Logo des Repositoriums

Auflistung nach Autor:in "Bauer, Mathias"

1 - 6 von 6
  • Konferenzbeitrag
    Configuration and Introspection of Situated User Support
    (13. GI-Workshop "Adapitivität und Benutzermodellierung in interaktiven Softwaresystemen", 2005) Baldes, Stephan; Kröner, Alexander; Bauer, Mathias
    User support which takes into account the user‘s current context is a key issue of ambient intelligence applications. In this contribution we address the question of how a user may configure and keep control of situated user support. Background of our work is a system, which creates an augmented, personal memory from events perceived in an instrumented environment. We show how the user may –assisted by the system– perform a collaborative introspection of this memory in order to learn about and to exploit past events for user support. In particular we discuss methods for evaluating events and for acquiring proactively feedback in order to improve the quality of a binding between situations and system services.
  • Conference Paper 
    Förderung der Lernmotivation durch adaptives E-Learning: Komparative Evaluation von Techniken zur adaptiven Nutzerführung
    (DELFI 2019, 2019) Bauer, Mathias; Schuldt, Jacqueline; Krömker, Heidi; Bau, Beatrix; Webers, Marie Jeanne
    Wenngleich Lernmotivation eine wichtige Einflussgröße eines erfolgreichen Lernprozesses darstellt, ist diese bisher kaum Gegenstand der Forschung im Bereich adaptiver E-Learning Systeme. In einem mehrphasigen Studienkonzept soll die Frage beantwortet werden, ob Lernmotivation mithilfe von Adaptation gefördert werden kann. Das aktuelle Papier fokussiert sich daher auf eine komparative Evaluation von drei Adaptationstechniken und deren Einfluss auf die Lernmotivation mit einem Sample von 132 Studierenden in Form einer experimentellen Laboruntersuchung im Vergleich mit einer nicht-adaptiven Version als Kontrollgruppe. Schwerpunkt ist die Auswertung von Logfiles zur Erfassung der Wirksamkeit der Adaptationstechniken und des aktuellen Verlaufs der Lernmotivation während der Arbeit mit einer E-Learning Plattform. Die Adaptation erfolgte auf Basis von Motivations-Selbsteinschätzungen. Die Analyse der Daten gibt erste Rückschlüsse auf die motivationsförderliche Gestaltung der Nutzerführung in E-Learning Systemen.
  • Textdokument
    SensoMot – Sensorische Erfassung von Motivationsindikatoren zur Steuerung adaptiver Lerninhalte
    (Bildungsräume 2017, 2017) Schneider, Oliver; Martens, Thomas; Bauer, Mathias; Ott-Kroner, Alexandra; Dick, Uwe; Dorochevsky, Michel
    Im Projekt SensoMot wird untersucht, wie Motivationslagen durch unaufdringliche Sensoren erfasst und die hieraus abgeleiteten Parameter genutzt werden können, um Lerninhaltezur Laufzeit anzupassen. Eine so erhöhte Lernmotivation könnte in technologiebasierten Lehr-Lern-Szenarios zu größeren Lernerfolgen führen.
  • Konferenzbeitrag
    SPECTER: a User-Centered View on Ubiquitous Computing
    (11. GI-Workshop "Adaptivität und Benutzermodellierung in interaktiven Softwaresystemen", 2003) Kleinbauer, Thomas; Bauer, Mathias; Jameson, Anthony
    Although there still remain a lot of open questions on technical issues in ubiquitous computing, mobile system must not forget to put the user in the center of their interest. Context data must be presented in an intelligible way, all system behavior must support the user according to a model of her preferences and interests and privacy concerns should always be seen as a key issue. In this paper, we discuss various aspects of these topics on the basis of the SPECTER system, without raising a claim to completeness.
  • Konferenzbeitrag
    The MAUT-Machine: An Adaptive Recommender System
    (10. GI-Workshop "Adaptivität und Benutzermodellierung in interaktiven Softwaresystemen", 2002) Schmitt, Christian; Dengler, Dietmar; Bauer, Mathias
    In this paper we introduce an adaptive recommender system that supports the user in finding interesting entries in an electronic product catalog. The Analytic Hierarchy Process (AHP) lays the foundation for the user preferences. The user interests are specified in terms of desired properties of the ideal product, expressed in the form of constraints (or criteria) on the products basic attributes. Logically related criteria can be combined to form a criteria tree and weights are used to specify the relative importance of these criteria (their contribution impact on the overall rating). An extension of the Multi-Attribute Utility Theory (MAUT) that allows complex and powerful combinations of criteria is used to compute the degree of interest (or utility) of the products regarding the user preferences. Once the user has defined her preferences she can quickly identify the most promising products in the treemaps display of the catalogue, colour-coding schemes indicating the different degrees of interest and the user having the possibility to sort the catalogue by several of its products basic attributes. The user profile and the catalogue are simultaneously visible and each modification of her preferences is immediately mirrored in the treemaps display that always presents the quality of the catalogue entries w.r.t. the current user preferences. The user can also select a given criteria node in her preferences and see the performance of the catalogue entries for that criteria. This way, rational decision making with multi-criteria objectives is significantly alleviated.
  • Konferenzbeitrag
    Transparent User Modeling for a Mobile Personal Assistant
    (12. GI-Workshop "Adapitivität und Benutzermodellierung in interaktiven Softwaresystemen", 2004) Bauer, Mathias
    User models (UM) – the explicit representation of all relevant aspects of a user's preferences, beliefs, etc. – form the basis of virtually all adaptive systems. While early approaches re- lied on explicit interviews to fill in the details of such a UM, more recent systems unobtrusively observe their users and automatically infer the contents of the UM. This reduces the workload for the user, but it also makes her lose control over the system's knowledge about her and the way this knowledge is used. This paper discusses the necessity for transparent user modeling as well as various techniques for achieving it.