Abstract
As learning object repositories grow and accumulate resources and metadata, the concern for quality has increased, leading to several approaches for quality assessment. The availability of on-line evaluations in some repositories has opened the opportunity to examine the characteristics of learning objects that are evaluated positively, in search of features that can be used as a priori predictors of quality. This paper reports a preliminary exploration of some learning object attributes that can be automatically analyzed and might serve as quality metrics, using a sample from the MERLOT repository. The bookmarking of learning objects in personal collections was found to be a potential predictor of quality. Among the initial metrics considered, the number of images has been found to be also a predictor in most of the disciplines and the only candidate for the Art discipline. More attributes have to be studied across disciplines to come up with automated analysis tools that have a degree of reliability.
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García-Barriocanal, E., Sicilia, M.Á. (2009). Preliminary Explorations on the Statistical Profiles of Highly-Rated Learning Objects. In: Sartori, F., Sicilia, M.Á., Manouselis, N. (eds) Metadata and Semantic Research. MTSR 2009. Communications in Computer and Information Science, vol 46. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04590-5_10
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DOI: https://doi.org/10.1007/978-3-642-04590-5_10
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