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Auflistung nach Schlagwort "adaptive learning system"

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  • Conference Paper 
    Sensor Based Adaptive Learning - Lessons Learned
    (DELFI 2019, 2019) Fortenbacher, Albrecht; Ninaus, Manuel; Yun, Haeseon; Helbig, René; Moeller, Korbinian
    Recent advances in sensor technology allow for investigating emotional and cognitive states of learners. However, making use of sensor data is a complex endeavor, even more so when considering physiological data to support learning. In the BMBF-funded project Learning Analytics for sensor-based adaptive learning (LISA), we developed a comprehensive solution for adaptive learning using sensor data for acquiring skin conductance, heart rate, as well as environmental factors (e.g. CO2). In particular, we developed, (i) a sensor wristband acquiring physiological and environmental data, (ii) a tablet application (SmartMonitor) for monitoring and visualizing sensor data, (iii) a learning analytics backend, which processes and stores sensor data obtained from SmartMonitor, and (iv) learning applications utilizing these features. In an ongoing study, we applied our solution to a serious game to adaptively control its difficulty. Post-hoc interviews indicated that learners became aware of the adaptation and rated the adaptive version better and more exciting. Although potentials of utilizing physiological data for learning analytics are very promising, more interdisciplinary research is necessary to exploit these for real-world educational settings.
  • Conference paper
    Term Extraction for Domain Modeling
    (Proceedings of DELFI 2024, 2024) Kruse, Theresa; Lohr, Dominic; Berges, Marc; Kohlhase, Michael; Moghbeli, Halimeh; Schütz, Marcel
    Adaptive learning systems need to use domain and learner models to provide meaningful support for learners. Building fine-grained domain models by hand is very time-consuming, so the demand for partial automation is high. This paper investigates how term extraction tools can support constructing a domain model. Therefore, we study if different automatic term extraction tools give comparable results to a human annotator. Our results show that the current extraction tools support the process, but their results are not directly usable and still need human adjustments.