TY  - JOUR
A1  - Grum, Marcus
A1  - Sultanow, Eldar
A1  - Friedmann, Daniel
A1  - Ulrich, Andre
A1  - Gronau, Norbert
T1  - Tools des Maschinellen Lernens
BT  - Marktstudie, Anwendungsbereiche & Lösungen der Künstlichen Intelligenz
N2  - Künstliche Intelligenz ist in aller Munde. Immer mehr Anwendungsbereiche werden durch die Auswertung von vorliegenden Daten mit Algorithmen und Frameworks z.B. des Maschinellen Lernens erschlossen. Dieses Buch hat das Ziel, einen Überblick über gegenwärtig vorhandene Lösungen zu geben und darüber hinaus konkrete Hilfestellung bei der Auswahl von Algorithmen oder Tools bei spezifischen Problemstellungen zu bieten. Um diesem Anspruch gerecht zu werden, wurden 90 Lösungen mittels einer systematischen Literaturrecherche und Praxissuche identifiziert sowie anschließend klassifiziert. Mit Hilfe dieses Buches gelingt es, schnell die notwendigen Grundlagen zu verstehen, gängige Anwendungsgebiete zu identifizieren und den Prozess zur Auswahl eines passenden ML-Tools für das eigene Projekt systematisch zu meistern.
Y1  - 2021
SN  - 978-3-95545-380-0
SN  - 978-3-95545-318-7
U6  - https://doi.org/10.30844/grum_2020
PB  - Gito
CY  - Berlin
ER  - 
TY  - GEN
A1  - Vladova, Gergana
A1  - Ullrich, André
A1  - Bender, Benedict
A1  - Gronau, Norbert
T1  - Students’ Acceptance of Technology-Mediated Teaching – How It Was Influenced During the COVID-19 Pandemic in 2020: A Study From Germany
T2  - Postprints der Universität Potsdam Wirtschafts- und Sozialwissenschaftliche Reihe
N2  - In response to the impending spread of COVID-19, universities worldwide abruptly stopped face-to-face teaching and switched to technology-mediated teaching. As a result, the use of technology in the learning processes of students of different disciplines became essential and the only way to teach, communicate and collaborate for months. In this crisis context, we conducted a longitudinal study in four German universities, in which we collected a total of 875 responses from students of information systems and music and arts at four points in time during the spring–summer 2020 semester. Our study focused on (1) the students’ acceptance of technology-mediated learning, (2) any change in this acceptance during the semester and (3) the differences in acceptance between the two disciplines. We applied the Technology Acceptance Model and were able to validate it for the extreme situation of the COVID-19 pandemic. We extended the model with three new variables (time flexibility, learning flexibility and social isolation) that influenced the construct of perceived usefulness. Furthermore, we detected differences between the disciplines and over time. In this paper, we present and discuss our study’s results and derive short- and long-term implications for science and practice.
T3  - Zweitveröffentlichungen der Universität Potsdam : Wirtschafts- und Sozialwissenschaftliche Reihe - 141 
KW  - COVID-19
KW  - digital learning
KW  - discipline differences
KW  - e-learning
KW  - TAM
KW  - technology acceptance
KW  - technology-mediated teaching
KW  - university teaching
Y1  - 2020
U6  - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:517-opus4-521615
SN  - 1867-5808
ER  - 
TY  - JOUR
A1  - Vladova, Gergana
A1  - Ullrich, André
A1  - Bender, Benedict
A1  - Gronau, Norbert
T1  - Students’ acceptance of technology-mediated teaching – How it was influenced during the COVID-19 Pandemic in 2020
BT  - A study from Germany
JF  - Frontiers in psychology / Frontiers Research Foundation
N2  - In response to the impending spread of COVID-19, universities worldwide abruptly stopped face-to-face teaching and switched to technology-mediated teaching. As a result, the use of technology in the learning processes of students of different disciplines became essential and the only way to teach, communicate and collaborate for months. In this crisis context, we conducted a longitudinal study in four German universities, in which we collected a total of 875 responses from students of information systems and music and arts at four points in time during the spring–summer 2020 semester. Our study focused on (1) the students’ acceptance of technology-mediated learning, (2) any change in this acceptance during the semester and (3) the differences in acceptance between the two disciplines. We applied the Technology Acceptance Model and were able to validate it for the extreme situation of the COVID-19 pandemic. We extended the model with three new variables (time flexibility, learning flexibility and social isolation) that influenced the construct of perceived usefulness. Furthermore, we detected differences between the disciplines and over time. In this paper, we present and discuss our study’s results and derive short- and long-term implications for science and practice.
KW  - COVID-19
KW  - digital learning
KW  - discipline differences
KW  - e-learning
KW  - TAM
KW  - technology acceptance
KW  - technology-mediated teaching
KW  - university teaching
Y1  - 2020
U6  - https://doi.org/10.3389/fpsyg.2021.636086
SN  - 1664-1078
VL  - 12
PB  - Frontiers Research Foundation
CY  - Lausanne
ER  -