2021, Pande, Charuta, Witschel, Hans Friedrich, Martin, Andreas, Montecchiari, Devid, Martin, Andreas, Hinkelmann, Knut, Fill, Hans-Georg, Gerber, Aurona, Lenat, Dough, Stolle, Reinhard, Harmelen, Frank van

We present an approach to improve individual and self-regulated learning in group assignments. We focus on supporting individual reflection by providing feedback through a conversational system. Our approach leverages machine learning techniques to recognize concepts in student utterances and combines them with knowledge representation to infer the student’s understanding of an assignment’s cognitive requirements. The conversational agent conducts end-to-end conversations with the students and prompts them to reflect and improve their understanding of an assignment. The conversational agent not only triggers reflection but also encourages explanations for partial solutions.

2019, Laurenzi, Emanuele, Hinkelmann, Knut, Jüngling, Stephan, Montecchiari, Devid, Pande, Charuta, Martin, Andreas, Martin, Andreas, Hinkelmann, Knut, Gerber, Aurona, Lenat, Doug, van Harmelen, Frank, Clark, Peter

The practice of business process modeling not only requires modeling expertise but also significant domain expertise. Bringing the latter into an early stage of modeling contributes to design models that appropriately capture an underlying reality. For this, modeling experts and domain experts need to intensively cooperate, especially when the former are not experienced within the domain they are modeling. This results in a time-consuming and demanding engineering effort. To address this challenge, we propose a process modeling approach that assists domain experts in the creation and adaptation of process models. To get an appropriate assistance, the approach is driven by semantic patterns and learning. Semantic patterns are domain-specific and consist of process model fragments (or end-to-end process models), which are continuously learned from feedback from domain as well as process modeling experts. This enables to incorporate good practices of process modeling into the semantic patterns. To this end, both machine-learning and knowledge engineering techniques are employed, which allow the semantic patterns to adapt over time and thus to keep up with the evolution of process modeling in the different business domains.