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Pervasive learning analytics for fostering learners' self-regulation
Pervasive learning analytics for fostering learners' self-regulation
Today's tertiary STEM (Science, Technology, Engineering and Mathematics) education in Europe poses problems to both teachers and students. With growing enrolment numbers, and numbers of teaching staff that are outmatched by this growth, student-teacher contact becomes more and more difficult to provide. Therefore, students are required to quickly adopt self-regulated and autonomous learning styles when entering European universities. Furthermore, teachers are required to divide their attention between large numbers of students. As a consequence, classical teaching formats of STEM education which often encompass experimentation or active exploration, become harder to implement. Educational software holds the promise of easing these problems, or, if not fully solving, at least of making them less acute: Learning Analytics generated by such software can foster self-regulation by providing students with both formative feedback and assessments. Educational software, in form of collaborative social media, makes it easier for teachers to collaborate, allows to reduce their workload and enables learning and teaching formats otherwise infeasible in large classes. The contribution of this thesis is threefold: Firstly, it reports on a social medium for tertiary STEM education called "Backstage2 / Projects" aimed specifically at these points: Improving learners' self-regulation by providing pervasive Learning Analytics, fostering teacher collaboration so as to reduce their workload, and providing means to deploy a variety of classical and novel learning and teaching formats in large classes. Secondly, it reports on several case studies conducted with that medium which point at the effectiveness of the medium and its provided Learning Analytics to increase learners' self-regulation, reduce teachers' workload, and improve how students learn. Thirdly, this thesis reports on findings from Learning Analytics which could be used in the future in designing further teaching and learning formats or case studies, yielding a rich perspective for future research and indications for improving tertiary STEM education.
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Heller, Niels
2020
English
Universitätsbibliothek der Ludwig-Maximilians-Universität München
Heller, Niels (2020): Pervasive learning analytics for fostering learners' self-regulation. Dissertation, LMU München: Faculty of Mathematics, Computer Science and Statistics
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Abstract

Today's tertiary STEM (Science, Technology, Engineering and Mathematics) education in Europe poses problems to both teachers and students. With growing enrolment numbers, and numbers of teaching staff that are outmatched by this growth, student-teacher contact becomes more and more difficult to provide. Therefore, students are required to quickly adopt self-regulated and autonomous learning styles when entering European universities. Furthermore, teachers are required to divide their attention between large numbers of students. As a consequence, classical teaching formats of STEM education which often encompass experimentation or active exploration, become harder to implement. Educational software holds the promise of easing these problems, or, if not fully solving, at least of making them less acute: Learning Analytics generated by such software can foster self-regulation by providing students with both formative feedback and assessments. Educational software, in form of collaborative social media, makes it easier for teachers to collaborate, allows to reduce their workload and enables learning and teaching formats otherwise infeasible in large classes. The contribution of this thesis is threefold: Firstly, it reports on a social medium for tertiary STEM education called "Backstage2 / Projects" aimed specifically at these points: Improving learners' self-regulation by providing pervasive Learning Analytics, fostering teacher collaboration so as to reduce their workload, and providing means to deploy a variety of classical and novel learning and teaching formats in large classes. Secondly, it reports on several case studies conducted with that medium which point at the effectiveness of the medium and its provided Learning Analytics to increase learners' self-regulation, reduce teachers' workload, and improve how students learn. Thirdly, this thesis reports on findings from Learning Analytics which could be used in the future in designing further teaching and learning formats or case studies, yielding a rich perspective for future research and indications for improving tertiary STEM education.