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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
Learning Management Systems (LMS), such as Moodle, enable the rapid progress of digitisation in teaching, which is no longer only taking place in the lecture hall, but increasingly “online” and asynchronously. New didactic concepts (blended learning, “flipped classroom”) consist of alternating self-learning and face-to-face phases, with the former taking place in the LMS, i.e. online. However, no analysis has yet been carried out as to how students act with the material in the self-learning phase, or the teachers are not provided with any information about the learning progress of the students during the self-learning phase. In this paper, concepts of learning and teaching analytics are presented to answer these questions and to integrate the measures derived from them into the teaching processes in a sustainable manner.
Background: Most clinical studies report the symptoms experienced by those infected with coronavirus disease 2019 (COVID-19) via patients already hospitalized. Here we analyzed the symptoms experienced outside of a hospital setting.
Methods: The Vienna Social Fund (FSW; Vienna, Austria), the Public Health Services of the City of Vienna (MA15) and the private company Symptoma collaborated to implement Vienna's official online COVID-19 symptom checker. Users answered 12 yes/no questions about symptoms to assess their risk for COVID-19. They could also specify their age and sex, and whether they had contact with someone who tested positive for COVID-19. Depending on the assessed risk of COVID-19 positivity, a SARS-CoV‑2 nucleic acid amplification test (NAAT) was performed. In this publication, we analyzed which factors (symptoms, sex or age) are associated with COVID-19 positivity. We also trained a classifier to correctly predict COVID-19 positivity from the collected data.
Results: Between 2 November 2020 and 18 November 2021, 9133 people experiencing COVID-19-like symptoms were assessed as high risk by the chatbot and were subsequently tested by a NAAT. Symptoms significantly associated with a positive COVID-19 test were malaise, fatigue, headache, cough, fever, dysgeusia and hyposmia. Our classifier could successfully predict COVID-19 positivity with an area under the curve (AUC) of 0.74.
Conclusion: This study provides reliable COVID-19 symptom statistics based on the general population verified by NAATs.
Keywords: Chatbot; Machine learning; Self-reported; Symptom assessment; Symptom checker.