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September 10, 2025International Journal of Education and Information TechnologiesOpen Access

Student Success Prediction Based on Machine Learning and Learning Styles

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Authors

DODijana OreškiUniversity of Zagreb

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Implication

This analysis demonstrates high prediction accuracy in student success outcomes, suggesting potential for customized learning experiences using machine learning and learning styles in Moodle.

Key Points

The predictive model integrates machine learning with learning style analysis to enhance student success.
The research analyzes student interaction data from Moodle, including logins and assignment submissions, to identify achievement patterns.
Machine learning algorithms are compared to determine the most effective model for predicting academic outcomes.
This work highlights the potential of educational data mining to adapt interventions based on students' learning styles.

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Dijana Oreški (2025) studied this question.

synapsesocial.com/papers/68c1bd4254b1d3bfb60eebba https://doi.org/10.46300/9109.2025.19.10

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Implication

This analysis demonstrates high prediction accuracy in student success outcomes, suggesting potential for customized learning experiences using machine learning and learning styles in Moodle.

Student Success Prediction Based on Machine Learning and Learning Styles

Discussion

Member takes

Implication

Key Points

Cite This Study

Student Success Prediction Based on Machine Learning and Learning Styles

Discussion

Member takes

Implication

Key Points

Cite This Study

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