Publikationen

Understanding the inner working of machine-learning models has become a crucial point of discussion in fairness and reliability of artificial intelligence (AI). In this perspective, we reveal insights from recently published scientific works on explainable AI (XAI) within the biomedical sciences. Specifically, we speculate that the COVID-19 pandemic is associated with the rate of publications in the field. Current research efforts seem to be directed more toward explaining black-box machine-learning models than designing novel interpretable architecture. Notably, an inflection period in the publication rate was observed in October 2020, when the quantity of XAI research in biomedical sciences surged upward significantly.While a universally accepted definition of explainability is unlikely, ongoing research efforts are pushing the biomedical field toward improving the robustness and reliability of applied machine learning, which we consider a positive trend.

The MOVING platform enables its users to improve their information literacy by training how to exploit data and text mining methods in their daily research tasks. In this paper, we show how it can support researchers in various tasks, and we introduce its main features, such as text and video retrieval and processing, advanced visualizations, and the technologies to assist the learning process.

Information literacy, the access to knowledge and use of it are becoming a precondition for individuals to actively take part in social,economic, cultural and political life. Information literacy must be considered as a fundamental competency like the ability to read, write and calculate. Therefore, we are working on automatic learning guidance with respect to three modules of the information literacy curriculum developed by the EU (DigComp 2.1 Framework). In prior work, we havelaid out the essential research questions from a technical side. In this work, we follow-up by specifying the concept to micro learning, and micro learning content units. This means, that the overall intervention that we design is concretized to: The widget is initialized by assessing the learners competence with the help of a knowledge test. This is the basis for recommending suitable micro learning content, adapted to the identified competence level. After the learner has read/worked through the content, the widget asks a reflective question to the learner. The goal of the reflective question is to deepen the learning. In this paper we present the concept of the widget and its integration in a search platform.

Learning dashboards (LD) are commonly applied for monitoring and visual analysis of learning activities. The main purpose of LDs is to increase awareness, to support self assessment and reflection and, when used in collaborative learning platforms (CLP), to improve the collaboration among learners. Collaborative learning platforms serve astools to bring learners together, who share the same interests and ideas and are willing to work and learn together – a process which, ideally, leads to effective knowledge building. However, there are collaborationand communications factors which affect the effectiveness of knowledge creation – human, social and motivational factors, design issues, technical conditions, and others. In this paper we introduce a learning dashboard – the Visualizer – that serves the purpose of (statistically) analyzing andexploring the behaviour of communities and users. Visualizer allows a learner to become aware of other learners with similar characteristics and also to draw comparisons with individuals having similar learninggoals. It also helps a teacher become aware of how individuals working in the groups (learning communities) interact with one another and across groups.