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Козин С. В., Каган Э. М., Вачкова С. Н. Большие данные для педагогических исследований: возможности, проблемы, ограничения // Вестник ПСТГУ. Серия IV: Педагогика. Психология. 2021. Вып. 63. С. 28-39. DOI: 10.15382/sturIV202163.28-39
Modern educational information systems collect and store a huge amount of data on the learning tracks of schoolchildren and students during the academic year, as well as the actions of the teaching staff. This data makes it possible to analyze the digital footprints of users, study various learning paths, investigate educational materials and their impact on the educational environment, identify information gaps in education, compare the results of knowledge control with preconditions for obtaining them, etc. Nevertheless, the collection and analysis of such data is associated with a large number of difficulties: inconsistent data, data retrieval in source databases, the volume of transfer data, problems associated with data gateways for external consumers, infrastructure performance, visualization and interpretation of such data. This article discusses several cases of using big data analysis for pedagogical research: identification and analysis of the most popular didactic materials created by teachers in the environment of the Moscow Electronic School (MES), analysis of the filling of the tree of didactic units of the thematic framework of the MES library with educational materials, analytics dynamics of the composition of homework by type, identification of teachers and students, the most active users of the MES and analysis of the relationship of this activity with other parameters. In addition, the author describes the problems encountered by the authors at the stage of data transfer and analysis and how to solve them; The main results are generalization of experience with big data MES, identification of opportunities, problems and limitations of big data for the implementation of pedagogical research.Opportunities — analysis of digital methods of users, studying the results of researching digital teaching methods, researching educational materials and their impact on the educational environment, identifying information gaps in education, comparing the results of knowledge control with the conditions for obtaining them, etc. Problems — data search in original undocumented databases, data inconsistency, data visualization, large amount of data, data interpretation. Limitations — data gateways, infrastructure performance, personal data.
big data, data analysis, Moscow Electronic School, MES, digital footprint, data visualization, pedagogical research, Educational Data Mining, homeworks, digital activity
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Kozin Svyatoslav
Place of work: Moscow City University; 14 Panferova ul., Moscow 119261, Russian Federation;
Post: Researcher at the Research Institute of Urban Science and Global Education;
ORCID: 0000-0002-7936-5795;
Email: kozyyy@yandex.ru.
Kagan Edward
Place of work: Moscow City University; 14 Panferova ul., Moscow 119261, Russian Federation;
Post: Researcher at the Research Institute of Urban Science and Global Education;
ORCID: 0000-0002-4317-2123;
Email: kaganem@mgpu.ru.
Vachkova Svetlana
Academic Degree: Doctor of Sciences* in Education;
Academic Rank: Associate Professor;
Place of work: Moscow City University; 14 Panferova ul., Moscow 119261, Russian Federation;
Post: Director of the Research Institute of Urban Science and Global Education;
ORCID: 0000-0002-3136-3336;
Email: svachkova@gmail.com. *According to ISCED 2011, a post-doctoral degree called Doctor of Sciences (D.Sc.) is given to reflect second advanced research qualifications or higher doctorates.