Management of the Success of Student’s Learning Based on the Markov Model

Noskov, M. V.; Somova, Marina Valerievna; Fedotova, I. M.

doi:10.32517/0234-0453-2018-33-10-4-11

Cited by 5 publications

(2 citation statements)

References 3 publications

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“…The model for forecasting Learning Success of mastering a Course (model LSC) is the model of the birth-and-death process [50]. It demonstrated high accuracy in predicting the success of learning in subjects accompanied by e-courses.…”

Section: Hybrid Approach To Forecasting the Success Of Learningmentioning

confidence: 99%

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Hybrid Approach to Predicting Learning Success Based on Digital Educational History for Timely Identification of At-Risk Students

Kustitskaya,

Esin,

Vainshtein

et al. 2024

Education Sciences

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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.

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Section: Hybrid Approach To Forecasting the Success Of Learningmentioning

confidence: 99%

“…These intensities represent the parameters of the birth-and-death process and are estimated on historical data. This approach is described in detail in the research papers [50][51][52][53].…”

mentioning

confidence: 99%

Hybrid Approach to Predicting Learning Success Based on Digital Educational History for Timely Identification of At-Risk Students

Kustitskaya,

Esin,

Vainshtein

et al. 2024

Education Sciences

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Mathematical model for early prediction of learning success

Noskov,

Fedotova,

Somova

et al. 2024

Problems in the Textile and Light Industry in the Context of Integration of Science and Industry and Ways to Solve Them: Ptlici

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Prognostic model for assessing the success of subject learning in conditions of digitalization of education

Noskov

Vainshtein

Somova

et al. 2023

Vestn. Ross. univ. družby nar., Ser. Informatiz. obraz.

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Problem statement. One of the approaches to solving the problem of predicting the academic performance of students is displayed. Unlike existing studies in this area, which are mainly aimed at predicting the effectiveness of graduation, that is, based on the results of intermediate certifications that allow us to assess the chances of students to successfully graduate from a university, the results of this study are aimed at predicting the success of education in the early stages of the educational process. Methodology. A feature and novelty of the proposed prognostic model is the forecasting of student performance based on the Markov model, the data sources of which are universal predictors of an e-learning course that determine the success of subject education based on the personal characteristics of the student. Results. The authors present a description of a predictive model for assessing the success of subject education in the context of digitalization of education, reveal their experience of its approbation for students of the Siberian Federal University in the field of study “Informatics and Computer Engineering” and the results of a qualitative assessment of the model. Conclusion. The prospects for building a digital service for predicting the academic performance of students in the electronic information and educational environment of the university based on the results of the study are stated.

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Management of the Success of Student’s Learning Based on the Markov Model

Cited by 5 publications

References 3 publications

Hybrid Approach to Predicting Learning Success Based on Digital Educational History for Timely Identification of At-Risk Students

Hybrid Approach to Predicting Learning Success Based on Digital Educational History for Timely Identification of At-Risk Students

Mathematical model for early prediction of learning success

Prognostic model for assessing the success of subject learning in conditions of digitalization of education

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