Сибирский федеральный университет

Тип публикации: статья из журнала

Год издания: 2023

Идентификатор DOI: 10.3390/educsci13090947

Ключевые слова: learning analytics, digital footprint, education database, digital educational history, learning success prediction, curriculum analytics, data-driven management

Аннотация: During the past two decades, higher education institutions have been experiencing challenges in transforming the traditional way of in-class teaching into blended learning formats with the support of e-learning technologies that make possible the collection and storing of considerable amounts of data on students. These data have considerable potential to bring digital technologies in education to a new level of personalized learning and data-driven management of the educational process. However, the way data are collected and stored in a typical university makes it difficult to achieve the mentioned goals, with limited examples of data being used for the purposes of learning analytics. In this work, based on the analysis of existing information systems and databases at Siberian Federal University, we propose principles of design for a university database architecture that allow for the development and implementation of a data-driven management approach. We consider various levels of detail of education data, describe the database organization and structure, and provide examples of learning analytics tools that can benefit from the proposed approach. Furthermore, we discuss various aspects of its implementation and associated questions.

Журнал: Education Sciences

Выпуск журнала: Т. 13, № 9

Номера страниц: 947

ISSN журнала: 22277102

Издатель: MDPI AG

• Kustitskaya T.A. (Siberian Federal University)
• Esin R.V. (Siberian Federal University)
• Kytmanov A.A. (Siberian Federal University)
• Zykova T.V. (Siberian Federal University)

• Ядро РИНЦ (eLIBRARY.RU)