TY - CHAP A1 - Döhring, Thorsten A1 - Stieglitz, Veronika A1 - Hudec, Rene A1 - Sapsai, Iryna A1 - Friedrich, Peter A1 - Burwitz, Vadim T1 - Characterization of a lobster-eye type X-ray telescope T2 - EOSAM-Conference N2 - Lobster-eye type X-ray telescopes use reflecting plano mirrors under grazing incidence and can observe a large field of view. As part of a Bavarian-Czech cooperation, two telescopes were build, equipped with mirrors coated with gold and iridium. Their X-ray characterization was carried out at the PANTER test facility, which simulates parallel starlight incident on the telescopes. The telescopes have an angular resolution of about 4 arc minutes in X-rays and a focal length of about 2 meters. The used X-ray mirrors reflect and focus visible light as well; their functionality in the optical regime was checked in laboratory tests. Now another test campaign will be carried out to examine the telescope resolution for real objects of the visible night sky and the imaging properties for star constellations. KW - Lobster-Eye KW - X-ray KW - telescope KW - iridium KW - mirror KW - Röntgenteleskop KW - Iridium KW - Röntgenspiegel Y1 - 2022 UR - https://www.europeanoptics.org/events/eos/eosam2022.html VL - 2022 SP - 1 EP - 1 ER - TY - CHAP A1 - Valencia, Yeimy A1 - Normann, Marc A1 - Sapsai, Iryna A1 - Abke, Jörg A1 - Madsen, Anders L. A1 - Weidl, Galia T1 - Learning Style Classification by Using Bayesian Networks Based on the Index of Learning Style T2 - ECSEE '23: Proceedings of the 5th European Conference on Software Engineering Education, June 2023 KW - Lernstil KW - Fragebogen Y1 - 2023 U6 - https://doi.org/10.1145/3593663.3593685 SP - 73 EP - 82 ER - TY - JOUR A1 - Sapsai, Iryna A1 - Valencia, Yeimy A1 - Abke, Jörg T1 - Learning Analytics Dashboard for Educators: Proposed Project to Design with Pedagogical Background JF - ECSEE '23: Proceedings of the 5th European Conference on Software Engineering Education N2 - In this article, the authors describe a prototype of a Learning Analytics Dashboard (LAD) for educators. It is based on the analysis of pedagogical actions and taking into the process and learning style of students in an online environment based on learning analytics (LA). A description of the Dashboard structure, divided into levels and categories based on available learning analytics, will allow the educator to dive deeper into the online course themselves and explore more. It will also allow them to determine the level of student performance, identify gaps in learning materials, and research student data. The authors have identified further directions for the development of a LAD for a professor, including modeling algorithms for researching student behavior and learning style using Artificial Intelligence and presenting LA in a visualized form. This paper shows the stages of creating a professor's LAD prototype as a functional part of the adaptive learning system in the HASKI-System to analyze visual information obtained from LA and the possibilities to monitor the learning process, learning progress, student activity, and make decisions on careful intervention in the students’ learning process. KW - Lerntechnik KW - Computerunterstütztes Lernen Y1 - 2023 UR - https://dl.acm.org/doi/abs/10.1145/3593663.3593686 U6 - https://doi.org/https://doi.org/10.1145/3593663.3593686 VL - 2023 IS - ECSEE '23: Proceedings of the 5th European Conference on Software Engineering Education SP - 38 EP - 47 ER - TY - CHAP A1 - Döhring, Thorsten A1 - Stieglitz, Veronika A1 - Hudec, Rene A1 - Sapsai, Iryna A1 - Friedrich, Peter A1 - Burwitz, Vadim T1 - Characterization of a lobster-eye type X-ray telescope T2 - EPJ Web of Conferences N2 - Lobster-eye type X-ray telescopes use reflecting plano mirrors under grazing incidence and can observe a large field of view. As part of a Bavarian-Czech cooperation, two telescopes were build, equipped with mirrors coated with gold and iridium. Their X-ray characterization was carried out at the PANTER test facility, which simulates parallel starlight incident on the telescopes. The telescopes have an angular resolution of about 4 arc minutes in X-rays and a focal length of about 2 meters. The used X-ray mirrors reflect and focus visible light as well; their functionality in the optical regime was checked in laboratory tests. Now another test campaign will be carried out to examine the telescope resolution for real objects of the visible night sky and the imaging properties for star constellations. KW - X-ray telescopes KW - Lobster-eye KW - Grazing incidence KW - Large field of view KW - Röntgenteleskop KW - Röntgenspiegel Y1 - 2022 UR - https://doi.org/10.1051/epjconf/202226613010 U6 - https://doi.org/10.1051/epjconf/202226613010 VL - 266 (2022) SP - 1 EP - 2 ER - TY - CHAP A1 - Haug, Jim A1 - Sapsai, Iryna A1 - Hock, Isabell A1 - Abke, Jörg A1 - Hagel, Georg ED - Haug, Jim T1 - EVALUATING AN AI-BASED ADAPTIVE LEARNING SYSTEM: GOALS, METHODS AND INITIAL RESULTS T2 - 16th International Conference on Education and New Learning Technologies, 1-3 July, 2024, Palma, Spain N2 - The aim of this paper is to describe the evaluation process and findings of an AI-based Adaptive Learning System for the Computer Science discipline at two different German universities and discuss an array of methods in regard to assessing such a system. The primary objectives have been twofold: firstly, to examine the reception of selected learning elements, which were conceptually outlined based on relevant literature, among the student body; and secondly, to investigate the efficacy of individualized adaptive learning paths. These paths were generated by employing a variety of algorithms to analyze students learning style tendencies, with a particular emphasis on adaptive navigational techniques. The used algorithms encompassed a modified version of a literature based adaptive mechanism, an Ant-Colony-Algorithm and a Genetic Algorithm, alongside a lecturer-recommended learning path for a non-adaptive comparison. While the system suggested suitable learning paths based on student data, it never forced the individuals to give up their self-directed learning. The evaluation criteria revolved around the evolution of student motivation, interest levels, and knowledge acquisition during the time they spent working in the system. The evaluation sought to facilitate comparative analyses and assess algorithmic fitness for proficient learning path generation. The methods included both quantitative and qualitative approaches to gather data, seeking to strike a balance between being student-friendly and scientifically informative. They ranged from Likert Scale self-assessments to screen and video observations with retrospective interviews. Since the purpose of adaptive learning systems is intertwined with personalized learning it seems imperative to already take the preferences and opinions of students into account while the system is still in development. This complexity underscores the challenge of evaluating such systems, as significant constraints on student choice - though simplifying evaluation - directly oppose the ethos of individualized, self-directed learning. Initial findings suggest that the underlying theoretical considerations on sequencing and structuring of learning elements are confirmed, coupled with providing adequate flexibility to meet diverse learning needs. Cross-site evaluation of the literature-based learning elements indicated a high comprehensibility and positive student ratings. While significant positive trends were observed regarding knowledge acquisition, they cannot be definitively attributed to a specific method of learning path generation. Motivation and interest analyses show no significant differences among learning path types, albeit heavily limited by sample size. Similarly, emotion measurements, though limited, hint at positive impacts from HASKI system use. Despite limitations, early indications suggest student acceptance and potential effectiveness of learning paths, highlighting the need for larger sample sizes for validation and expansion. Ensuring alignment with student needs and user-friendly design are crucial considerations. KW - Adaptive learning environment, tertiary education, personalized e-learning, evaluation. KW - E-Learning KW - Evaluation KW - Künstliche Intelligenz Y1 - 2024 UR - https://library.iated.org/view/HAUG2024EVA U6 - https://doi.org/10.21125/edulearn.2024.0834 VL - 2024 IS - EDULEARN24 Proceedings SP - 3157 EP - 3166 ER - TY - JOUR A1 - Sapsai, Iryna A1 - Haug, Jim A1 - Abke, Jörg A1 - Hagel, Georg T1 - IDENTIFYING STUDENT EMOTIONS IN AN ADAPTIVE LEARNING SYSTEM WITH A BAYESIAN NETWORK MODEL JF - IATED Digital Library Home N2 - Analyzing scientific literature on the impact of emotions in e-learning highlights the importance of assessing students' emotional states and their role during online learning processes. This assessment often relies on physiological sensors, which may not be accessible to all institutions or acceptable to all students. Therefore, detecting students' emotional states in an online learning environment remains a complex challenge requiring a fundamental understanding of the role of emotions in such environments. This paper explores the potential benefits of using self-reported surveys to identify students' affective states and improve their learning experience by responding to specific emotional states during their learning progress. Our objectives include identifying emotional states through surveys, pinpointing frequently selected emotion-descriptive words, analyzing survey results, and developing a Bayesian Network (BN) model for automatic emotional state analysis. This BN model aims to facilitate real-time interactions within an adaptive learning system by autonomously assessing students' emotions without direct educator intervention. We present findings from an emotion identification survey conducted among engineering students in an e-learning course provided in an adaptive learning environment. Practically, we plan to implement the BN model within the aforementioned environment to interact with students through real-time pop-up messages, identifying and responding to their emotional states. Its generic character structure allows for changes in the set of analyzed words, ensuring its broad applicability. The challenges of identifying emotions during different online learning activities and the question of the optimal word selection remain for future exploration. Effective identification of students' emotional states is expected to enhance online learning, improving emotional well-being, motivation, and individual success. KW - e-learning, Emotion Identification, Automatic Detection Techniques, Bayesian Networks. KW - Bayes-Netz KW - E-Learning Y1 - 2024 UR - https://library.iated.org/view/SAPSAI2024IDE U6 - https://doi.org/10.21125/iceri.2024.1192 VL - 2024 IS - ICERI2024 Proceedings SP - 4829 EP - 4837 ER -