@conference {,
	title = {Beyond viewing counts, likes \& Co - An Analysis of Instructional Videos on Youtube},
	booktitle = {Proceedings of the 14th International Conference on Computer Supported Education (CSEDU 2022) : Vol. 1. 14th International Conference on Computer Supported Education},
	year = {2022},
	pages = {236-241},
	publisher = {Science and Technology Publications},
	organization = {Science and Technology Publications},
	address = {Set{\'u}bal},
	abstract = {<p>The aim of this study is to open up criteria of instructional videos through a search of the scientific literature. Shoufan (2019) tested the Cognitive Value (VCV) of instructional videos as a function of cognitive and other characteristics. Here, the VCV is formed from the likes and viewing counts. Based on Shoufan\&rsquo;s results, the present study aims to answer the following two research questions: 1. Can comparable correlations be reproduced for instructional videos from another subject area? 2. Can the correlations be further explained by criteria regarding the instructional design? For this purpose, 100 videos using the YouTube API were collected. Multiple linear regression analyses were performed. However, only 9\% could be explained by cognitive characteristics and 5\% by instructional design criteria of the total variance in VCV. The recent study could not reproduce Shoufan\&$\#$39;s results and supplementary criteria could not explain further data variance. With regard to the literature, criteria such as likes and viewing counts cannot describe the instructional quality ratings of instructional videos. More promising approaches to evaluate quality perception of instructional videos (e.g., comments) are mentioned.</p>
},
	keywords = {Explanatory Video, Instructional Video, Learning Analytics, Youtube},
	isbn = {978-989-758-562-3},
	issn = {2184-5026},
	doi = {10.5220/0011032800003182},
	author = {Miriam Mulders and Johannes Wendt}
}
@conference {9277,
	title = {Potentials of visual analytics to support (social) online learning processes},
	booktitle = {JURE},
	year = {2017},
	address = {Tampere, Finland},
	keywords = {3C-Model, Learning Analytics, Visual Analytics},
	author = {Hayit, Darya and Martin Rehm and Michael Kerres}
}
@proceedings {6771,
	title = {Visualizing online (social) learning processes - Designing a Dashboard to support reflection},
	year = {2016},
	address = {Lyon},
	abstract = {Learning analytics, as a means to visualize learning, has been repeatedly suggested to enhance learners{\textquoteright} and teachers{\textquoteright} self-reflection in online learning processes. Departing from this notion, we propose a combination of this visual approach to learning analytics with the concept of social presence, thereby acknowledging social aspects of online learning processes that are often overlooked. More specifically, we present the considerations and design of a dedicated dashboard that supports self-reflection by visualizing (social) online learning processes. The approach is based on our belief that visualizing learning by itself does not automatically lead to self-reflection and awareness among students and teachers. Instead, organizers and instructors of learning activities need to be conscious about the social aspects of learning.},
	keywords = {Dashboard, Learning Analytics, Online Learning, social presence, Visual Analytics},
	url = {http://ceur-ws.org/Vol-1736/},
	author = {Hayit, Darya and Tobias H{\"o}lterhof and Martin Rehm and Oskar Carl and Michael Kerres},
	editor = {Milos Kravcik and Alexander Mikroyannidis and Viktoria Pammer and Michael Prilla and Daniel T. Ullmann}
}
