Hybrid Approach to Predicting Learning Success Based on Digital Educational History for Timely Identification of At-Risk Students
Bibliographic Data
| ID | 22047204 |
|---|---|
| Authors | Tatiana A Kustitskaya (0000-0001-9854-1259, Siberian Federal University), Roman V Esin (0000-0002-9682-4690, Siberian Federal University), Yuliya V Vainshtein (Siberian Federal University), Mikhail V Noskov (Siberian Federal University), М В Носков (0000-0001-8966-3633, Siberian Federal University, corresponding author) |
| Year | 2024 |
| Volume | 14 |
| Issue | 6 |
| Pages | 657 |
| Publication date | 2024-06-17 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Education Sciences (JOURNAL) |
| Journal identifiers | ISSN: 2227-7102 • E-ISSN: 2227-7102 |
| Publisher | MDPI AG (PUBLISHER • IT) |
| DOI | 10.3390/educsci14060657 |
| OpenAlex | W4399736467 |
| Language | EN |
| Citations received | 2 |
| References cited | 42 |
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 them, are largely determined by the educational environment. The article proposes a hybrid approach to forecasting success in completing an academic semester, which involves creating a set of predictive models. Some of the models use historical student data, while others are intended to refine the forecast using current data on student performance and engagement, which are regularly extracted from available sources. Based on this approach, we developed an ensemble of machine learning models and the Markov-process model that simultaneously address the tasks of forecasting success in mastering a course and success in completing a semester. The models utilize digital footprint data, digital educational history, and digital personality portraits of students extracted from the databases of Siberian Federal University, and the resulting ensemble demonstrates a high quality of the forecast. The proposed approach can be utilized by other HEIs as a framework for creating mutually complementary forecasting models based on different types of accessible educational data
Educational technology · Knowledge management · Mathematics education · Multimedia · Computer Science · Innovative Teaching Methods · Online and Blended Learning · Online Learning and Analytics · Psychology
Dropping out of university
Student retention and engagement in higher education
Dropout from Higher Education
Assessing the Impact of a Distance-Based Spatial Factor on Retention in the U.S. Colleges
Designing an Education Database in a Higher Education Institution for the Data-Driven Management of the Educational Process
Early prediction of undergraduate Student's academic performance in completely online learning
Students matter the most in learning analytics
Factors influencing academic performance and dropout rates in higher education
| Citations per year | 2 |
|---|---|
| Citation span | 2026 - 2026 (1) |
| Highly cited | No |