Application of data mining techniques for predicting residents' performance on pre-board examinations: A case study

Amirhajlou, Leila; Sohrabi, Zohreh; Alebouyeh, Mardawig; Tavakoli, Nader; Haghighi, Roghye Zare; Hashemi, Akram; Asoodeh, Amir

doi:10.4103/jehp.jehp_394_18

Cited by 15 publications

(1 citation statement)

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“…The author utilizes four machine learning methods (ANN, Decision Tree (DT), Support Vector Machine (SVM), and Naive Bayes (NB)), and neural networks show the best result. A similar solution, but for a medical university in Iran, is presented in [22].…”

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

confidence: 99%