Statistical Profiles of Highly-Rated Learning Objects

Autor/inn/en	Cechinel, Cristian; Sanchez-Alonso, Salvador; Garcia-Barriocanal, Elena
Titel	Statistical Profiles of Highly-Rated Learning Objects
Quelle	In: Computers & Education, 57 (2011) 1, S.1255-1269 (15 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	0360-1315
DOI	10.1016/j.compedu.2011.01.012
Schlagwörter	Discriminant Analysis; Educational Resources; Classification; Automation; Intellectual Disciplines; Evaluation Methods; Profiles; Evaluation Criteria; Computer Uses in Education; Information Technology; Computer System Design; Internet; Information Storage; Computer Software; Computer Software Evaluation + Suchen Sie Ihr Suchwort? Diskriminanzanalyse; Bildungsmittel; Classification system; Klassifikation; Klassifikationssystem; Geisteswissenschaften; Charakterisierung; Profilanalyse; Computernutzung; Informationstechnologie; Informationsspeicherung; Softwareanalyse
Abstract	The continuously growth of learning resources available in on-line repositories has raised the concern for the development of automated methods for quality assessment. The current existence of on-line evaluations in such repositories has opened the possibility of searching for statistical profiles of highly-rated resources that can be used as priori indicators of quality. In this paper, we analyzed 35 metrics in learning objects refereed inside the MERLOT repository and elaborated profiles for these resources regarding the different categories of disciplines and material types available. We found that some of the intrinsic metrics presented significant differences between highly rated and poorly-rated resources and that those differences are dependent on the category of discipline to which the resource belongs and on the type of the resource. Moreover, we found that different profiles should be identified according to the type of rating (peer-review or user) under evaluation. At last, we developed an initial model using linear discriminant analysis to evaluate the strength of relevant metrics when performing an automated quality classification task. The initial results of this work are promising and will be used as the foundations for the further development of an automated tool for contextualized quality assessment of learning objects inside repositories. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2017/4/10