SHS Web Conf.
Volume 98, 2021The Third Annual International Symposium “Education and City: Education and Quality of Living in the City” (Education and City 2020)
|Number of page(s)||6|
|Section||Media Space and Digital Technology in Education|
|Published online||09 March 2021|
Digital development trajectory as a tool for improving the quality of education
1 Moscow City University, IT Department, Moscow, Russia
2 Lomonosov Moscow State University, Faculty of Mechanics and Mathematics, Department of Mathematical Logic and Theory of Algorithms, Moscow, Russia
3 FRC “Computer Science and Control” of Russian Academy of Sciences, Axel Berg Institute of Cybernetics and Educational Computing, Moscow, Russia
4 National Research University Higher School of Economics, Institute of Education, Department of Educational Programs, Moscow, Russia
5 University of Sydney, Institutional Analytics and Data Science, Sydney, Australia
* Corresponding author: email@example.com
The present article proposes an approach to using students’ digital development trajectories to improve the quality of education. Designing a digital development trajectory has a positive effect on students’ motivation and active participation in deciding on its formation. The goal of the study is to create a conception of using methods and approaches of educational data mining and machine learning in analyzing and predicting digital development trajectories to improve the educational process quality. Implementation of learning management systems with the opportunity to dynamically track students’ academic performance allows timely correcting gaps appearing in students’ knowledge and thereby reducing the risk of them falling behind in their study group. Recording the obtained results of completed assignments and interactions with learning management systems also benefits teachers who get an opportunity to receive feedback in an implicit form and account for gaps appearing in the curriculum in a processed and aggregated form. The analysis is conducted based on a database of additional education in a primary school (grades 1-3). The present study examines several scenarios of using a digital development trajectory in education including students’ progress in an academic performance trajectory based on their grades, a trajectory in terms of time spent on solving problems to determine their complexity, and a heatmap for determining problematic areas and weak spots in each teacher’s educational course. Based on educational data from a learning management system, the authors present a set of recommendations for teachers on the adaptation of their educational course to students’ abilities promoting the increase of the overall quality of education.
Key words: personal trajectory / data-driven education management / electronic portfolio / educational data mining
© The Authors, published by EDP Sciences, 2021
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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