Learning is at the heart of every progress the human species makes. It is most effective when it considers who we are as individuals, what learning approach we prefer and what we already know to begin with. In the digital age, we strive to capture such information in the form of a digital representation -- the so-called learner model --, to tailor learning-related systems to this information and build upon it to create more personalised learning experiences. Over recent years, the proliferation of diverse models across various educational applications and disciplines has made it challenging to access targeted research.

In this survey, we aim to address this gap, reviewing the latest advances in learner modelling and conducting a comprehensive analysis of the existing approaches, focusing on developments from 2014 to 2023. With the help of a systematic literature review, we want to provide designers and developers of learner models with a structured overview and simplified entrance into the topic and the field of learner models. We investigate the question: What do learner models look like and how are they filled, kept up-to-date, and used?

To this end, we analyse and classify existing approaches. Our findings provide a comprehensive and structured overview of the field of learner modelling, allowing researchers to navigate and understand the diverse approaches more easily and providing developers of learner models or adaptive systems with a practical tool to access relevant information according to their needs.

Trees for real-time media are typically created using procedural algorithms and then baked to a polygon format, requiring large amounts of memory. We propose a novel procedural system and model for generating and rendering realistic trees and similar vegetation specifically tailored to run in real-time on GPUs. By using GPU work graphs with mesh nodes, we render gigabytes-worth of tree geometry from kilobytes of generation code every frame exclusively on the GPU. Contrary to prior work, our method combines instant in-engine artist authoring, continuous frame-specific level of detail and tessellation, highly detailed animation, and seasonal details like blossoms, fruits, and snow. Generating the unique tree geometries of our teaser test scene and rendering them to the G-buffer takes 3.13 ms on an AMD Radeon RX 7900 XTX.

The textual realm and the geographic/spatial realm intersect when we use human language to talk about geographic space. Various terms have been used to talk about this intersection (“geoparsing”, “georeferencing”, “toponym resolution”, “spatial grounding” etc.) and related applications such as geographic information retrieval. In this keynote, I will review some things that the community has accomplished since 2003, what occupies people’s minds at the moment, and I will raise a few research questions that would be interesting to answer, or that would unlock the potential for new kinds of applications. I conclude with some personal conjectures about how one version of the future might look like.

Hintergrund: Bis dato befassen sich die meisten Forschungsvorhaben im Bereich der KB mit Wirkungsforschung, wobei Nachweise von Wirkungen durch KB aufgrund von komplexen und auch multikausalen Wirkzusammenhängen nicht vorbehaltlos zu erbringen sind. Forschung im Bereich der Kulturellen Bildung (KB), die dazu beiträgt, den Begriff KB, die zugrundeliegende Theorie, den Praxisbereich und auch die Angebote von KB zu verstehen und einzuordnen, ist hingegen eher rar. Die Potenziale der KB unterstreichen jedoch den Bedarf der Weiterentwicklung und theoretisch-methodischen Fundierung der KB. Es wird also Forschung gebraucht, die einen Beitrag zur evidenzbasierten Qualitätsverbesserung und -sicherung leistet. Mit der vorliegenden Erhebung bei wichtigen Akteur:innen der KB soll das Verständnis von KB in der Europäischen Metropolregion Nürnberg (EMN) abgebildet werden.

Methodik: Es wurde eine quantitative Querschnittsstudie in Form einer Online-Befragung durchgeführt, und es bestand die Möglichkeit der Beantwortung der Fragen via paper+pencil. Es wurden vorrangig quantitative Daten erhoben, in geringem Ausmaß auch qualitative Daten in Form von Antwortmöglichkeiten unter Sonstiges und Freitextfragen. Eine Orientierung bezüglich zentraler inhaltlicher Aussagen und daraus entwickelter Kategorien gab eine qualitative Vorstudie (siehe: Hamani et al. 2023).

Ergebnisse: Die Stichprobe bestand aus 73 Akteur:innen im Bereich der KB in der EMN. Die Ergebnisse wurden deskriptivstatistisch ausgewertet. Die qualitativen Antworten in den Feldern Sonstiges und zu den Freitextfragen wurden in induktiv gebildete Kategorien eingeordnet. Auch wurden explorative Analysen durchgeführt.

Diskussion und Fazit: Die vielschichtigen Erkenntnisse aus der Befragung unterstreichen die Komplexität von KB und verdichten sich bezüglich mancher Handlungs- und Inhaltsdimensionen. Hierfür hat die Studie Kategorisierungsmöglichkeiten entwickelt und durch handelnde Akteur:innen gewichten lassen.

A number of machine learning process models (SEMMA, KDD, CRISP-DM, CRISP-ML, Data-to-Value1 etc.) have been recently proposed to facilitate the development of machine learning models in their organizational context. While the existing proposals vary with respect to complexity and suitability for particular tasks, it would be desirable to have software tools that embody or support these process models, and make it easier for project teams to capture, share among team members and stakeholders and preserve the relevant project information pertaining to the various process stages. In particular, recorded past statistics may be applied to predict the duration of stages or the overall project effort.

Presently, to the best of our knowledge, no requirement analysis exists that stipulates the detailed needs. To this end, we present a first collection and analysis of a requirements document for the software tooling for machine learning process models. We describe the functional and non-functional requirements of a Computer-Aided Machine Learning Modeling (CAMLM) tool, the soft-computing world’s counter-part to a CASE (Computer Aided Software Engineering) tool.

Various software cover sub-areas such as team management and communication management (Confluence, Jira, Slack, Zoom...) or project management (CRISP-DM, Scrum, Kanban-Board...) or data and information management (model management [Weber, Christian; Hirmer, Pascal; Reimann, Peter; Schwarz, Holger (2019): A New Process Model for the Comprehensive Management of Machine Learning Models. In: Proceedings of the 21st International Conference on Enterprise Information Systems: SCITEPRESS - Science and Technology Publications.] ). What is not available to our knowledge, however, is software that covers the entire sub-areas and the entire life cycle of machine learning projects in detail.

Learners in higher education tend to become an increasingly heterogeneous group. Paying proper attention to individual differences is a challenge that may be leveraged by individualized automated recommendations of learning elements. This presupposes some knowledge of the learners’ profiles which can be captured in so-called learner models. Yet, so far, there is no comprehensive overview of existing standards and their contribution related to learner models. This paper presents the results of a systematic literature research devoted to norms and standards in the area of learner models. As it turns out, 16 norms or standards have some relationship to learner models, 3 of them present their versions of a learner model. None of the standards and norms offers a comprehensive learner model, but in their entirety these models provide hints on reasonable contents and structure of learner models.