A formal mechanism for automatic classification of learning objects

Abstract

This article is mainly developed a formal mechanism for automatic classificatíon of a set of learning objects according to the value of sorne indicators in arder to maintain backward in a continuous cycle of improvement. This involves building a measure space, a measure function, a function of dissimilarity and a method of classification. In addition to this, we applíed a measurement process that helps to give greater meaning to the informatíon obtained far each subject and the possible clusters obtained as a result of the classification. This mechanism allows classifying learning objects of different types, tested with different sets of indicators, including sortíng achieved independently of the meanings of the latter. The classification method fails to do so in linear time, which can handle a large number of objects wíthout signifícantly increasing c/assification time. lt does define an efficient procedure for adding or removing objects from the classification, with the goal of eliminating errors in the evaluations of these objects or to update these values because the object has been improved. Se propases that objects are evaluated by a team multidísciplinary, in arder to evaluate different aspects. lt is a/so argued as the mechanism meets a number of key characteristics far rating systems. Final/y it shows how the addition ar removal of objects can make objects change learning a cluster to another, or even disappear clusters.