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A Learning Experience Platform (LXP) is an extension of a Learning Management System (LMS) with the emphasis on personalized learning support and learner motivation. In this paper extensions for the well established LMS Moodle are proposed, that include, beyond others, AI supported learning recommendations and user based feedback mechanisms to transform Moodle to Moodle LXP. The architecture of this Moodle LXP provides plug-ins for three types of recommendations, i.e. content-based and two collaborative recommendations. Furthermore, a separate module was developed that is responsible for data storage and calculation of recommended learning resources. Various metadata such as description, number of clicks of learning resources, rating, and feedback of the resources are used for the calculation. The extensions have been integrated into several lectures for three semesters. The system operates effectively and the students use the plug-ins for learning. To learn more about the quality of the recommendations, a mechanism for evaluating the results of the content-based recommendation using feedback from the lecturers was developed.
Open Educational Resources (OER) fördern Lehren und Lernen. Content Curation unterstützt die Integration von OER in Lernumgebungen. Allerdings wächst durch die Einbindung von OER die Zahl an Lerninhalten an, so dass Lernende beim Auffinden passender Lerninhalte unterstützt werden sollten. Zu diesem Zweck bieten sich KI-basierte Lernempfehlungen an, die Lernende in ihrem Lernprozess auf relevante Inhalte hinweisen. In diesem Artikel wird die Erweiterung der Lernplattform Moodle hin zu einer Learning Experience Plattform (LXP) beschrieben, die individuelle Lernerfahrungen für Studierende schaffen soll, indem Content Curation gemeinsam mit KI-basierten Lernempfehlungen integriert werden.
We propose a Moodle-based LXP (Learning Experience Platform) architecture that extends the classical Moodle LMS (Learning Management System) into LXP. The extension of the Moodle LMS to an LXP is developed to improve the learner’s motivation and to enable personalized learning. The first component in our architecture of the Moodle - based LXP is a recommender component based on Artificial Intelligence (AI). It helps learners by proposing appropriate learning resources based on the content they are currently studying. These recommendations are derived from metadata of the learning resources, such as predefined descriptions, number of views, ratings, and comments on the resources.
Selbsttests in Lernmanagementsystemen (LMS) ermöglichen es Studierenden, den eigenen Lernfortschritt einzuschätzen. Im Gegensatz zur Einreichung und Korrektur vollständig ausformulierter Aufgabenlösungen nutzen LMS überwiegend die Eingabe der Lösung im Antwort-Auswahl-Verfahren (Single-Choice). Nach didaktischen Ansatz „Physik durch Informatik“ geben die Lernenden stattdessen ihre Aufgabenlösungen in einer Programmiersprache ins LMS ein, was eine automatisierte Rückmeldung erleichtert und das Erreichen einer höheren Kompetenzstufe fördert. Es wurden zehn LMS-Selbsttests erstellt, bei denen die Lösungen zu einer Lehrbuch-Aufgabenstellung jeweils durch Eingabe in einer Programmiersprache und von einer Kontrollgruppe im Antwort-Auswahl-Verfahren abgefragt wurden. Ergebnisse aus dem ersten Einsatz dieser Selbsttests für die Lehrveranstaltung Physik im Studiengang Biotechnologie werden vorgestellt.
Patients with focal ventricular tachycardia are at risk of hemodynamic failure and if no treatment is provided the mortality rate can exceed 30%. Therefore, medical professionals must be adequately trained in the management of these conditions. To achieve the best treatment, the origin of the abnormality should be known, as well as the course of the disease. This study provides an opportunity to visualize various focal ventricular tachycardias using the Offenburg heart rhythm model. Modeling and simulation of focal ventricular tachycardias in the Offenburg heart rhythm model was performed using CST (Computer Simulation Technology) software from Dessault Systèms. A bundle of nerve tissue in different regions in the left and right ventricle was defined as the focus in the already existing heart rhythm model. This ultimately served as the origin of the focal excitation sites. For the simulations, the heart rhythm model was divided into a mesh consisting of 5354516 tetrahedra, which is required to calculate the electric field lines. The simulations in the Offenburg heart rhythm model were able to successfully represent the progression of focal ventricular tachycardia in the heart using measured electrical field lines. The simulation results were realized as an animated sequence of images running in real time at a frame rate of 20 frames per second. By changing the frame rate, these simulations can additionally be produced at different speeds. The Offenburg heart rhythm model allows visualization of focal ventricular arrhythmias using computer simulations.