Autors: Ivanova, M. S., G. Grosseck., C. Holotescu.
Title: Analysis and modeling the domain of open educational resources from learning analytics perspective
Keywords: Open educational resources; learning analytics; machine learning; modeling

Abstract: Open Educational Resources (OERs) contributes to ways for knowledge sharing and reusing among given scientific and academic societies. The students and teachers have rapid and unlimited access to content, prepared for different purposes and digital formats. Many projects have been started to extend and improve the existing digital collections. A few analytical tools are developed for automatic identification and monitoring of newly created and added OERs and for tracing the changes in existing OERs. The researchers have applied learning analytics techniques for discovery, analysis and reflection on given characteristics of emerged online content. The aim of the paper is to review, analyze and model the domain of the OERs focusing on the following research questions: What is the current state of research in the area of OERs?, What are typical features of OERs? What kind of tools are used for OERs development, tracing and analysis?



    The 16th International Scientific Conference "eLearning and Software for Education", vol. 3, pp. 66-74, 2020, Romania, Editura Universitara, ISSN: 2066 - 026X/DOI: 10.12753/2066-026X-20-178

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    Цитирания (Citation/s):
    1. Nagaiah, M., Thanuskodi, S., Alagu, A., Application of Lotka's Law to the Research Productivity in the field of Open Educational Resources during 2011-2020, Library Philosophy and Practice, 2021, ISSN: 1522-0222 - 2021 - в издания, индексирани в Scopus или Web of Science

    Вид: публикация в международен форум, индексирана в Scopus