vovrВысшее образование в России (Vysshee obrazovanie v Rossii = Higher Education in Russia)Vysshee Obrazovanie v Rossii = Higher Education in Russia0869-36172072-0459Moscow Polytechnic University10.31992/0869-3617-2024-33-5-86-111vovr-4980Research ArticleСтатьиПрименение учебной аналитики в высшем образовании: датасеты, методы и инструментыApplication of Learning Analytics in Higher Education: Datasets, Methods and Toolshttps://orcid.org/0000-0003-1314-5367ДюличеваЮ. Ю.DyulichevaYu. Yu.

Дюличева Юлия Юрьевна   –  канд. физ.-мат. наук, доцент, доцент кафедры прикладной математики Физико-технического  института

Scopus  Author  ID: 56669951600

Researcher ID: R-2515-2017

295007, Республика Крым, Симферополь, пр-т Академика Вернадского, 4

Yulia  Yu.  Dyulicheva  –  Cand.  Sci.  (Mathematics), Associate  Professor,  Associate  Professor  of Applied Mathematics Department of the Physical Technology Institute

Scopus Author ID: 56669951600

Researcher ID: R-2515-2017

4 Vernadsky Ave., Simferopol, 295007

dyulicheva_yu@mail.ruКрымский федеральный университет им. В.И. ВернадскогоРоссияV.I. Vernadsky Crimean Federal UniversityRussian Federation20241906202433586111Copyright © Дюличева Ю.Ю., 20242024Дюличева Ю.Ю.Dyulicheva Y.Y.Данная работа распространяется под лицензией Creative Commons Attribution 4.0.This work is licensed under a Creative Commons Attribution 4.0 License.https://vovr.elpub.ru/jour/article/view/4980

Накапливание  больших  объёмов  образовательных  данных  на  платформах вузов  и  социальных  медиа  приводит  к  необходимости  разработки  инструментов  для  из-влечения закономерностей из образовательных данных, которые могут быть использованы для  понимания  поведенческих  паттернов  обучающихся  и  преподавателей,  для  улучшения методик  преподавания  и  качества  учебного  процесса,  а  также  для  разработки  обоснован-ных стратегий развития вузов и формирования их политики. В данной статье приводится анализ и систематизация датасетов из открытых репозиториев с учётом решаемых на их основе задач учебной аналитики. В частности, в статье отмечается преобладание датасетов, направленных на решение задач аналитики на уровне понимания поведения студентов, в то же время датасеты, направленные на решение задач аналитики на уровне понимания потребностей преподавателей и административно-управленческого персонала вузов, практически отсутствуют. Между тем, полный потенциал инструментов учебной аналитики может быть раскрыт только при внедрении комплексного подхода к анализу образовательных  данных,  учитывающего  потребности  всех  участников  и  организаторов  учебного  процесса.

В предлагаемой обзорной статье рассматриваются методы учебной аналитики для решения задач, связанных с исследованием паттернов социального взаимодействия между обучающимися и преподавателями, и инструменты учебной аналитики  – от внедрения простых дашбордов  до  сложных  фреймворков,  исследующих  различные  уровни  учебной  аналитики. Отмечается, что вузы в целом заинтересованы во внедрении инструментов учебной аналитики,  которые  способны  улучшить  качество  учебного  процесса  за  счёт  разработки  стратегий адресной поддержки отдельных групп обучающихся, однако преподаватели относятся  к  таким  инициативам  с  осторожностью  из-за  недостатка  навыков  анализа  данных  и правильной интерпретации результатов анализа. Новизна данного аналитического обзора связана с рассмотрением учебной аналитики на разных уровнях её реализации в контексте подходов к открытости, обработке и анализу образовательных данных.

Данная статья будет интересна разработчикам инструментов учебной аналитики, научно-педагогическим  работникам,  административно-управленческому  персоналу вузов  с точки зрения формирования представления о целостности процесса аналитики вуза с учётом различных уровней реализации аналитики, направленных на понимание потребностей всех участников учебного процесса.

The  accumulation  of  big  educational  data  on  the  platforms  of  universities  and  social media  leads  to  the  need  to  develop  tools  for  extracting  regularities  from  educational  data,  which can be used for understanding the behavioral patterns of students and teachers, improve teaching methods  and  the  quality  of  the  educational  process,  as  well  as  form  sound  strategies  and  policies for  universities  development. 

This  article  provides  an  analysis  and  systematization  of  datasets  on available repositories, taking into account the learning analytics problems solved on their basis. In particular, the article notes the predominance of datasets aimed at solving analytical problems at the level of student’s behavior understanding, Datasets aimed at solving analytical problems at the level of understanding the needs of teachers and administrative and managerial staff of universities are practically absent. Meanwhile, the full potential of learning analytics tools can only be revealed by introducing an integrated approach to the analysis of educational data, taking into account the needs of all participants and organizers of the educational process.

This  review  article  discusses  learning  analytics  methods  related  to  the  study  of  social  interaction patterns between students and teachers, and learning analytics tools from the implementation of simple dashboards to complex frameworks that explore various levels of learning analytics. The problems and limitations that prevent learning analytics from realizing its potential in universities are considered. It is noted that universities are generally interested in introducing learning analytics tools that can improve the quality of the educational process by developing strategies for targeted support for individual groups of students, however, teachers treat such initiatives with caution due to a lack of data analysis skills and correct interpretation of analysis results. The novelty of this analytical review is associated with the consideration of learning analytics at different levels of its implementation in the context of approaches to openness, processing and analysis of educational data.

This article will be of interest to developers of learning analytics tools, scientific and pedagogical workers, and administrative and managerial staff of universities from the point of view of forming an idea of the integrity of the university analytics process, taking into account various levels of analytics implementation aimed at understanding the needs and requirements of all participants in the educational process.

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The authors declare that there are no conflicts of interest present.