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AI Engineering @ FHTW
(2022)
The current shift in teaching and learning away from the physical
classroom to blended and digital learning environments presents many
challenges and opportunities for both teachers and learners. A
somewhat overlooked aspect of this transition concerns the issue of
student collaboration in blended learning situations. Students teaming
up to improve their learning process, exchange ideas and achieve
learning goals has been an integral part of the higher education
experience for many, while also strengthening students social skills.
With the physical distancing and accompanying shift to increased
online learning settings of the past few years, establishing this
collaboration between students has become more and more difficult.
Well-conceived digital social learning spaces and opportunities might
be a way to compensate for these missed out traditional learning
situations with peers in or after class.
Going beyond typical group work activities teachers often utilize in
their classes, Moodle offers a wide variety of opportunities for
teachers to design these digital learning spaces tailored to the
specific needs and objectives of their classes and students. Following
a student-centred learning paradigm and a conception of the teacher as
a designer and enabler of learning opportunities, we want to present a
few use cases of Moodle activities, plugins and integrated tools
suitable for designing these social spaces online. Among others we
would like to showcase possible scenarios for peer assessments, open
video conferencing rooms for students with BigBlueButton,
collaborative test preparation with StudentQuiz, and connecting
learners through a creative usage of the database activity. Picking up
these different resources, we hope to motivate and inspire educators
to design and roll out collaborative online spaces for their students
to enable better teamwork and achieve deeper learning.
Vortrag im Zuge des Security Monats in Form des FHTW Security Potpourri 2022
Dying Experiments
(2022)
Ziel dieser Studie war das überlagerte Elektromyogramm (ueEMG) und das decompositionierte (dEMG) auf deren Reliabilität und Aussagekraft zu überprüfen. Hierfür vollzogen zehn gesunde männliche Probanden zu drei Testzeitpunkten isometrische Ermüdungsmessungen. Hintergrund Bei der Messung des ueEMG werden die Motor Units Aktionspotentiale (MUAPS) elektrisch überlagert. Physiologische Muskelermüdung zeigt sich hier unter anderem in der Veränderung des Frequenzspektrums und der Signalamplitude. Letzteres steigt bei Ermüdung an, wohingegen die mediane Frequenz (MF) sinkt. Eine weitere Möglichkeit die Muskelermüdung mittels Oberflächen-EMG zu ermitteln ist die Anwendung des dEMGs. Dieses ermöglicht mithilfe einer Mustererkennungssoftware einzelne MUAPS zu erkennen. Studien zeigten, dass aktive Motor Units (MU) bei wiederholten oder anhaltenden submaximalen Kontraktionen als Muskelermüdung ihre Feuerungsrate erhöhen und neue MU rekrutiert werden. Bisher wurden diese Methoden jeweils einzeln betrachtet. Um sicherzustellen welcher Ansatz für klinische Daten geeigneter ist, wurden Datensätze zu beiden Methoden gleichzeitig generiert und hinsichtlich ihrer Aussagekraft und Wiederholbarkeit mit statistischen Mitteln verglichen.