Nowadays, the comparison of educational courses and modules is performed manually by experts in the field of education. The main objective of this research work is to create an approach for the automation of this process. The main contribution of this work is a novel, ontology alignment-based methodology for the automated comparison of academic courses and modules, belonging to the cognitive learning domain. The results of this work are appropriate for such tasks as prior learning and degree recognition, the introduction of joint educational programmes and quality assurance in higher education institutions. The set-theoretical models of an educational course, its modules, learning outcomes and keywords were created and converted to the ontology. The choice of the information to be presented in the ontology was based on the careful analysis of programme specifications, module templates and Bologna recommendations for the comparison of educational courses. Ontology was chosen as the data model due to its ability to formally specify semantics, to represent taxonomies and to make inferences regarding data. The formal grammars of a keyword and a learning outcome were created to enable the semi-automated population of the ontology from the module templates. The corresponding annotators were designed in the General Architecture for Text Engineering 6.1. The algorithm for the comparison of educational courses and modules was based on the alignment of ontologies of their keywords and learning outcomes. A novel measure for calculating the similarity between the action verbs in the learning outcomes was introduced and was utilised. Both the measure and the algorithm were implemented in Java. For evaluation purposes, we utilised the module templates from the De Montfort and the Bauman Moscow State Technical Universities. The automatically produced annotations of the keywords and the learning outcomes were evaluated against a manually created gold standard. The high values of the precision, recall and f-measure proved their quality and their suitability for the task. The results produced by the alignment algorithm were compared with those produced by human judgement. The results returned by the experts and the algorithm were comparable, thus showing that the proposed approach is applicable for the partial automation of the comparison of educational modules and courses.
| Identifer | oai:union.ndltd.org:bl.uk/oai:ethos.bl.uk:613553 |
| Date | January 2014 |
| Creators | Chernikova, Elena |
| Publisher | De Montfort University |
| Source Sets | Ethos UK |
| Detected Language | English |
| Type | Electronic Thesis or Dissertation |
| Source | http://hdl.handle.net/2086/10107 |
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