Hinkelmann, Knut
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Hinkelmann, Knut
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- PublikationPractice 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 [in: Proceedings of the Society 5.0 Conference 2022 - Integrating digital world and real world to resolve challenges in business and society]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
- PublikationHybrid 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 [in: Proceedings of the AAAI 2021 Spring Symposium on Combining Machine Learning and Knowledge Engineering (AAAI-MAKE 2021)]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
- PublikationLearning and engineering similarity functions for business recommenders(2019) Witschel, Hans Friedrich; Martin, Andreas; Martin, Andreas; Hinkelmann, Knut; Gerber, Aurona; Lenat, Doug; Harmelen, Frank van; Clark, Peter [in: Proceedings of the AAAI 2019 Spring Symposium on Combining Machine Learning with Knowledge Engineering (AAAI-MAKE 2019)]We study the optimisation of similarity measures in tasks where the computation of similarities is not directly visible to end users, namely clustering and case-based recommenders. In both, similarity plays a crucial role, but there are also other algorithmic components that contribute to the end result. Our suggested approach introduces a new form of interaction into these scenarios that make the use of similarities transparent to end users and thus allows to gather direct feedback about similarity from them. This happens without distracting them from their goal – rather allowing them to obtain better and more trustworthy results by excluding dissimilar items. We then propose to use the feedback in a way that incorporates machine learning for updating weights and decisions of knowledge engineers about possible additional features, based on insights derived from a summary of user feedback. The reviewed literature and our own previous empirical investigations suggest that this is the most feasible way – involving both machine and human, each in a task that they are particularly good at.04B - Beitrag Konferenzschrift
- PublikationWorkplace Learning - Providing Recommendations of Experts and Learning Resources in a Context-sensitive and Personalized Manner(2016) Hinkelmann, Knut [in: Proceedings of Special Session on Learning Modeling in Complex Organizations (LCMO) at MODELSWARD'16]Support of workplace learning is increasingly important as change in every form determines today's working world in industry and public administrations alike. Adapt quickly to a new job, a new task or a new team is a major challenge that must be dealt with ever faster. Workplace learning differs significantly from school learning as it should be strictly aligned to business goals. In our approach we support workplace learning by providing recommendations of experts and learning resources in a context-sensitive and personalized manner. We utilize user s' workplace environment, we consider their learning preferences and zone of proximal development, and compare required and acquired competencies in order to issue the best suited recommendations. Our approach is part of the European funded project Learn PAd. Applied research method is Design Science Research. Evaluation is done in an iterative process. The recommender system introduced here is evaluated theoretically based on user requirements and practically in an early evaluation process conducted by the Learn PAd application partner.04B - Beitrag Konferenzschrift
- PublikationKPIs 4 Workplace Learning(Springer, 2016) Hinkelmann, Knut [in: Proceedings of the 8th International Conference on Knowledge Management and Information Sharing (KMIS)]Enterprises and Public Administrations alike need to ensure that newly hired employees are able to learn the ropes fast. Employers also need to support continuous workplace learning. Work-place learning should be strongly related to business goals and thus, learning goals should direct-ly add to business goals. To measure achievement of both learning and business goals we pro-pose augmented Key Performance Indicators (KPI). In our research we applied model driven engineering. Hence we developed a model for a Learning Scorecard comprising of business and learning goals and their KPIs represented in an ontology. KPI performance values and scores are calculated with formal rules based on the SPARQL Inferencing Notation. Results are presented in a dashboard on an individual level as well as on a team/group level. Requirements, goals and KPIs as well as performance measurement were defined in close co-operation with Marche Region, business partner in Learn PAd.04B - Beitrag Konferenzschrift
- PublikationAdapting an Enterprise Architecture for Business Intelligence(2015) Hinkelmann, Knut [in: 8th IFIP WG 8.1 working conference on the Practice of Enterprise Modelling]04B - Beitrag Konferenzschrift
- PublikationAuswahl der richtigen Wissensmanagement-Methoden(W. Gassmann, 2012) Hinkelmann, Knut [in: Blickpunkt KMU]Einerseits sind die Notwendigkeit für einen adäquaten Umgang mit der Ressource "Wissen" und der daraus resultierende potenzielle Gewinn für ein Unternehmen allgemein anerkannt. Andererseits entwickeln sich längst nicht alle Wissensmanagement-Projekte in der Praxis zu Erfolgsgeschichten. Im Gegenteil: selbst beim Einsatz vermeintlich bewährter Wissensmanagement-Strategien kommt es immer wieder vor, dass grossen Investitionen seitens eines Unternehmens am Ende kaum beobachtbare Verbesserungen gegenüberstehen. Häufigste Ursache hierfür ist die mangelnde Akzeptanz der implementierten Lösungen bei den Mitarbeitern.01B - Beitrag in Magazin oder Zeitung