Volume 19, Issue 1

Using two case studies from biology, the article demonstrates and analyses how domain-specific self-learning items with variable content can be generated automatically for a blended learning environment. It shows that automated item generation works well even for highly specific technical properties and that a good item quality can be produced. Evaluations are based on sample exercises from two courses in botany and genetics, each with more than 100 participants.

Caused by a deviance of their reward system, autistic people show attention deficits for learning content outside their special fields of interest. This can lead to significant problems, especially in formal learning situations. A promising approach to increase attention is the use of game-based learning concepts. The effect of individual playful aspects could be shown in existing learning systems. However, these do not provide consistent game experiences, which may result in a decreasing motivation for training. Therefore, this paper presents requirements as well as a related game concept to integrate the learning content with a playful narrative in order to promote motivation and attention for socio-emotional training.

The GDPR regulates at present the handling with personal data fundamentally new and thereby opens new leeway. At the same time, it creates great uncertainty among those affected. One example of this is web tracking: It helps designers to improve the utility and usability of their websites based on, in part, extensive (personal) data collection, or enable operators to finance them. Against this background, in this article we first show the practical relevance of web tracking by collecting the web trackers of the 100 most popular pages of each of the 28 EU member states. Building on this, we show which data these trackers collect and analyze their legal bases. Finally, we discuss possible consequences in design and architecture for fulfilling the legally outlined requirements, taking into account a user’s perspective.

The aim of this paper is to suggest a framework for categorizing social robots with respect to four dimensions relevant to an ethical, legal and social evaluation. We argue that by categorizing them thusly, we can circumvent problematic evaluations of social robots that are often based on overly broad and abstract considerations. Instead of questioning, for example, whether social robots are ethically good or bad in general, we instead propose that different configurations of (and combinations thereof) the suggested dimensions entail different paradigmatic challenges with respect to ethical, legal and social issues (ELSI). We therefore encourage practitioners to consider these paradigmatic challenges when designing social robots to find creative design solutions.