The multitude of software tools is available for the creation of learning resources. However the majority of these tools provided by different software producers do not have a unified mechanism by means of which it would be possible to search and reuse the existing learning resources or their elements. To solve this problem the structures of descriptive data can be used. The aim of this paper is to describe a meta-model of e-learning objects and e-learning formats that could be used in the creation of e-learning materials compatible with various e-learning standards. The meta-data models that are used in widely-known learning resources' repositories and their structure's metadata standards providing cross-system compatibility have been evaluated. The key metadata standards of learning objects were identified and their comparative analysis was performed. The e-learning material logical model was created and the essential demands for elearning object's data repository were defined. The technologies and their provided electronic learning objects' classification systems were investigated for the future development of e-learning materials. The scheme of eLM development process was obtained, which provides the transformation of different modules.
Deep learning algorithms are becoming default solution for application in business processes where recognition, identification and automated learning are involved. For human identification, analysis of various features can be applied. Face feature analysis is most popular method for identification of person in various stages of life, including children and infants. The aim of this research was to propose deep learning solution for long-term identification of children in educational institutions. Previously proposed conceptual model for long-term re-identification was enhanced. The enhancements include processing of unexpected persons’ scenarios, knowledge base improvements based on results of supervised and unsupervised learning, implementation of video surveillance zones within educational institutions and object tracking results’ data chaining between multiple logical processes. Object tracking results are the solution we found for long-term identification realization.
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