Factors influencing academic performance and dropout rates in higher education

Kocsis, Ádám; Molnár, Gyöngyvér

doi:10.1080/03054985.2024.2316616

Cited by 14 publications

(1 citation statement)

References 64 publications

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“…Summarizing the results of the researchers, we can conclude that the following features have the strongest predictive power for learning success prediction in various studies: average grade, earned credits, and gender [29]; emotional, demographic, academic, students' motivation [30], and general data [31]; learning data in the online environment and records from the LMS [32]. However, when using personal characteristics data and data from the LMS, it is important to consider that self-reported questionnaire data, intended for predicting academic success, may not be as objective compared to LMS data [33], and at the same time, demographic data and entrance exam data are more reliable for predicting learning success than LMS data [34].…”

Section: Literature Review Of the Data And Machine Learning Algorithm...mentioning

confidence: 85%

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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