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Auflistung nach Autor:in "Rzepka, Nathalie"

1 - 3 von 3
  • Konferenzbeitrag
    Adaptive Learning as a Service – A concept to extend digital learning platforms?
    (20. Fachtagung Bildungstechnologien (DELFI), 2022) Rzepka, Nathalie; Simbeck, Katharina; Müller, Hans-Georg; Pinkwart, Niels
    Adaptive learning environments that adjust to the individual user are promising. Unfortunately, many digital learning environments are not yet adaptive and the transformation of legacy software to an adaptive learning environment is complex and costly. Our work introduces the concept of adaptive learning as a service and discusses potential benefits as well as challenges.
  • Konferenzbeitrag
    Energy Consumption of AI in Education: A Case Study
    (21. Fachtagung Bildungstechnologien (DELFI), 2023) Bültemann, Marlene; Rzepka, Nathalie; Junger, Dennis; Simbeck, Katharina; Müller, Hans-Georg
    Although the utilization of AI in education has grown considerably in the last decade, its environmental impact has been disregarded thus far. In this paper, we examine the energy consumption of Artificial Intelligence (AI) in education, which is employed, for instance, in adaptive learning. We measured the energy requirements of four AI implementations used on the student learning platform orthografietrainer.net. We found that two of the implementations have notably low energy and CPU demands in comparison to the baseline, while in two other implementations, these parameters are significantly higher. We conclude that more attention should be paid to whether the comparable performance of AI in education can be achieved with lower energy consumption.
  • Konferenzbeitrag
    Show me the numbers! - Student-facing Interventions in Adaptive Learning Environments for German Spelling
    (21. Fachtagung Bildungstechnologien (DELFI), 2023) Rzepka, Nathalie; Simbeck, Katharina; Müller, Hans-Georg; Bültemann, Marlene; Pinkwart, Niels
    Our work presents the result of an experiment conducted on an online platform for the acquisition of German spelling skills. We compared the traditional online learning platform to three different adap-tive versions of the platform that implement machine learning-based student-facing interventions that show the personalized solution probability. We evaluate the different interventions with regards to the error rate, the number of early dropouts, and the users’ competency. Our results show that the number of mistakes decreased in comparison to the control group. Additionally, an increasing num-ber of dropouts was found. We did not find any significant effects on the users’ competency. We conclude that student-facing adaptive learning environments are effective in improving a person’s error rate and should be chosen wisely to have a motivating impact.