The main goal of this study was to investigate if a computational analyses of text data from the National Student Survey (NSS) can add value to the existing, manual analysis. The results showed the computational analysis of the texts from the open questions of the NSS contain information which enriches the results of standard quantitative analysis of the NSS.
A common strategy to assign keywords to documents is to select the most appropriate words from the document text. One of the most important criteria for a word to be selected as keyword is its relevance for the text. The tf.idf score of a term is a widely used relevance measure. While easy to compute and giving quite satisfactory results, this measure does not take (semantic) relations between words into account. In this paper we study some alternative relevance measures that do use relations between words. They are computed by defining co-occurrence distributions for words and comparing these distributions with the document and the corpus distribution. We then evaluate keyword extraction algorithms defined by selecting different relevance measures. For two corpora of abstracts with manually assigned keywords, we compare manually extracted keywords with different automatically extracted ones. The results show that using word co-occurrence information can improve precision and recall over tf.idf.
This Professional Doctorate (PD) project explores the intersection of artistic research, digital heritage, and interactive media, focusing on the reimagining of medieval Persian bestiaries through high dark fantasy and game-making. The research investigates how the process of creation with interactive 3D media can function as a memory practice. At its core, the project treats bestiaries—pre-modern collections of real and imaginary classifications of the world—as a window into West and Central Asian flora, fauna, and the landscape of memory, serving as both repositories of knowledge and imaginative, cosmological accounts of the more-than-human world. As tools for exploring non-human pre-modern agency, bestiaries offer a medium of speculative storytelling, and explicate the unstable nature of memory in diasporic contexts. By integrating these themes into an interactive digital world, the research develops new methodologies for artistic research, treating world-building as a technique of attunement to heritage. Using a practice-based approach, the project aligns with MERIAN’s emphasis on "research in the wild," where artistic and scientific inquiries merge in experimental ways. It engages with hard-core game mechanics, mythopoetic decompressed environmental storytelling, and hand-crafted detailed intentional world-building to offer new ways of interacting with the past that challenges nostalgia and monumentalization. How can a cultural practice do justice to other, more experimental forms of remembering and encountering cultural pasts, particularly those that embrace the interconnections between human and non-human entities? Specifically, how can artistic practice, through the medium of a virtual, bestiary-inspired dark fantasy interactive media, allow for new modes of remembering that resist idealized and monumentalized histories? What forms of inquiry can emerge when technology (3D media, open-world interactive digital media) becomes a tool of attention and a site of experimental attunement to cosmological heritage?
Organisations are increasingly embedding Artificial Intelligence (AI) techniques and tools in their processes. Typical examples are generative AI for images, videos, text, and classification tasks commonly used, for example, in medical applications and industry. One danger of the proliferation of AI systems is the focus on the performance of AI models, neglecting important aspects such as fairness and sustainability. For example, an organisation might be tempted to use a model with better global performance, even if it works poorly for specific vulnerable groups. The same logic can be applied to high-performance models that require a significant amount of energy for training and usage. At the same time, many organisations recognise the need for responsible AI development that balances performance with fairness and sustainability. This KIEM project proposal aims to develop a tool that can be employed by organizations that develop and implement AI systems and aim to do so more responsibly. Through visual aiding and data visualisation, the tool facilitates making these trade-offs. By showing what these values mean in practice, which choices could be made and highlighting the relationship with performance, we aspire to educate users on how the use of different metrics impacts the decisions made by the model and its wider consequences, such as energy consumption or fairness-related harms. This tool is meant to facilitate conversation between developers, product owners and project leaders to assist them in making their choices more explicit and responsible.
