Reverse engineering domain ontologies to conceptual data models

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Topic of the Paper: Ontologies and conceptual modellingEngineering Conceptual Data Models from Domain Ontologies: A Critical Evaluation

This paper studies the differences and similarities between domain ontologies and conceptual data models and the role that ontologies can play in establishing conceptual data models during the process of information systems development. A mapping algorithm has been proposed and embedded in a special purpose Transformation Engine to generate a conceptual data model from a given domain ontology. Both quantitative and qualitative methods have been adopted to critically evaluate this new approach. In addition, this paper focuses on evaluating the quality of the generated conceptual data model elements using Bunge-Wand-Weber and OntoClean ontologies. The results of this evaluation indicate that the generated conceptual data model provides a high degree of accuracy in identifying the substantial domain entities along with their attributes and relationships being derived from the consensual semantics of domain knowledge. The results are encouraging and support the potential role that this approach can take part in the process of information system development

Comparison of Effectiveness of Different Learning Technologies

E-learning has become one of the powerful supporting tools that expand traditional teaching in higher education. Designers of learning objects (LOs) for blended learning higher education face number of challenges; one of them is choosing the right technology to develop learning objects. This study adopts the Bloom-Redeker-Guerra (B-R-G) mapping model which guides designers to transform the contents and objectives of a traditional course into a number of suggested LOs for a blended course. The study attempts to empirically validate the first dimension of its evaluation scale which measures the effectiveness of learning objects that targets achieving lower order thinking skills (i.e. Knowledge and Comprehension) according to Bloom's Taxonomy. This paper presents the results of the empirical study that validates the students' learning achievement and students' perceived satisfaction differ for receptive learning objects that have been developed with different learning technologies. The empirical study has been implemented using pretest-posttest experiments, in addition to a questionnaire that measures students' satisfaction. Participants were about 100 Information Technology (IT) students enrolled in different courses. Results show that students' learning achievement and students' perceived satisfaction improve with learning objects designed with advanced learning technologies (according to Guerra scale), hence better achieve the targeted learning objectives