Mikhail V Noskov
Biographic Data
| ID | 222223 |
|---|---|
| NAME | Mikhail V Noskov |
| GIVEN NAMES | Mikhail V |
| FAMILY NAME | Noskov |
| SIGNATURE | NOSKOV M V |
| AFFILIATIONS | Siberian Federal University |
| VERIFIED | No |
| TOTAL WORKS | 2 |
| TOTAL CITATIONS | 0 |
| WORKS AS AUTHOR | 2 |
| WORKS AS EDITOR | 0 |
| FIRST PUBLICATION YEAR | 2019 |
| LATEST PUBLICATION YEAR | 2024 |
| H-INDEX | 0 |
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…
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 …
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…
Computer Science (2 works) · Educational technology (2 works) · Mathematics education (2 works) · Online Learning and Analytics (2 works) · Psychology (2 works) · Adaptive Learning (1 work) · Artificial Intelligence (1 work) · Artificial Intelligence in Education (1 work) · E learning (1 work) · Educational Innovations and Challenges (1 work)