Adaptive Rückmeldungen im intelligenten Tutorensystem LARGO

The Intelligent Tutoring System LARGO is designed to help law students learn argumentation skills. The approach implemented in LARGO uses transcripts of oral arguments as learning resources: Students annotate them and create graphical representations of the argument flow. The system encourages students to reflect upon arguments proposed by the attorneys and helps students detect possible weaknesses in their analysis of the dispute. Technically, graph grammar and collaborative filtering algorithms are employed to detect these weaknesses. This article describes how “usage contexts” are determined and used to create adaptive feedback in LARGO. On the basis of a controlled study with the system that took place with law students at the University of Pittsburgh, we discuss to what extent the automatically calculated usage contexts can predict student’s learning gains

Towards an approach for analysing external representations created during sensemaking using generative grammar

During sensemaking, users often create external representations to help them make sense of what they know, and what they need to know. In doing so, they necessarily adopt or construct some form of representational language using the tools at hand. By describing such languages implicit in representations we believe that we are better able to describe and differentiate what users do and better able to describe and differentiate interfaces that might support them. Drawing on approaches to the analysis of language, and in particular, Mann and Thompson’s Rhetorical Structure Theory, we analyse the representations that users create to expose their underlying ‘visual grammar’. We do this in the context of a user study involving evidential reasoning. Participants were asked to address an adapted version of IEEE VAST 2011 mini challenge 3 (interpret a potential terrorist plot implicit in a set of news reports). We show how our approach enables the unpacking of the heterogeneous and embedded nature of user-generated representations and allows us to show how visual grammars evolve and become more complex over time in response to evolving sensemaking needs

Multiple headspace extraction for gas detection in ionic liquids

In this study multiple headspace extraction was used for the first time to measure the saturation concentration of carbon monoxide and oxygen in various ionic liquids (ILs). Many processes in ILs involve the reaction of gases so that the reactant solubility is not a mere characteristical parameter, but understanding the solubility of gases in ILs is required for assessing the feasibility of possible applications. Multiple headspace extraction has proofed to be a powerful tool to obtain solubilities in good accordance with literature data. The measured saturation concentration for carbon monoxide and oxygen in ILs based on rarely researched tetracyanoborates and other anions was in the range of 1.5-6.5 mmol/L. The great advantage of multiple headspace extraction is that it is a nonexpensive method that can be realised in most analytical laboratories by combination of a simple gas chromatograph and an eligible headspace injector

Development of a pure oxygen fuel cell system for an autonomous underwater vehicle with focus on fuel starvation due to inert gas accumulation on the anode

In this paper a LT-PEM fuel cell stack is used in an autonomous underwater vehicle (AUV) as a range-extender to increase its mission duration by continuously supplying the AUV with electricity. A battery buffers the difference between generated power from the fuel cell and consumed power of the AUV motor and payload. For fuel cell operation in an AUV different challenges arise linked to the surrounding sea water. These conditions lead to the design of a closed fuel cell system which will be outlined. Additionally measurements concerning a purge free dead-ended anode operation of the fuel cell stack will be presented