Abstract
With the increasing number of learning objects (LOs), the possibility of their fast and effective retrieving and storing has become a more critical issue. The classification of LOs enables users to search for, access, and reuse them in an effective and efficient way. In this article, the multi-label learning approach is represented for classifying and ranking multi-labeled LOs, whereas each LO might be associated with multiple labels as opposed to a single-label approach. A comprehensive overview of the common fundamental multi-label classification algorithms and metrics will be discussed. In this article, a new multi-labeled LOs dataset will be created and extracted from ARIADNE Learning Object Repository. We experimentally train four effective multi-label classifiers on the created LOs dataset and then, assess their performance based on the results of 16 evaluation metrics. The result of this article will answer the question of; what is the best multi-label classification algorithm for classifying multi-labeled LOs? (c) 2016 Wiley Periodicals, Inc. Comput Appl Eng Educ 24:651-660, 2016; View this article online at ; DOI