Search citation statements
Paper Sections
Citation Types
Year Published
Publication Types
Relationship
Authors
Journals
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.
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.
Цифровизация является одним из ключевых факторов развития современной системы образования. Использование в образовательном процессе цифровых сквозных технологий ведет к накоплению большого количества данных о поведении и результатах обучения студентов в цифровой образовательной среде, анализ которых позволит повысить эффективность и качество их обучения. В статье проанализирована взаимосвязь между успешностью обучения и поведением студентов в цифровой образовательной среде вуза на основе методов образовательной аналитики. При проведении исследования были использованы теоретические, эмпирические и математические методы. Приведены определение образовательных данных, их целевое назначение. Описаны группы методов анализа образовательных данных, рассмотрены примеры применения моделей на их основе в образовательной сфере. Разработана и продемонстрирована модель взаимосвязи итоговой оценки студента и его активности / поведения при обучении на примере онлайн-курса по программированию, размещенного на платформе LMS Moodle. Анализ зависимости проводился как по отдельным показателям, так и по их совокупности. В результате были выделены действия студента, в наибольшей степени влияющие на итоговую оценку. Модель может быть применена для прогноза успешности обучения студентов в цифровой образовательной среде на основе их активности и образовательных результатов с целью повышения эффективности управления процессом обучения в вузах.Ключевые слова: методы анализа данных, цифровая образовательная среда, анализ образовательных данных, регрессионный анализ, образовательные данные, студент, вуз.
Digital assistants are increasingly penetrating various areas of human activity, including education. Today, they are no longer just automated systems or web applications that support and automate certain processes, including educational processes. Now they are more intelligent and more autonomous systems. Digital assistants play a special role in a student’s life, in a sense replacing the dean’s office, mentor, tutor, representatives of other university services and other elements of educational infrastructure. The digital support for the student is important and useful, especially in the first year during his adaptation to the environment of higher education, which is significantly different from the school one. It is at this point that the largest amount of students dropouts occurs due to academic failure. According to the authors, a digital assistant in the form of a mobile application that can predict learning outcomes and inform about it in time, can provide important support for the student and help him/her orient and adjust his/her behavior in case of a threat of a negative result. To solve the problems of creating a predictive model of student learning outcomes and a mobile application that implements it, as well as to conduct a pre-project study, the following methods and tools of mathematical statistics were used: k-means method, Kendall correlation method, Friedman’ test with Durbin—Conover posterior test, linear regression, logistic regression, categorical Bayesian classifier, random forest method, neural network (multilayer perceptron), non-parametric estimation of the Nadaraya—Watson regression function, STATISTICA 10.0 and Jamovi 2.2.5, Python libraries. As a result of the study, a mathematical model for predicting learning outcomes in disciplines based on current performance in e-learning courses was created. The accuracy of the model depends on the week of training in which it is applied and reaches 92,6 %. In the early stages (e. g., for week 7), the accuracy is at least 85 % and varies depending on the contingent of the student population and disciplines. As a result of the study, a mobile application was developed that implements a predictive model and other related functions to inform the student about his/her estimated educational success. The created predictive model is based on current performance data obtained from electronic courses and is capable of making accurate predictions, which allows it to be applied in practice online and through the mobile application to inform students.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
customersupport@researchsolutions.com
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.