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Sabine Seufert
Title
Prof. Dr.
Last Name
Seufert
First name
Sabine
Email
sabine.seufert@unisg.ch
Phone
+41 71 224 2632
Homepage
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1 - 10 of 76
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PublicationBig Data in Education: Supporting Learners in Their Role as Reflective Practitioners.(Springer, )Kinshuk, E.Type: book section
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PublicationFortgeschrittene Digitalisierung und Strategien für die berufliche (Weiter-)Bildung. Augmentation, Fusion Skills und Augumentationsstrategien.(Springer VS, 2023-11-02)
;Claudia De Witt ;Christina GloerfeldSilke Elisabeth WredeAusgehend von Entwicklungen im Bereich künstliche Intelligenz und Machine Learning finden leistungsfähige »Smart Machines« zunehmend Eingang in Berufs- und Arbeitsfelder. Damit wird die Fähigkeit, mit Smart Machines produktiv zusammenzuarbeiten, nicht nur zu einem wichtigen Treiber für Leistungsfähigkeit – von Einzelpersonen, von Teams sowie von gesamten Organisationen – sondern auch zu einem wichtigen Bildungsziel. Bildungsverantwortliche sind daher gefordert, Konzepte wie Augmentation, ›Fusion Skills‹ und Augmentationsstrategien zu verstehen und ihre Bildungsarbeit darauf auszurichten.Type: book section -
PublicationKompetenzen von Führungskräften zur Gestaltung der digitalen Transformation von Bildungsorganisationen(hep Verlag, 2023)
;Tobias Röhl ;Johannes Breitschaft ;Eliane BurriNicole WespiType: book section -
PublicationWho is Best Suited for the Job? Task Allocation Process Between Teachers and Smart Machines Based on Comparative StrengthsDue to advances in machine learning (ML) and artificial intelligence (AI), computer systems are becoming increasingly intelligent and capable of taking on new tasks (e.g., automatic translation of texts). In education, such AI-powered smart machines (e.g., chatbots, social robots) have the potential to support teachers in the classroom in order to improve the quality of teaching. However, from a teacher’s point of view, it may be unclear which subtasks could be best outsourced to the smart machine. Considering human augmentation, this paper presents a theoretical basis for the use of smart machines in education. It highlights the relative strengths of teachers and smart machines in the classroom and proposes a staged process for assigning classroom tasks. The derived task allocation process can be characterized by its three main steps of 1) break-down of task sequence and rethinking the existing task structure, 2) invariable task assignment (normative and technical considerations), and 3) variable task assignment (efficiency considerations). Based on the comparative strengths of both parties, the derived process ensures that subtasks are assigned as efficiently as possible (variable task assignment), while always granting priority to subtasks of normative importance (invariable task assignment). In this way, the derived task allocation process can serve as a guideline for the design and the implementation of smart machine projects in education.
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PublicationSocial robots in education: conceptual overview and use case of academic writing(Springer, 2022-01)
;Ifenthaler, Dirk ;Isaías, PedroSampson, DemetriosSocial robots are increasingly being used in education. They can take over various roles including teaching assistant, tutor, and novice. This chapter aims to provide a conceptual overview of the phenomenon. A classification of social robots is outlined; the criteria are: visual appearance, social capabilities, and autonomy and intelligence. The majority of robots used in education are humanoid; Nao from SoftBank Robotics is a quasi-standard type. An important social capability is empathy; a model illustrating how a robot can show empathy is discussed. A taxonomy is presented in order to capture the various degrees of robot autonomy. To achieve autonomy, artificial intelligence is necessary. This chapter advocates for a symbiotic design approach where tasks are collaboratively carried out by the teacher and the social robot, utilizing the complementary strength of both parties. This may be in line with the concept of hybrid intelligence. The ethical aspects of social robot use are explored, including privacy, control, responsibility, and the role of teachers. Moreover, the acceptance of social robots is discussed. Overall, attitudes towards social robots seem to be positive; however, there are also contrary findings. Finally, results are presented from a technology acceptance study with a sample of N = 462 university students from the social sciences. The chapter closes with suggestions for further research.Type: book section -
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PublicationShaping AI Transformation: Digital Competencies and Augmentation Strategies of HRD Professionals(Springer, 2022)
;Dirk, IfenthalerType: book section -
PublicationType: book section
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PublicationWhen Lecturers have a Choice: Covid-19 Teaching Format Preferences in a Large-Scale Course of Freshmen Students in Switzerland(Gesellschaft für Informatik e.V., 2021-09)Covid-19 is increasingly forcing educational institutions to explore new avenues and weight the pros and cons between on-site instruction, online instruction, and mixed formats. Understanding teaching format preferences of lecturers may be helpful for creating meaningful solutions with educational technologies. The paper at hand documents and reflects on the organization and implementation of a large-scale first-semester course in Switzerland in the fall term 2020, where seminar lecturers were free to decide on their used course format (on-site, online, mixed). The format preferences of all 39 seminar lecturers were captured and evaluated. Our results indicate that seminar lecturers predominantly opted for mixed or online seminars; often they like to conduct the very first lesson on site for the purpose of becoming familiar with the students.Type: book section
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PublicationType: book section