Title: Educational feedback needs during the times of Covid pandemics - given by Gayane Sedrakyan
- Abstract: After some time of full/partial lockdown experiences the effects of limited ways of giving/receiving feedback on learning processes, student motivation and outcomes are suggestive for rethinking traditional feedback practices, not only in the context of Covid pandemics but also towards post-pandemics digital/hybrid education in which current interactional instruments become the new norm. What is the type of feedback and digital channels/features that worked better for giving/receiving feedback with digital experiences during the covid pandemics, to which extent the (adjusted) formats used by teachers proved effective among students, will the feedback adjustments continue even after the lockdown ends when social distancing will eventually pass, became an important topic for research. In addition to these questions, in this research, we also attempt to provide a preliminary assessment of feedback needs for skill-based courses that in addition to theory expose students to practical knowledge (e.g. pair-wise work, learn-by-doing such as engineering, surgery, etc.). This is achieved by surveying teachers and students from highschool, bachelor, master university program in the Netherlands and Germany.
- Bio: I am an Assistant Professor at Utwente teaching BI and low-code BIT and IEM bachelor/master courses. I obtained my PhD in Business Economics from KU Leuven focusing on information systems, model-driven engineering, learning process analytics and process-oriented feedback, the results of which were nominated for a university wide educational award for innovative educational feedback. My research interests include code generation (soft/web, backend/frontend/UI) to support simulation/testability, semantic/syntactic validation of business requirements represented as models (UML/XML/text) and process- / behavior- analytics based feedback automation. I am also interested in expanding my domain towards a broader context of data analytics; explainable AI; visual analytics, recommender system dashboards, NLP; machine learning.
Title: Automated Legal Assessment of Standard Form Contracts - given by Daniel Braun
- Abstract: As consumers, we face so-called standard form contracts, i.e. contracts that are drafted unilaterally by one party, like T&C of webshops, on a daily basis. Although they are often used to hide disadvantageous or even illegal clauses from consumers, we regularly agree to them, without even reading. In my talk, I will show how NLP technologies can be applied to automate the legal assessment of clauses in standard form contracts in order to support the cause of consumer protection.
- Bio: Daniel received his PhD from the Technical University of Munich, where he worked as the chair of Software Engineering for Business Information Systems from 2016 to 2021. He holds a Master's degree in computing science from the University of Aberdeen and a Bachelor's degree in computer science with a minor in computational linguistics from Saarland University. His research focuses on Legal Tech and the application of Natural Language Processing (NLP) and Artificial Intelligence (AI) to problems from the legal domain. More broadly, he is also interested in the general application of NLP and Natural Language Generation (NLG) to solve problems in organizations.