The purpose of this paper is to present a framework for an adaptive mechanism implemented in Moodle in order to improve learning outcomes and students' satisfaction with the learning process. The proposed mechanism adapts the learning content within the course according to students' characteristics expressed by their learning style. In our study, student's learning style is dynamically determined by monitoring students’ actions and activities during the learning process, and detecting patterns of behavior that correspond to specific learning style. Semantic web technologies are in the background of the entire adaptive system. In order to examine the effectiveness of the proposed model and students' feedback, an evaluation study was conducted on two groups of students. Students from the control group had access to standard Moodle course, while students from experimental group had access to personalized learning content. The results indicated that students' performance was improved by using the proposed framework, while the student's feedback from regarding its usefulness was positive.
Abstract. Detection of synonyms in data modeling is considered as a significant problem, especially within the semantic evaluation of a conceptual data model. This paper presents an approach for synonyms detection in a system for conceptual data model semantic evaluation. It is based on automated reasoning in ontology mapping with conceptual data model with tool that formalizes ontology and conceptual data model and merges them with a set of reasoning rules. Reasoning was done with Prolog system. These rules are created for ontology-toconceptual data model mapping, as well for synonyms extraction. Examples of testing reasoning rules are also shown in the paper.Keywords: synonyms detection, conceptual data model, ontology, reasoning. IntroductionResearch in information system design evaluation has recently received considerable attention in information technology community [1]. In the field of models in information system development [2] introduces a general metrics framework related to syntax, semantic and pragmatic aspect of a model quality evaluation. A comparative analysis and categorization of many systems analysis and design methods has been presented in [3]. Data quality research [4] is related to development of methodologies, frameworks and tools for measurement and improvement of data models and data in databases. Results in this field propose frameworks that define set of quality characteristics, metrics that could measure the level of quality characteristics achievement in particular case and the set of activities to perform in aim to perform measurement and metrics data processing.This paper presents the developed system for synonyms detection within the evaluation of conceptual data models, based on ontology mapping. In the synonyms detection, methods of the composite matching, combined with structural analysis were used. 228
Conceptual data models can change during the information system development and teamwork phases, which require constantly monitoring with synonyms detection. This study elaborates on an approach for detecting synonyms in an entity-relationship model based on mapping with ontological elements. The use of a specific data model validator (DMV) tool enables formalization of the ontology and ER models, as well as their integration with the set of reasoning rules. The reasoning rules enable mapping between formalized elements of the ontology and ER model, and the extraction of synonyms. Formalized elements and reasoning rules are processed within Prolog for the extraction of synonyms. An empirical study conducted by using university student exams demonstrates usability of the proposed approach. The results show effectiveness in extraction of synonyms in all types of conceptual data model elements.
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