Prediction for Student Academic Performance Using SMNaive Bayes Model

Jia, Bo; Niu, Ke; Hou, Xia; Li, Ning; Peng, Xueping; Gu, Peihua; Jia, Ran

doi:10.1007/978-3-030-35231-8_52

Cited by 4 publications

(2 citation statements)

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“…We analysed the dataset of 2459 students from 2003 to 2015 to confirm that random forest outperforms other supervised classification algorithms. Jia et al [49] proposed the SM-Naive-Bayes model to overcome the problem of low accuracy faced by classification techniques to predict student results. The developed model used previous stage course performance to predict future performance.…”

Section: Literature Review a Machine Learning In Educational Data Analysismentioning

confidence: 99%

Guaranteeing Correctness of Machine Learning Based Decision Making at Higher Educational Institutions

et al. 2021

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State-of-the-art software technologies have enabled Higher Education Institutions to record and store large amounts of student data. Analyzing this large amount of data can facilitate the decisionmaking process. Decisions made in higher education institutions affect policies, strategies and actions that improve education quality. It can be argued that machine learning algorithms demonstrate a remarkable ability to recognize models and predict results based on data. However, machine learning outcomes were subject to bias and erroneous labelling. Consequently, to mitigate the impact of such errors in decision making, it is necessary to put in place mechanisms to correct decision rules. This article presents an approach to learn decision rules through supervised machine learning and proves the correctness of these rules with hierarchical Coloured Petri Nets. The use of formalism in the proposed methodology ensures that the decision rules are correct and comprehensible. Empirical results show that we improved correctness with 98.68% accuracy on decision rules. This research work contributes to the improvement of the decisionmaking process for the academic administration of Higher Education Institutes.INDEX TERMS Decision making, supervised machine learning, decision trees, formal verification, formal methods, formal modeling, model checking, coloured petri nets.

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Section: Literature Review a Machine Learning In Educational Data Analysismentioning

confidence: 99%

Guaranteeing Correctness of Machine Learning Based Decision Making at Higher Educational Institutions

et al. 2021

View full text Add to dashboard Cite

show abstract

“…Las indicaciones tempranas sobre el progreso de los estudiantes ayudan a los docentes a optimizar sus estrategias de aprendizaje y a enfocarse en diversas prácticas educativas para que la experiencia de aprendizaje sea exitosa; la aplicación del ML puede ayudar a los docentes a predecir las debilidades esperadas en los procesos de aprendizaje y, como resultado, pueden involucrar proactivamente a dichos estudiantes en una mejor experiencia de aprendizaje. Al igual que en EEUU, las investigaciones emplean técnicas de ML con información de los estudiantes y realizan comparativos para identificar la técnica óptima en términos de precisión (Hellas et al, 2018;Jia et al, 2019;Surenthiran et al, 2021;Suresh et al, 2021).…”

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