Witschel, Hans Friedrich
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Hans Friedrich
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Witschel, Hans Friedrich
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- 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
- PublikationEnhance classroom preparation for flipped classroom using AI and analytics(SciTePress, 2018) Diwanji, Prajakta; Hinkelmann, Knut; Witschel, Hans Friedrich; Hammoudi, Slimane; Smialek, Michal; Camp, Olivier; Filipe, Joaquim [in: ICEIS 2018. 20th International Conference on Enterprise Information Systems. Proceedings]In a flipped classroom setting, it is important for students to come prepared for the classroom. Being prepared in advance helps students to grasp the concepts taught during classroom sessions. A recent student survey at Fachhochschule Nordwestschweiz (FHNW), Business School, Switzerland, revealed that only 27.7% students often prepared before a class and only 7% always prepared before a class. The main reason for not preparing for classes was lack of time and workload. A literature review study revealed that there is a growth of the use of Artificial Intelligence (AI), for example, chatbots and teaching assistants, which support both teachers and students for classroom preparation. There is also a rise in the use of data analytics to support tutor decision making in real time. However, many of these tools are based on external motivation factors like grading and assessment. Intrinsic motivation among students is more rewarding in the long term. This paper proposes an application based on AI and data analysis that focuses on intrinsically motivating and preparing students in a flipped classroom approach.04B - Beitrag Konferenzschrift
- PublikationWorkplace Learning - Providing Recommendations of Experts and Learning Resources in a Context-sensitive and Personalized Manner(2016) Emmenegger, Sandro; Laurenzi, Emanuele; Thönssen, Barbara; Zhang Sprenger, Congyu; Hinkelmann, Knut; Witschel, Hans Friedrich [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) Emmenegger, Sandro; Thönssen, Barbara; Hinkelmann, Knut; Witschel, Hans Friedrich; Ana, Fred; Aveiro, David [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
- PublikationAn Ontology-based and Case-based Reasoning supported Workplace Learning Approach(Springer, 2016) Emmenegger, Sandro; Thönssen, Barbara; Laurenzi, Emanuele; Martin, Andreas; Zhang Sprenger, Congyu; Hinkelmann, Knut; Witschel, Hans Friedrich [in: Communications in Computer and Information Science]01A - Beitrag in wissenschaftlicher Zeitschrift
- PublikationAdapting an Enterprise Architecture for Business Intelligence(2015) von Bergen, Pascal; Hinkelmann, Knut; Witschel, Hans Friedrich [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; Witschel, Hans Friedrich [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
- PublikationLearning Business Rules for Adaptive Process Models(2012) Hinkelmann, Knut; Witschel, Hans Friedrich; Nguyen, Tuan Q. [in: BUSTECH 2012 - The Second International Conference on Business Intelligence and Technology]This work presents a new approach to handling knowledge-intensive business processes in an adaptive, flexible and accurate way. We propose to support processes by executing a process skeleton, consisting of the most important recurring activities of the process, through a workflow engine. This skeleton should be kept simple. The corresponding workflow is complemented by two features: firstly, a task management tool through which workflow tasks are delivered and that give human executors flexibility and freedom to adapt tasks by adding subtasks and resources as required by the context. And secondly, a component that learns business rules from the log files of this task management and that will predict subtasks and resources on the basis of knowledge from previous executions. We present supervised and unsupervised approaches for rule learning and evaluate both on a real business process with 61 instances. Results are promising, showing that meaningful rules can be learned even from this comparatively small data set.04 - Beitrag Sammelband oder Konferenzschrift
- PublikationRefining process models through the analysis of informal work practice(2011) Brander, Simon; Hinkelmann, Knut; Hu, Bo; Martin, Andreas; Riss, Uwe; Thönssen, Barbara; Witschel, Hans FriedrichThe work presented in this paper explores the potential of leveraging the traces of informal work and collaboration in order to improve business processes over time. As process executions often differ from the original design due to individual preferences, skills or competencies and exceptions, we propose methods to analyse personal preferences of work, such as email communication and personal task execution in a task management application. Outcome of these methods is the detection of internal substructures (subtasks or branches) of activities on the one hand and the recommendation of resources to be used in activities on the other hand, leading to the improvement of business process models. Our first results show that even though human intervention is still required to operationalise these insights it is indeed possible to derive interesting and new insights about business processes from traces of informal work and infer suggestions for process model changes.04B - Beitrag Konferenzschrift