Auflistung nach Autor:in "Simbeck, Katharina"
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- KonferenzbeitragAdaptive Learning as a Service – A concept to extend digital learning platforms?(20. Fachtagung Bildungstechnologien (DELFI), 2022) Rzepka, Nathalie; Simbeck, Katharina; Müller, Hans-Georg; Pinkwart, NielsAdaptive 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.
- KonferenzbeitragBreaking It Down: On the Presentation of Fine-Grained Learning Objects in Virtual Learning Environments(21. Fachtagung Bildungstechnologien (DELFI), 2023) Selmanagić, André; Simbeck, KatharinaCurrent research on personalized learning environments has a strong focus on the individualized sequencing of learning objects and the creation of adaptive feedback within them. Little emphasis has been placed on designing virtual learning environments (VLEs) that retrieve the necessary learning objects from online repositories and utilize semantic metadata to present them dynamically. Thus, this paper presents an approach for transforming a semantic web of fine-grained learning objects into a functional VLE that delivers these objects to learners.
- Conference PaperCode of Practice for Sensor-Based Learning(DELFI 2019, 2019) Yun, Haeseon; Riazy, Shirin; Fortenbacher, Albrecht; Simbeck, KatharinaSensor-based learning refers to utilizing physiological sensor data from learners and information from a learning environment to promote learning. Sensor data enclose learner’s personal information so ethical practice of adopting sensor data in learning analytics needs to be explored thoroughly. In this positional paper, we examine current ethical practices in learning analytics to derive a code of practice for sensor-based learning. Furthermore, we critically validate a wearable sensor device developed as a learning support against the derived code of practice.
- KonferenzbeitragDesigning Granular Competency Frameworks for Adaptive Learning on the Example of Naïve Bayes Classifiers(Proceedings of DELFI Workshops 2022, 2022) Selmanagić, André; Simbeck, KatharinaAdaptive learning environments that follow a competency-based learning approach require granular, domain-specific competency frameworks (models) for the continuous assessment of a learner’s knowledge and skills as well as for the subsequent personalization of instruction. This case-study describes the iterative creation process for a competency framework in the domain of Naïve Bayes classifiers, including the design principles that led to the framework and the tools used for making it publishable as linked, open data.
- KonferenzbeitragEnergy Consumption of AI in Education: A Case Study(21. Fachtagung Bildungstechnologien (DELFI), 2023) BĂĽltemann, Marlene; Rzepka, Nathalie; Junger, Dennis; Simbeck, Katharina; MĂĽller, Hans-GeorgAlthough 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.
- ZeitschriftenartikelHighly Accurate, But Still Discriminatory(Business & Information Systems Engineering: Vol. 63, No. 1, 2021) Köchling, Alina; Riazy, Shirin; Wehner, Marius Claus; Simbeck, KatharinaThe study aims to identify whether algorithmic decision making leads to unfair (i.e., unequal) treatment of certain protected groups in the recruitment context. Firms increasingly implement algorithmic decision making to save costs and increase efficiency. Moreover, algorithmic decision making is considered to be fairer than human decisions due to social prejudices. Recent publications, however, imply that the fairness of algorithmic decision making is not necessarily given. Therefore, to investigate this further, highly accurate algorithms were used to analyze a pre-existing data set of 10,000 video clips of individuals in self-presentation settings. The analysis shows that the under-representation concerning gender and ethnicity in the training data set leads to an unpredictable overestimation and/or underestimation of the likelihood of inviting representatives of these groups to a job interview. Furthermore, algorithms replicate the existing inequalities in the data set. Firms have to be careful when implementing algorithmic video analysis during recruitment as biases occur if the underlying training data set is unbalanced.
- KonferenzbeitragLecturers’ reflections on adaptive feedback in learning management systems as input for sustainable instruction design(20. Fachtagung Bildungstechnologien (DELFI), 2022) Donevska-Todorova, Ana; Simbeck, Katharina; Dziergwa,Katrin
- KonferenzbeitragMobile First: Trends in Virtual Learning Environments(DELFI 2020 – Die 18. Fachtagung Bildungstechnologien der Gesellschaft für Informatik e.V., 2020) Riazy, Shirin; Simbeck, Katharina; Träger, Marco; Wöstenfeld, RobertAlthough mobile learning has long been predicted to become a vital part of the educational reality, schools often seem reluctant to implement mobile teaching solutions. In order to assess the current preferences of learning modalities for school students (ages 9-15) and teachers, an e-learning environments traffic data was analyzed. We have detected two trends: The first is a total rise of mobile usage, especially in comparison to the usage of desktop PCs in the past four years. Second, we have detected that especially students aged 12-15 mostly prefer mobile devices. Hence platform design should be adapted for better use with mobile devices to meet the learners’ needs.
- Conference PaperPredictive Algorithms in Learning Analytics and their Fairness(DELFI 2019, 2019) Riazy, Shirin; Simbeck, KatharinaPredictions in learning analytics are made to improve tailored educational interventions. However, it has been pointed out that machine learning algorithms might discriminate, depending on different measures of fairness. In this paper, we will demonstrate that predictive models, even given a satisfactory level of accuracy, perform differently across student subgroups, especially for different genders or for students with disabilities.
- KonferenzbeitragShow 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, NielsOur 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.