate ZKP technologies. This wallet acts as a hub and verification system, connecting different methods of credential storage and validation provided by governmental and non-governmental entities, in line with frameworks such as the European Digital Identity (EUDI) wallet. These contributions indicate the potential of blockchain to significantly improve efficiency, transparency and trust in education, while addressing critical issues of privacy and sustainability, pointing the way to a more secure education ecosystem. Description: Ph.D.(Melit.)

2025-01-01T00:00:00Z Gromann, Dagmar Apostol, Elena-Simona Chiarcos, Christian Cremaschi, Marco Gracia, Jorge Gkirtzou, Katerina Liebeskind, Chaya Mockiene, Liudmila Rosner, Michael Schuurman, Ineke Sérasset, Gilles Silvano, Purificação Spahiu, Blerina Truică, Ciprian-Octavian Utka, Andrius Valunaite Oleskeviciene, Giedre /library/oar/handle/123456789/139126 2025-09-22T07:43:48Z 2024-01-01T00:00:00Z

Title: Multilinguality and LLOD : a survey across linguistic description levels Authors: Gromann, Dagmar; Apostol, Elena-Simona; Chiarcos, Christian; Cremaschi, Marco; Gracia, Jorge; Gkirtzou, Katerina; Liebeskind, Chaya; Mockiene, Liudmila; Rosner, Michael; Schuurman, Ineke; Sérasset, Gilles; Silvano, Purificação; Spahiu, Blerina; Truică, Ciprian-Octavian; Utka, Andrius; Valunaite Oleskeviciene, Giedre Abstract: Limited accessibility to language resources and technologies represents a challenge for the analysis, preservation, and documentation of natural languages other than English. Linguistic Linked (Open) Data (LLOD) holds the promise to ease the creation, linking, and reuse of multilingual linguistic data across distributed and heterogeneous resources. However, individual language resources and technologies accommodate or target different linguistic description levels, e.g., morphology, syntax, phonology, and pragmatics. In this comprehensive survey, the state-of-the-art of multilinguality and LLOD is being represented with a particular focus on linguistic description levels, identifying open challenges and gaps as well as proposing an ideal ecosystem for multilingual LLOD across description levels. This survey seeks to contribute an introductory text for newcomers to the field of multilingual LLOD, uncover gaps and challenges to be tackled by the LLOD community in reference to linguistic description levels, and present a solid basis for a future best practice of multilingual LLOD across description levels.

2024-01-01T00:00:00Z /library/oar/handle/123456789/138342 2025-08-25T10:45:48Z 2025-01-01T00:00:00Z

Title: Human pose estimation in powerlifting Abstract: Amidst the rising popularity of powerlifting, the need for advanced techniques to maintain proper form and prevent injuries has become increasingly crucial. This study explores the burgeoning field of powerlifting, leveraging AI‐driven solutions to enhance movement analysis and support lifters in optimising their technique. Our approach focused on curating a dataset to train a powerlifting‐specific Human Pose Estimation (HPE) model, ensuring diverse representations of squats, bench presses, and deadlifts. The model was trained using the You Only Look Once (YOLO) framework, leveraging manually labelled keypoints as ground truth data for both training and evaluation. The model demonstrated strong performance across multiple evaluation metrics, confirming its effectiveness in powerlifting analysis. It achieved a Percentage of Correct Parts (PCP) of 89.79%, a Percentage of Detected Joints (PDJ) of 97.16%, and a Percentage of Correct Keypoints (PCK) of 91.31%, highlighting its high precision in keypoint detection. Additionally, the Mean Per Joint Position Error (MPJPE) of 14.25 pixels and Mean Absolute Joint Angle Error (MAJAE) of 6.16 degrees reflect its accuracy in localising joints and estimating movement angles. To ensure AI‐driven analysis translates effectively into practical use, a UI was developed, allowing users to upload media, visualise detected keypoints, and receive structured feedback. The trained YOLO model was integrated into a process that includes additional calculations for perspective classification and form analysis, achieving 92.76% and 90.06% accuracy, respectively. These insights were processed using the ChatGPT API to generate contextually relevant and actionable feedback. Designed as a proof of concept, the UI demonstrates the potential of AI‐powered form feedback in real‐world applications. Extensive testing validated its usability and robustness, ensuring smooth functionality to support lifters in refining their technique. The system establishes a strong foundation for advancing AI‐driven sports analysis, enabling refinements in real‐time assessment, dataset expansion, and model accuracy. By bridging the gap between AI technology and practical athletics, this research highlights its transformative potential in performance analysis. Aligned with global health and fitness goals, it underscores AI’s role in fostering inclusivity, promoting safer training methodologies, and setting new standards in the application of AI for enhancing sports performance and safety. Description: M.Sc.(Melit.)

2025-01-01T00:00:00Z /library/oar/handle/123456789/137982 2025-08-06T08:10:08Z 2024-01-01T00:00:00Z

Title: Digital autonomous virtual educator DAVE Abstract: Every student deserves an individualised learning experience that is tailored to their specific needs in order to help them reach their full potential. The current educational system generalises students, making them vulnerable to falling behind. Despite efforts to provide more individualised attention, teachers simply have too much on their plates. This thesis develops the Digital Autonomous Virtual Educator (DAVE) project which provides a personalised digital tutor to each student. The DAVE project utilises AI technologies to analyse student academic data, adapt academic material according to individual needs, and provide clear and detailed explanations in real-time. Through DAVE, round-the-clock personalised learning assistance is provided to students, ensuring accuracy and safety in the process. Through the integration of advanced automated prompt engineering, responses are personalised, focusing on the specific needs of each student. Additionally, by developing a novel, multi-stage verification approach named FORT Verification, harmful and incorrect content is filtered out before it is sent to users, allowing them to take confidence in the support they are receiving. The DAVE project demonstrates its efficacy through real-world testing evaluating the system's performance against commercial AI systems. Students utilising DAVE achieved improved results on mathematics worksheets by a margin of 32% while also reporting higher user satisfaction levels of 12%. Students emphasised DAVE's helpfulness, clear explanations, and personalised support. These results demonstrate the feasibility, benefits, and potential of effectively integrating AI into educational systems. Description: M.Sc. ICT(Melit.)

2024-01-01T00:00:00Z