Machine learning for classifying learning objects

Building an ontology for learning objects can be useful for translating such objects between learning contexts. Such translations are important because they afford learners and educators with the opportunity to a survey a wide selection of learning and teaching material. For instance, university instructors are sometimes required to assess curriculum from courses delivered from other programs or universities, even internationally. Often, the only learning object available to do so is the course outline made available in HTML format on a web page. Generally there is an abundance of metadata available from such learning objects and this information can be used to generate useful components of the ontology. Other useful information can be derived from first establishing the domain of the object, Electricity and Computing for instance, or possibly History. Once extracted, the information representing learning objects can be stored as elements in an XML. template. The purpose of this work was to develop and implement a machine learning strategy for classifying course outlines into pre-defined domains and sub-domains in order to provide this information to an ontology repository designed to aid in the translation of such objects. First some typical domains were identified. Then, 20-30 course outlines were chosen to represent each sub-domain. Next, frequency tables of words common to the course outlines for a given sub-domain were generated in order to compile an ordered list of synonyms used to represent the sub-domains. Finally, a new set of course outlines were randomly selected for classification based on an analysis of the synonym content of each. Establishing the frequency tables and completing the synonym analysis was automated completely thereby constituting the machine learning strategy.