@article{CaspariSadeghi, author = {Caspari-Sadeghi, Sima}, title = {Applying Learning Analytics in Online Environments: Measuring Learners' Engagement Unobtrusively}, series = {Frontiers in Education}, journal = {Frontiers in Education}, doi = {10.3389/feduc.2022.840947}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:739-opus4-10440}, pages = {1 -- 6}, abstract = {Prior to the emergence of Big Data and technologies such as Learning Analytics (LA), classroom research focused mainly on measuring learning outcomes of a small sample through tests. Research on online environments shows that learners' engagement is a critical precondition for successful learning and lack of engagement is associated with failure and dropout. LA helps instructors to track, measure and visualize students' online behavior and use such digital traces to improve instruction and provide individualized support, i.e., feedback. This paper examines 1) metrics or indicators of learners' engagement as extracted and displayed by LA, 2) their relationship with academic achievement and performance, and 3) some freely available LA tools for instructors and their usability. The paper concludes with making recommendations for practice and further research by considering challenges associated with using LA in classrooms.}, language = {en} } @phdthesis{Hinz, author = {Hinz, Lieselotte}, title = {Die Rechtsfolgen eines fehlerhaften Massenentlassungsverfahrens : eine am Sinn und Zweck des Verfahrens orientierte und folglich verh{\"a}ltnism{\"a}ßige Anwendung der Unwirksamkeitsfolge}, volume = {2024}, publisher = {Nomos}, address = {Baden-Baden}, isbn = {978-3-7489-4365-5}, doi = {10.5771/9783748943655}, url = {http://nbn-resolving.de/urn:nbn:de:101:1-2405242015169.078961835793}, school = {Universit{\"a}t Passau}, pages = {219 Seiten}, abstract = {In st{\"a}ndiger Rechtsprechung sanktioniert das Bundesarbeitsgericht fehlerhafte Massenentlassungsverfahren mit der Unwirksamkeit der Entlassungen, \S 134 BGB i. V. mit \S 17 KSchG. Die Arbeit untersucht anhand einer dreistufigen Pr{\"u}fung, ob und wie geschehene Fehler zu sanktionieren sind. 1) Fehler sind immer dann zu sanktionieren, wenn sie den Sinn und Zweck des Verfahrens tangieren, wenn sie also relevant sind. 2) Arbeitnehmer:innen k{\"o}nnen sich auf einen Fehler nur berufen, wenn sie von diesem betroffen sind. 3) Erscheint eine Sanktion im konkreten Einzelfall als unangemessen, ist eine Ausnahme vom Unwirksamkeitsverdikt zu gew{\"a}hren - andere, ggf. weniger weitreichende Rechtsfolgen kommen dann aber ebenfalls nicht in Betracht.}, language = {de} } @article{ErdoganHassen, author = {Erdogan, G{\"u}lsah and Hassen, Wiem Fekih}, title = {Charging scheduling of hybrid energy storage systems for EV charging stations}, series = {Energies}, volume = {2023}, journal = {Energies}, number = {16}, publisher = {MDPI}, address = {Basel}, doi = {10.3390/en16186656}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:739-opus4-14128}, pages = {29 Seiten}, abstract = {The growing demand for electric vehicles (EV) in the last decade and the most recent European Commission regulation to only allow EV on the road from 2035 involved the necessity to design a cost-effective and sustainable EV charging station (CS). A crucial challenge for charging stations arises from matching fluctuating power supplies and meeting peak load demand. The overall objective of this paper is to optimize the charging scheduling of a hybrid energy storage system (HESS) for EV charging stations while maximizing PV power usage and reducing grid energy costs. This goal is achieved by forecasting the PV power and the load demand using different deep learning (DL) algorithms such as the recurrent neural network (RNN) and long short-term memory (LSTM). Then, the predicted data are adopted to design a scheduling algorithm that determines the optimal charging time slots for the HESS. The findings demonstrate the efficiency of the proposed approach, showcasing a root-mean-square error (RMSE) of 5.78\% for real-time PV power forecasting and 9.70\% for real-time load demand forecasting. Moreover, the proposed scheduling algorithm reduces the total grid energy cost by 12.13\%.}, language = {en} }