Roman V Esin
Biographic Data
| ID | 222222 |
|---|---|
| NAME | Roman V Esin |
| GIVEN NAMES | Roman V |
| FAMILY NAME | Esin |
| SIGNATURE | ESIN R V |
| AFFILIATIONS | Siberian Federal University |
| ORCID | 0000-0002-9682-4690 |
| VERIFIED | Yes |
| TOTAL WORKS | 4 |
| TOTAL CITATIONS | 0 |
| WORKS AS AUTHOR | 4 |
| WORKS AS EDITOR | 0 |
| FIRST PUBLICATION YEAR | 2019 |
| LATEST PUBLICATION YEAR | 2026 |
| H-INDEX | 0 |
Two-Level Monitoring System for Preventing Academic Failure, Based on Predictive Models and SHAP Analysis
Student dropout remains a critical challenge in higher education, requiring early detection and targeted intervention. This study aims to develop an interpretable two-level monitoring framework for identifying at-risk students—those with academic debts but not yet dismissed—across successive stages of the academic debt lifecycle. Using digital profile data and LMS digital footprints from a large public university (18,192 records covering the year…
Hybrid Approach to Predicting Learning Success Based on Digital Educational History for Timely Identification of At-Risk Students
Student retention is a significant challenge for higher education institutions (HEIs). The fact that a considerable number of dropouts from universities are primarily due to academic underperformance motivates universities to develop learning analytics tools based on models for predicting learning success. However, the scalability of such models is limited since students’ academic performance and engagement, as well as the factors influencing the…
Designing an Education Database in a Higher Education Institution for the Data-Driven Management of the Educational Process
During the past two decades, higher education institutions have been experiencing challenges in transforming the traditional way of in-class teaching into blended learning formats with the support of e-learning technologies that make possible the collection and storing of considerable amounts of data on students. These data have considerable potential to bring digital technologies in education to a new level of personalized learning and data-driv…
Individualisation of Education in Terms of E‐learning
The problem of individualisation in education in terms of e-learning remains relevant despite a lot of research done in the area. The present article is intended to review existing education practice individualisation in e-learning and to determine the prospects of development of adaptive e-learning courses that provide the student with an individual educational environment content which focuses on the individual characteristics of students. The …
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Individualisation of Education in Terms of E‐learning
The problem of individualisation in education in terms of e-learning remains relevant despite a lot of research done in the area. The present article is intended to review existing education practice individualisation in e-learning and to determine the prospects of development of adaptive e-learning courses that provide the student with an individual educational environment content which focuses on the individual characteristics of students. The …
Designing an Education Database in a Higher Education Institution for the Data-Driven Management of the Educational Process
During the past two decades, higher education institutions have been experiencing challenges in transforming the traditional way of in-class teaching into blended learning formats with the support of e-learning technologies that make possible the collection and storing of considerable amounts of data on students. These data have considerable potential to bring digital technologies in education to a new level of personalized learning and data-driv…
Hybrid Approach to Predicting Learning Success Based on Digital Educational History for Timely Identification of At-Risk Students
Student retention is a significant challenge for higher education institutions (HEIs). The fact that a considerable number of dropouts from universities are primarily due to academic underperformance motivates universities to develop learning analytics tools based on models for predicting learning success. However, the scalability of such models is limited since students’ academic performance and engagement, as well as the factors influencing the…
Two-Level Monitoring System for Preventing Academic Failure, Based on Predictive Models and SHAP Analysis
Student dropout remains a critical challenge in higher education, requiring early detection and targeted intervention. This study aims to develop an interpretable two-level monitoring framework for identifying at-risk students—those with academic debts but not yet dismissed—across successive stages of the academic debt lifecycle. Using digital profile data and LMS digital footprints from a large public university (18,192 records covering the year…
Online Learning and Analytics (4 works) · Computer Science (3 works) · Artificial Intelligence (2 works) · Educational Innovations and Challenges (2 works) · Educational technology (2 works) · Higher education (2 works) · Knowledge management (2 works) · Mathematics education (2 works) · Psychology (2 works) · Academic integrity and plagiarism (1 work)