Digital Educational History Database Structure for Solving Learning Analytics Tasks
Based on an analysis of existing information systems and databases of Siberian Federal University, we propose an approach to designing the structure of the university education database, including various levels of detail of student data. We formulate the principles of designing the database archite...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | Russian |
| Published: |
The Fund for Promotion of Internet media, IT education, human development «League Internet Media»
2024-07-01
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| Series: | Современные информационные технологии и IT-образование |
| Subjects: | |
| Online Access: | http://sitito.cs.msu.ru/index.php/SITITO/article/view/1109 |
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| Summary: | Based on an analysis of existing information systems and databases of Siberian Federal University, we propose an approach to designing the structure of the university education database, including various levels of detail of student data. We formulate the principles of designing the database architecture that allow for the development and implementation of a data-based management approach. These principles assume student-centeredness, as well as continuity and consistency of data, which should ensure the correct structure and organization of data storage. We give examples of reports that can be obtained using SQL queries to a database of digital educational history of students are given. As an example of the use of such a database, the problem of calculating the characteristics of the curriculum as the main document defining the structure of the educational program is considered. This approach can be used for such tasks as comparative analysis of curricula or assessing the quality of educational programs. Based on the proposed tools that increase the transparency of the educational process and facilitate the identification of problem areas, it becomes possible to make more informed decisions regarding, for example, the modernization of curricula or individual courses implementing disciplines of the educational program, forming admission targets based on identifying trends in demand for training in various educational programs, identifying critical points of educational programs where the greatest loss of students occurs, etc. An important step here is designing the architecture of the higher education institution database in such a way that data on students is collected and stored in a form that can greatly facilitate the creation of learning analytics tools. |
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| ISSN: | 2411-1473 |