Mutawa, Abdullah M.; Muttawa, Jamil Abdul Kareem Al; Sruthi, Sai
In: Applied Sciences, Bd. 13, Ausg. 8, S. 4983, 2023, ISSN: 2076-3417.
Abstract | Links | BibTeX | Schlagwörter: advanced technologies, e-learning, H5P, interactive learning, learning analytics, O
@article{Mutawa2023,
title = {The Effectiveness of Using H5P for Undergraduate Students in the Asynchronous Distance Learning Environment},
author = {Abdullah M. Mutawa and Jamil Abdul Kareem Al Muttawa and Sai Sruthi},
url = {https://doi.org/10.3390/app13084983},
doi = {10.3390/app13084983},
issn = {2076-3417},
year = {2023},
date = {2023-04-15},
journal = {Applied Sciences},
volume = {13},
issue = {8},
pages = {4983},
abstract = {As the COVID-19 pandemic caused many schools to go online, asynchronous distant learning has become popular. One of the main challenges of asynchronous distance learning is keeping students engaged and motivated, as they do not have the same engagement with their peers and teachers as in traditional face-to-face learning environments. HTML 5 package (H5P) is an interactive learning tool that has the potential to fill this need due to its numerous immediate interactive features, such as interactive videos, pop quizzes, and games during media playback. This study investigates the effectiveness of using H5P and Moodle in asynchronous distance learning environments for undergraduate students. The data collection methods included pre-and post-surveys for Moodle and H5P and the questions related to the student perspectives towards H5P features. The technology acceptance model (TAM) is employed to find student satisfaction. The results of this study suggest that both the H5P and Moodle could be valuable tools for making E-learning more effective. The interactive and engaging nature of H5P can provide students with a more enjoyable and effective learning experience, helping to keep them motivated and engaged throughout their studies.},
keywords = {advanced technologies, e-learning, H5P, interactive learning, learning analytics, O},
pubstate = {published},
tppubtype = {article}
}
Hansen, Jan; Rensing, Christoph; Hermann, Oliver; Drachsler, Hendrik
Verhaltenskodex für Trusted Learning Analytics. Version 1.0. Entwurf für die hessischen Hochschulen Diskussionspapier
2020.
Abstract | Links | BibTeX | Schlagwörter: learning analytics
@workingpaper{Hansen2020,
title = {Verhaltenskodex für Trusted Learning Analytics. Version 1.0. Entwurf für die hessischen Hochschulen},
author = {Jan Hansen and Christoph Rensing and Oliver Hermann and Hendrik Drachsler},
url = {https://doi.org/10.25657/02:18903
https://nbn-resolving.org/urn:nbn:de:0111-dipfdocs-189038},
doi = {10.25657/02:18903},
year = {2020},
date = {2020-01-01},
address = {Frankfurt am Main},
organization = {Innovationsforum Trusted Learning Analytics 2020},
abstract = {Dieser Entwurf eines Verhaltenskodex richtet sich an Hochschulen, die mittels Learning Analytics die Qualität des Lernens und Lehrens verbessern wollen. Der Kodex kann als Vorlage zur Erstellung von organisationsspezifischen Verhaltenskodizes dienen. Er sollte an Hochschulen, die Learning Analytics einführen wollen, durch Konsultationen mit allen Interessengruppen überprüft und an die Ziele sowie die bestehende Praxis innerhalb der jeweiligen Hochschulen angepasst werden. Der Kodex wurde auf Grundlage einer Analyse bestehender europäischer Kodizes und der in Deutschland geltenden Rechtsgrundlage vom Innovationsforum Trusted Learning Analytics des hessenweiten Projektes "Digital gestütztes Lehren und Lernen in Hessen" entwickelt.},
keywords = {learning analytics},
pubstate = {published},
tppubtype = {workingpaper}
}
Selwyn, Neil
What’s the Problem with Learning Analytics? Artikel
In: Journal of Learning Analytics, Bd. 6, Nr. 3, S. 11–19, 2019.
Abstract | Links | BibTeX | Schlagwörter: critical discussion, data economy, Education, learning analytics, O, social context
@article{Selwyn2019,
title = {What’s the Problem with Learning Analytics?},
author = {Neil Selwyn},
url = {https://doi.org/10.18608/jla.2019.63.3},
doi = {10.18608/jla.2019.63.3},
year = {2019},
date = {2019-12-13},
journal = {Journal of Learning Analytics},
volume = {6},
number = {3},
pages = {11–19},
abstract = {This article summarizes some emerging concerns as learning analytics become implemented throughout education. The article takes a sociotechnical perspective — positioning learning analytics as shaped by a range of social, cultural, political, and economic factors. In this manner, various concerns are outlined regarding the propensity of learning analytics to entrench and deepen the status quo, disempower and disenfranchise vulnerable groups, and further subjugate public education to the profit-led machinations of the burgeoning “data economy.” In light of these charges, the article briefly considers some possible areas of change. These include the design of analytics applications that are more open and accessible, that offer genuine control and oversight to users, and that better reflect students’ lived reality. The article also considers ways of rethinking the political economy of the learning analytics industry. Above all, learning analytics researchers need to begin talking more openly about the values and politics of data-driven analytics technologies as they are implemented along mass lines throughout school and university contexts.},
keywords = {critical discussion, data economy, Education, learning analytics, O, social context},
pubstate = {published},
tppubtype = {article}
}
Renz, Jan; Rohloff, Tobias; Meinel, Christoph
Automatisierte Qualitätssicherung in MOOCs durch Learning Analytics Konferenzberichte
Proceedings of DeLFI and GMWWorkshops 2017, Chemnitz, Germany, September 5, 2017, 2017, ISSN: 1613-0073.
Abstract | Links | BibTeX | Schlagwörter: A, learning analytics, massive open online courses (MOOCs), Qualitätssicherung, quality
@proceedings{Renz2017,
title = {Automatisierte Qualitätssicherung in MOOCs durch Learning Analytics},
author = {Jan Renz and Tobias Rohloff and Christoph Meinel},
editor = {Carsten Ullrich and Martin Wessner},
url = {http://ceur-ws.org/Vol-2092/paper24.pdf
https://www.researchgate.net/publication/325226167_Automatisierte_Qualitatssicherung_in_MOOCs_durch_Learning_Analytics
https://www.researchgate.net/publication/321105881_Automatisierte_Qualitatssicherung_in_MOOCs_durch_Learning_Analytics},
issn = {1613-0073},
year = {2017},
date = {2017-09-05},
urldate = {2018-12-20},
series = {CEUR Workshop Proceedings},
abstract = {Dieser Beitrag beschreibt wie mithilfe von Learning Analytics Daten eine automatisierte Qualitätssicherung in MOOCs durchgeführt werden kann. Die Ergebnisse sind auch für andere skalierende E-Learning Systeme anwendbar. Hierfür wird zunächst beschrieben, wie in den untersuchten Systemen (die als verteilte Dienste in einer Microservice-Architektur implementiert sind) Learning Analytics Werkzeuge umgesetzt sind. Darauf aufbauend werden Konzept und Implementierung einer automatisierten Qualitätssicherung beschrieben. In einer ersten Evaluation wird die Nutzung der Funktion auf einer Instanz der am HPI entwickelten MOOC-Plattform untersucht. Anschließend wird ein Ausblick auf Erweiterungen und zukünftige Forschungsfragen gegeben.},
howpublished = {Proceedings of DeLFI and GMWWorkshops 2017, Chemnitz, Germany, September 5, 2017},
keywords = {A, learning analytics, massive open online courses (MOOCs), Qualitätssicherung, quality},
pubstate = {published},
tppubtype = {proceedings}
}