Testing scientific models using a QR model: Application to cellulose biodegradation

The rapidly growing set of scientific publications makes it difficult for researchers to keep track of the progress to-wards adequate mechanistic explanations of phenomena. However, high-level representations can support integrating seemingly different results and ideas presented in the literature. This paper reports on our effort to deploy the qualitative reasoning framework as an instrument towards this end

Requirements and challenges for hybrid intelligence: A case-study in education

The potential for Artificial Intelligence is widely proclaimed. Yet, in everyday educational settings the use of this technology is limited. Particularly, if we consider smart systems that actually interact with learners in a knowledgeable way and as such support the learning process. It illustrates the fact that teaching professionally is a complex challenge that is beyond the capabilities of current autonomous robots. On the other hand, dedicated forms of Artificial Intelligence can be very good at certain things. For example, computers are excellent chess players and automated route planners easily outperform humans. To deploy this potential, experts argue for a hybrid approach in which humans and smart systems collaboratively accomplish goals. How to realize this for education? What does it entail in practice? In this contribution, we investigate the idea of a hybrid approach in secondary education. As a case-study, we focus on learners acquiring systems thinking skills and our recently for this purpose developed pedagogical approach. Particularly, we discuss the kind of Artificial Intelligence that is needed in this situation, as well as which tasks the software can perform well and which tasks are better, or necessarily, left with the teacher

Pair modeling with DynaLearn - Students' attitudes and actual effects

With DynaLearn learners can construct scientific knowledge by manipulating icons and their in-ter-relationships, using a diagrammatic representation. The diagrams represent models that can be simulated, confronting learners with the logical consequences of the knowledge they expressed. Such modeling activities are highly advocated by science educators. Learning from the construction and debugging processes of modeling can be enhanced by collaboration. The modeling elements can serve as anchors for discussing, justifying, and explaining the model. Researchers have suggested various ways of supporting collaboration. In this study we employed Pair Modeling, which is an adaptation of the pair programming technique that is used for enhancing collaborative programming both in the industry and in academia. In this paper we present encouraging results for the use of this collaboration technique based on assignments' scores, observations, and a ques-tionnaire. Students' attitudes were neutral on the average, but the average score of the group that employed Pair Modeling was significantly higher than the average score of the control group that employed unstructured pair collaboration. We discuss the implications of the obtained results and the limitations of the study