TY  - CHAP
A1  - Jacqmin, Julien
A1  - Özdemir, Paker Doğu
A1  - Fell Kurban, Caroline
A1  - Tunç Pekkan, Zelha
A1  - Koskinen, Johanna
A1  - Suonpää, Maija
A1  - Seng, Cheyvuth
A1  - Carlon, May Kristine Jonson
A1  - Gayed, John Maurice
A1  - Cross, Jeffrey S.
A1  - Langseth, Inger
A1  - Jacobsen, Dan Yngve
A1  - Haugsbakken, Halvdan
A1  - Bethge, Joseph
A1  - Serth, Sebastian
A1  - Staubitz, Thomas
A1  - Wuttke, Tobias
A1  - Nordemann, Oliver
A1  - Das, Partha-Pratim
A1  - Meinel, Christoph
A1  - Ponce, Eva
A1  - Srinath, Sindhu
A1  - Allegue, Laura
A1  - Perach, Shai
A1  - Alexandron, Giora
A1  - Corti, Paola
A1  - Baudo, Valeria
A1  - Turró, Carlos
A1  - Moura Santos, Ana
A1  - Nilsson, Charlotta
A1  - Maldonado-Mahauad, Jorge
A1  - Valdiviezo, Javier
A1  - Carvallo, Juan Pablo
A1  - Samaniego-Erazo, Nicolay
A1  - Poce, Antonella
A1  - Re, Maria Rosaria
A1  - Valente, Mara
A1  - Karp Gershon, Sa’ar
A1  - Ruipérez-Valiente, José A.
A1  - Despujol, Ignacio
A1  - Busquets, Jaime
A1  - Kerr, John
A1  - Lorenz, Anja
A1  - Schön, Sandra
A1  - Ebner, Martin
A1  - Wittke, Andreas
A1  - Beirne, Elaine
A1  - Nic Giolla Mhichíl, Mairéad
A1  - Brown, Mark
A1  - Mac Lochlainn, Conchúr
A1  - Topali, Paraskevi
A1  - Chounta, Irene-Angelica
A1  - Ortega-Arranz, Alejandro
A1  - Villagrá-Sobrino, Sara L.
A1  - Martínez-Monés, Alejandra
A1  - Blackwell, Virginia Katherine
A1  - Wiltrout, Mary Ellen
A1  - Rami Gaddem, Mohamed
A1  - Hernández Reyes, César Augusto
A1  - Nagahama, Toru
A1  - Buchem, Ilona
A1  - Okatan, Ebru
A1  - Khalil, Mohammad
A1  - Casiraghi, Daniela
A1  - Sancassani, Susanna
A1  - Brambilla, Federica
A1  - Mihaescu, Vlad
A1  - Andone, Diana
A1  - Vasiu, Radu
A1  - Şahin, Muhittin
A1  - Egloffstein, Marc
A1  - Bothe, Max
A1  - Rohloff, Tobias
A1  - Schenk, Nathanael
A1  - Schwerer, Florian
A1  - Ifenthaler, Dirk
A1  - Hense, Julia
A1  - Bernd, Mike
ED  - Meinel, Christoph
ED  - Staubitz, Thomas
ED  - Schweiger, Stefanie
ED  - Friedl, Christian
ED  - Kiers, Janine
ED  - Ebner, Martin
ED  - Lorenz, Anja
ED  - Ubachs, George
ED  - Mongenet, Catherine
ED  - Ruipérez-Valiente, José A.
ED  - Cortes Mendez, Manoel
T1  - EMOOCs 2021
N2  - From June 22 to June 24, 2021, Hasso Plattner Institute, Potsdam, hosted the seventh European MOOC Stakeholder Summit (EMOOCs 2021) together with the eighth ACM Learning@Scale Conference.
Due to the COVID-19 situation, the conference was held fully online.
The boost in digital education worldwide as a result of the pandemic was also one of the main topics of this year’s EMOOCs. All institutions of learning have been forced to transform and redesign their educational methods, moving from traditional models to hybrid or completely online models at scale. The learnings, derived from practical experience and research, have been explored in EMOOCs 2021 in six tracks and additional workshops, covering various aspects of this field. In this publication, we present papers from the conference’s Experience Track, the Policy Track, the Business Track, the International Track, and the Workshops.
KW  - e-learning
KW  - microcredential
KW  - MOOC
KW  - digital education
KW  - experience
KW  - online course design
KW  - online course creation
KW  - higher education
Y1  - 2021
U6  - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:517-opus4-510300
SN  - 978-3-86956-512-5
PB  - Universitätsverlag Potsdam
CY  - Potsdam
ER  - 
TY  - JOUR
A1  - Bethge, Joseph
A1  - Serth, Sebastian
A1  - Staubitz, Thomas
A1  - Wuttke, Tobias
A1  - Nordemann, Oliver
A1  - Das, Partha-Pratim
A1  - Meinel, Christoph
T1  - TransPipe
BT  - A Pipeline for Automated Transcription and Translation of Videos
JF  - EMOOCs 2021
N2  - Online learning environments, such as Massive Open Online Courses (MOOCs), often rely on videos as a major component to convey knowledge. However, these videos exclude potential participants who do not understand the lecturer’s language, regardless of whether that is due to language unfamiliarity or aural handicaps. Subtitles and/or interactive transcripts solve this issue, ease navigation based on the content, and enable indexing and retrieval by search engines. Although there are several automated speech-to-text converters and translation tools, their quality varies and the process of integrating them can be quite tedious. Thus, in practice, many videos on MOOC platforms only receive subtitles after the course is already finished (if at all) due to a lack of resources. This work describes an approach to tackle this issue by providing a dedicated tool, which is closing this gap between MOOC platforms and transcription and translation tools and offering a simple workflow that can easily be handled by users with a less technical background. The proposed method is designed and evaluated by qualitative interviews with three major MOOC providers.
Y1  - 2021
U6  - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:517-opus4-516943
VL  - 2021
SP  - 79
EP  - 94
PB  - Universitätsverlag Potsdam
CY  - Potsdam
ER  - 
TY  - GEN
A1  - Bartz, Christian
A1  - Yang, Haojin
A1  - Bethge, Joseph
A1  - Meinel, Christoph
T1  - LoANs
BT  - Weakly Supervised Object Detection with Localizer Assessor Networks
T2  - Computer Vision – ACCV 2018 Workshops
N2  - Recently, deep neural networks have achieved remarkable performance on the task of object detection and recognition. The reason for this success is mainly grounded in the availability of large scale, fully annotated datasets, but the creation of such a dataset is a complicated and costly task. In this paper, we propose a novel method for weakly supervised object detection that simplifies the process of gathering data for training an object detector. We train an ensemble of two models that work together in a student-teacher fashion. Our student (localizer) is a model that learns to localize an object, the teacher (assessor) assesses the quality of the localization and provides feedback to the student. The student uses this feedback to learn how to localize objects and is thus entirely supervised by the teacher, as we are using no labels for training the localizer. In our experiments, we show that our model is very robust to noise and reaches competitive performance compared to a state-of-the-art fully supervised approach. We also show the simplicity of creating a new dataset, based on a few videos (e.g. downloaded from YouTube) and artificially generated data.
Y1  - 2019
SN  - 978-3-030-21074-8
SN  - 978-3-030-21073-1
U6  - https://doi.org/10.1007/978-3-030-21074-8_29
SN  - 0302-9743
SN  - 1611-3349
VL  - 11367
SP  - 341
EP  - 356
PB  - Springer
CY  - Cham
ER  -