Search

Your search keyword '"Dvoretskaya, Irina"' showing total 5 results
Start Over Author "Dvoretskaya, Irina"
5 results on '"Dvoretskaya, Irina"'

1. School Digitalization from the Teachers' Perspective in Russia

Author

Dvoretskaya, Irina

Abstract

In this study, we would like to show the analysis of latent factor structure of school digitalization from the teachers' perspective based on pilot SELFIE (Self-reflection tool for digitally capable schools) data. The survey of 685 teachers who took part in the pilot SELFIE study in Russia were subjected to an exploratory factor analysis. Five factors of technology-enhanced teaching emerged. Taking into consideration the lack of recent studies in internal drivers of digital transformation in Russia, this study contributes to the understanding of changes happening within a complex system such as the school system is. Further perspectives for digital transformation's aspects analysis based on SELFIE data are also proposed. [For the complete proceedings, see ED600498.]

Published
2018

2. The Smart Education Progress Measurement: Can Field Experts’ Opinions Help?

Author

Dvoretskaya, Irina, Uvarov, Alexander, Kochak, Eren, Huang, Ronghuai, Series Editor, Kinshuk, Series Editor, Jemni, Mohamed, Series Editor, Chen, Nian-Shing, Series Editor, Spector, J. Michael, Series Editor, Yang, Junfeng, editor, Liu, Dejian, editor, Tlili, Ahmed, editor, Chang, Maiga, editor, Popescu, Elvira, editor, Burgos, Daniel, editor, and Altınay, Zehra, editor

Published
2022
Full Text
View/download PDF

4. Unlocking potential: understanding mobile device use patterns among year 9–11 students for learning.

Author

Dvoretskaya, Irina

Subjects
*DIGITAL transformation, *DIGITAL technology, *DIGITAL learning, *AT-risk students, *EDUCATIONAL objectives
Abstract

The study examines the nature of smartphones for learning use patterns among students in grades 9–11 and its contexts in relation to personalised learning extracting risk factors associated with less mobile device use in learning. A latent class analysis (LCA) was employed. Various covariates were used to highlight groups of students at risk for effective smartphone-supported instruction. Participants were recruited for a cross-sectional online survey within the Monitoring of digital transformation series conducted in Russia in 2020/21. We identified four distinct profile groups of mobile device use among school students. Examining profile groups through the lenses of the Personalisation–Pluralisation Quadrant (PPQ) developed by Kucirkova and Littleton and the Substitution, Augmentation, Modification and Redefinition (SAMR) framework of Puentedura, we show that heterogeneous guidance should be provided to help students maximise the potential of smartphones for learning. This study represents a first attempt to examine the heterogeneous structure of mobile use among school students, using a latent class analysis and can be used as a reference to better understand the use of personal mobile devices among school students for educational purposes and personalised learning. The findings also result in targeted approaches to the teachers’ instruction according to the constructivism principles. [ABSTRACT FROM AUTHOR]

Published
2024
Full Text
View/download PDF

5. MAKE USE OF SELFIE DATA TO GENERATE TARGETED RECOMMENDATIONS FOR SCHOOL TEAMS: TWO INSTRUMENTS.

Author

Dvoretskaya, Irina and Uvarov, Alexander

Subjects
DIGITAL technology, KRUSKAL-Wallis Test, TEACHING methods, CLUSTER analysis (Statistics), EDUCATIONAL change
Abstract

As SELFIE (Self-reflection tool for digitally capable schools) piloting has shown, self-assessment of the use of digital technologies (DT) in schools provides school teams with a lot of useful information. Nevertheless, as the feedback highlights, school team members are very interested in also getting the specific targeted recommendations for improving work planning in this area. Here we suggest two practical data-driven approaches for preparing such scale-based recommendations. The first tool is based on Kruskal-Wallis non-parametric test of responses and helps to evaluate if the school develops and supports the shared vision on the integration of DT in the school. It allows us to correspond to the dichotomous scale of a shared vision. It was found out that on the sample level the influence of the shared vision formation is not tangible. The second tool helps to classify across the sample schools, the progress in the use of innovative teaching and learning with DT. Each school was attributed based on the variability of teachers' and school leaders' responses. A cluster analysis was implemented to classify results. Ten clusters obtained were assigned to three out of five levels of the RISC (Reinventing School Coalition) metric/scale. [ABSTRACT FROM AUTHOR]

Published
2020

Catalog

Books, media, physical & digital resources
See catalog results