A review on Student Performance Prediction using Educational Data mining and Artificial Intelligence

Pawar, Poonam S; Jain, Rajashree

doi:10.1109/temsmet53515.2021.9768773

Cited by 2 publications

(1 citation statement)

References 45 publications

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“…Educational Data Mining (EDM) methods can help in the early detection of students at risk of poor academic progress and dropping out, can enhance the personalization of learning, and are key for visualizing academic data in a variety of ways. Recently, Pawar & Jain [21] reported in a systematic review that techniques of data mining and artificial intelligence (IA) are effective to predict student performance.…”

Section: Educational Data Mining (Edm)mentioning

confidence: 99%

Predicting students’ academic progress and related attributes in first-year medical students: an analysis with artificial neural networks and Naïve Bayes

Monteverde-Suárez,

González-Flores,

Santos-Solórzano

et al. 2024

BMC Med Educ

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Background Dropout and poor academic performance are persistent problems in medical schools in emerging economies. Identifying at-risk students early and knowing the factors that contribute to their success would be useful for designing educational interventions. Educational Data Mining (EDM) methods can identify students at risk of poor academic progress and dropping out. The main goal of this study was to use machine learning models, Artificial Neural Networks (ANN) and Naïve Bayes (NB), to identify first year medical students that succeed academically, using sociodemographic data and academic history. Methods Data from seven cohorts (2011 to 2017) of admitted medical students to the National Autonomous University of Mexico (UNAM) Faculty of Medicine in Mexico City were analysed. Data from 7,976 students (2011 to 2017 cohorts) of the program were included. Information from admission diagnostic exam results, academic history, sociodemographic characteristics and family environment was used. The main dataset included 48 variables. The study followed the general knowledge discovery process: pre-processing, data analysis, and validation. Artificial Neural Networks (ANN) and Naïve Bayes (NB) models were used for data mining analysis. Results ANNs models had slightly better performance in accuracy, sensitivity, and specificity. Both models had better sensitivity when classifying regular students and better specificity when classifying irregular students. Of the 25 variables with highest predictive value in the Naïve Bayes model, percentage of correct answers in the diagnostic exam was the best variable. Conclusions Both ANN and Naïve Bayes methods can be useful for predicting medical students’ academic achievement in an undergraduate program, based on information of their prior knowledge and socio-demographic factors. Although ANN offered slightly superior results, Naïve Bayes made it possible to obtain an in-depth analysis of how the different variables influenced the model. The use of educational data mining techniques and machine learning classification techniques have potential in medical education.

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Section: Educational Data Mining (Edm)mentioning

confidence: 99%