SN-Learning: An Exploratory Study beyond E-Learning and Evaluation of Its Applications Using EV-SNL Framework

Autor/inn/en	Krouska, Akrivi; Troussas, Christos; Virvou, Maria
Titel	SN-Learning: An Exploratory Study beyond E-Learning and Evaluation of Its Applications Using EV-SNL Framework
Quelle	In: Journal of Computer Assisted Learning, 35 (2019) 2, S.168-177 (10 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Zusatzinformation	ORCID (Krouska, Akrivi)
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	0266-4909
DOI	10.1111/jcal.12330
Schlagwörter	Social Networks; Guidelines; Electronic Learning; Teacher Student Relationship; Technological Advancement; Intelligent Tutoring Systems; Instructional Improvement; Computer Software Evaluation; Researchers; Artificial Intelligence; Teaching Methods; Computer Assisted Instruction + Suchen Sie Ihr Suchwort? Social network; Soziales Netzwerk; Richtlinien; Teacher student relationships; Lehrer-Schüler-Beziehung; Technological development; Technologische Entwicklung; Intelligentes Tutorsystem; Unterrichtsqualität; Softwareanalyse; Researcher; Forscher; Künstliche Intelligenz; Teaching method; Lehrmethode; Unterrichtsmethode; Computer based training; Computerunterstützter Unterricht
Abstract	Social networks have intruded in human life by providing new technological innovations in a range of fields, including the education. The use of social networks in education has the potential to extend e-learning and to introduce new forms of tutoring, communication, and collaboration between students and instructors. Thus, e-learning is the threshold of Social Networking-based Learning (SN-Learning). SN-Learning consists of a new term, introduced in this paper, and involves e-learning systems with social networking characteristics or learning through social networking platforms. To this direction, the main objective of this paper is to present this new technological advancement emerged nowadays and to evaluate relevant applications of the last decade using our adjusted evaluation framework, EV-SNL, in order to highlight their strengths and weaknesses regarding digital learning. The major finding is that SN-Learning systems focus mainly on the incorporation of social features and do not provide yet personalized and adaptive tutoring. This research provides guidelines to computer science researchers on the design and implementation of SN-Learning platforms using artificial intelligence and modelling techniques. Moreover, it can support teachers of different fields so that they can enhance their instruction with new technologies. There is scope for a lot of improvement. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2020/1/01