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 - JOUR A1 - Thomas, Max A1 - Staubitz, Thomas A1 - Meinel, Christoph ED - Meinel, Christoph ED - Schweiger, Stefanie ED - Staubitz, Thomas ED - Conrad, Robert ED - Alario Hoyos, Carlos ED - Ebner, Martin ED - Sancassani, Susanna ED - Żur, Agnieszka ED - Friedl, Christian ED - Halawa, Sherif ED - Gamage, Dilrukshi ED - Scott, Jeffrey ED - Kristine Jonson Carlon, May ED - Deville, Yves ED - Gaebel, Michael ED - Delgado Kloos, Carlos ED - von Schmieden, Karen T1 - Preparing MOOChub metadata for the future of online learning BT - optimizing for AI recommendation services JF - EMOOCs 2023 : Post-Covid Prospects for Massive Open Online Courses - Boost or Backlash? N2 - With the growing number of online learning resources, it becomes increasingly difficult and overwhelming to keep track of the latest developments and to find orientation in the plethora of offers. AI-driven services to recommend standalone learning resources or even complete learning paths are discussed as a possible solution for this challenge. To function properly, such services require a well-defined set of metadata provided by the learning resource. During the last few years, the so-called MOOChub metadata format has been established as a de-facto standard by a group of MOOC providers in German-speaking countries. This format, which is based on schema.org, already delivers a quite comprehensive set of metadata. So far, this set has been sufficient to list, display, sort, filter, and search for courses on several MOOC and open educational resources (OER) aggregators. AI recommendation services and further automated integration, beyond a plain listing, have special requirements, however. To optimize the format for proper support of such systems, several extensions and modifications have to be applied. We herein report on a set of suggested changes to prepare the format for this task. KW - Digitale Bildung KW - Kursdesign KW - MOOC KW - Micro Degree KW - Online-Lehre KW - Onlinekurs KW - Onlinekurs-Produktion KW - digital education KW - e-learning KW - micro degree KW - micro-credential KW - online course creation KW - online course design KW - online teaching Y1 - 2023 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:517-opus4-624830 SP - 329 EP - 338 PB - Universitätsverlag Potsdam CY - Potsdam ER -