Witschel, Hans Friedrich

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Witschel
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Hans Friedrich
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Witschel, Hans Friedrich

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Now showing 1 - 10 of 50
  • Publication
    Student performance prediction model based on course description and student similarity
    (Springer, 2024) Mäder, David; Spahic, Maja; Witschel, Hans Friedrich; Almeida, João Paulo A.; Di Ciccio, Claudio; Kalloniatis, Christos
    04B - Beitrag Konferenzschrift
  • Publication
    Visualisierung von Mustern für hybrides Lernen und Reasoning mit menschlicher Beteiligung
    (Springer, 2023) Witschel, Hans Friedrich; Pande, Charuta; Martin, Andreas; Laurenzi, Emanuele; Hinkelmann, Knut; Dornberger, Rolf
    04A - Beitrag Sammelband
  • Publication
    Ein dialogbasiertes Tutorsystem für projektbasiertes Lernen in der Wirtschaftsinformatikausbildung
    (Springer, 2023) Witschel, Hans Friedrich; Diwanji, Prajakta; Hinkelmann, Knut; Dornberger, Rolf
    04A - Beitrag Sammelband
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    Publication
    Uncovering cross-platform spreading patterns of fake news about COVID-19
    (2023) Schiesser, Lukas; Witschel, Hans Friedrich; de la Harpe, Andre; Hinkelmann, Knut; Gerber, Aurona
    The spreading of fake news or misinformation on social media is a serious threat to modern societies, making more and more people susceptible to being unfairly influenced in their decision-making, be it in elections or other democratic processes. We contribute to the body of work in the area of fake news detection by studying cross-platform, multivariate spreading patterns of fake news on Covid-19-related topics – where existing studies have focused strongly on single platforms and/or on single metrics or indicators. Our findings show that there are several attributes that are specific to the cross-platform spreading process that become important predictors of fake news: there is e.g. a clear tendency that fake news travels faster from one platform to the other than real news. Meanwhile, although we have compiled a cross-platform corpus of fake and real news that future research may build on, data availability remains a challenge for future work.
    04B - Beitrag Konferenzschrift
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    Publication
    Practice track: a learning tracker using digital biomarkers for autistic preschoolers
    (2022) Sandhu, Gurmit; Kilburg, Anne; Martin, Andreas; Pande, Charuta; Witschel, Hans Friedrich; Laurenzi, Emanuele; Billing, Erik; Hinkelmann, Knut; Gerber, Aurona
    Preschool children, when diagnosed with Autism Spectrum Disorder (ASD), often ex- perience a long and painful journey on their way to self-advocacy. Access to standard of care is poor, with long waiting times and the feeling of stigmatization in many social set- tings. Early interventions in ASD have been found to deliver promising results, but have a high cost for all stakeholders. Some recent studies have suggested that digital biomarkers (e.g., eye gaze), tracked using affordable wearable devices such as smartphones or tablets, could play a role in identifying children with special needs. In this paper, we discuss the possibility of supporting neurodiverse children with technologies based on digital biomark- ers which can help to a) monitor the performance of children diagnosed with ASD and b) predict those who would benefit most from early interventions. We describe an ongoing feasibility study that uses the “DREAM dataset”, stemming from a clinical study with 61 pre-school children diagnosed with ASD, to identify digital biomarkers informative for the child’s progression on tasks such as imitation of gestures. We describe our vision of a tool that will use these prediction models and that ASD pre-schoolers could use to train certain social skills at home. Our discussion includes the settings in which this usage could be embedded.
    04B - Beitrag Konferenzschrift
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    Publication
    New hybrid techniques for business recommender systems
    (MDPI, 2022) Pande, Charuta; Witschel, Hans Friedrich; Martin, Andreas
    Besides the typical applications of recommender systems in B2C scenarios such as movie or shopping platforms, there is a rising interest in transforming the human-driven advice provided, e.g., in consultancy via the use of recommender systems. We explore the special characteristics of such knowledge-based B2B services and propose a process that allows incorporating recommender systems into them. We suggest and compare several recommender techniques that allow incorporating the necessary contextual knowledge (e.g., company demographics). These techniques are evaluated in isolation on a test set of business intelligence consultancy cases. We then identify the respective strengths of the different techniques and propose a new hybridisation strategy to combine these strengths. Our results show that the hybridisation leads to substantial performance improvement over the individual methods.
    01A - Beitrag in wissenschaftlicher Zeitschrift
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    Publication
    Combining machine learning with human knowledge for delivery time estimations
    (American Association for Artificial Intelligence (AAAI) Press, 2022) Lochbrunner, Markus; Witschel, Hans Friedrich; Martin, Andreas; Hinkelmann, Knut; Fill, Hans-Georg; Gerber, Aurona; Lenat, Doug; Stolle, Reinhard; van Harmelen, Frank
    Although machine learning algorithms outperform humans in many predictive tasks, their quality depends much on the availability of sufficient and representative training data. On the other hand, humans are capable of making predictions based on “spontaneous” transfers of knowledge from other domains or situations in cases where no directly relevant experiences exist. This can be seen very well in the task of predicting lead times in goods transport, where sudden disruptions or shortages may occur that are not reflected in historical data, but known to a well-informed human. If the variation can be anticipated and more accurate lead times estimated, proactive measures can be taken to decrease the impact. Therefore, we describe three novel approaches for delivery time predictions, combining a machine learning model with human input. The proposed logic covers two phases, learning based on actual delivery data and capturing human knowledge to cover exceptional situations not reflected in historical data. The proposed models and the resulting estimates were evaluated using deliveries from a retail company. It was found that the pure machine learning model delivers better results than a combination of humans and machines. On the one hand, this is caused by the complexity of incorporating human knowledge into the algorithm in a suitable way. On the other hand, it is also due to the tendency of humans to over-generalise the impact of certain events. Thus, although the pure machine learning model delivers superior estimation accuracy than the human-machine combination, our systematic qualitative analysis of the results presents insights for future development in this area.
    04B - Beitrag Konferenzschrift
  • Publication
    Studying interaction patterns for knowledge graph exploration
    (SciTePress, 2022) Grether, Loris; Witschel, Hans Friedrich; Coenen, Frans; Fred, Ana; Filipe, Joaquim
    04B - Beitrag Konferenzschrift
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    Publication
    Hybrid conversational AI for intelligent tutoring systems
    (Sun SITE, Informatik V, RWTH Aachen, 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.
    04B - Beitrag Konferenzschrift
  • Publication
    Natural language-based user guidance for knowledge graph exploration: a user study
    (SciTePress, 2021) Witschel, Hans Friedrich; Riesen, Kaspar; Grether, Loris; Cucchiara, Rita; Fred, Ana; Filipe, Joaquim
    Large knowledge graphs hold the promise of helping knowledge workers in their tasks by answering simple and complex questions in specialised domains. However, searching and exploring knowledge graphs in current practice still requires knowledge of certain query languages such as SPARQL or Cypher, which many untrained end users do not possess. Approaches for more user-friendly exploration have been proposed and range from natural language querying over visual cues up to query-by-example mechanisms, often enhanced with recommendation mechanisms offering guidance. We observe, however, a lack of user studies indicating which of these approaches lead to a better user experience and optimal exploration outcomes. In this work, we make a step towards closing this gap by conducting a qualitative user study with a system that relies on formulating queries in natural language and providing answers in the form of subgraph visualisations. Our system is able to offer guidance via query recommendations based on a current context. The user study evaluates the impact of this guidance in terms of both efficiency and effectiveness (recall) of user sessions. We find that both aspects are improved, especially since query recommendations provide inspiration, leading to a larger number of insights discovered in roughly the same time.
    04B - Beitrag Konferenzschrift