LMS cluster data analysis for correcting students' learning

Semen M. Levin

doi:10.1117/12.2669585

5 January 2023 LMS cluster data analysis for correcting students' learning

Semen M. Levin

Proceedings Volume 12564, 2nd International Conference on Computer Applications for Management and Sustainable Development of Production and Industry (CMSD-II-2022); 1256416 (2023) https://doi.org/10.1117/12.2669585
Event: Computer Applications for Management and Sustainable Development of Production and Industry (CMSD2022), 2022, Dushanbe, Tajikistan

Abstract

This article deals with the analysis of data obtained from databases of learning management systems to organize student-centred learning with the application of course adaptation methods by changing the set of asynchronous and synchronous learning tools. The relevance of the research in this area is due to the current teaching paradigm as well as the increasing volume of digital data available to universities. The aim of the study is to investigate the possibility of effectively influencing the educational process based on the results of data aggregated from learning management systems (LMS) using cluster analysis. The results of the study confirm the possibility of dividing students into clusters based on their success in perceiving synchronous and asynchronous educational methods, and the effectiveness of corrective impact on the respective clusters of students to improve academic performance. As a conclusion the author made a statement about the effectiveness of practical everyday application of learning analytics aimed at person-centered learning.

Citation Download Citation

Semen M. Levin "LMS cluster data analysis for correcting students' learning", Proc. SPIE 12564, 2nd International Conference on Computer Applications for Management and Sustainable Development of Production and Industry (CMSD-II-2022), 1256416 (5 January 2023); https://doi.org/10.1117/12.2669585

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available