Chair of Application Systems and E-Business

Chair of Application Systems and E-Business

About us

Prof. Dr. Matthias Schumann

The Chair of Application Systems and E-Business is concerned with the development of modern business information systems, and with their implementation and operation. The benefits connected to IT-based solutions are of special interest.

The core questions are how new algorithms, new technical possibilities, changed processes, changed integration or a new organisational setup can contribute towards improved solutions when developing company or intercompany application systems, how they can be further automated departing from the current situation, or how entirely new solutions or business models can be developed.

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Contributions at EDULEARN17

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The Chair of Application Systems and E-Business is represented by three contributions at the EDULEARN17. Jan Moritz Anke is chair of the “sustainability education” session. He will present the paper „E-learning modules supporting education for sustainable development – state of the art and future research directions”. In this contribution, the state of the art of e-learning usage in the Education for sustainable development is evaluated. The paper "micro and mobile learning in enterprises – what are benefits and challenges of these learning concpets?" is presented by Jasmin Decker. In this paper, the results of two interview studies are used to examine benefits and challenges of these learning concepts. The paper "Design criteria of video elements and their effects on simulation based competence measurement" is presented by Janne Kleinhans. In this contribution, consequences from video design on item difficulty and test motivation are investigated.

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Contribution at the “European Conference on Social Media” (ECSM)

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The Chair of Application Systems and E-Business is represented by a contribution at the “European Conference on Social Media”. Aaron Mengelkamp will present the paper Evaluating Machine Learning Algorithms for Sentiment Classification of Tweets in Credit Assessment”. In this contribution, steps for data preprocessing and machine learning algorithms for automated sentiment analysis of textual data from Twitter in the domain of corporate credit risk assessment are evaluated.

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