Exploring the Use of Educational Data Mining and Learning Analytics Through AI to Improve Instructional Practices and Student Performance. Agarwal, N., Babu, Y., Awadh, R., & Mishra, V. In Gupta, A., Hinchey, M., & Zalevsky, Z., editors, Artificial Intelligence and Its Applications, pages 60–71, Cham, 2025. Springer Nature Switzerland. doi abstract bibtex AI learning analytics (AI LA) and educational data mining (EDM) are two new fields that use the power of data analysis to better educational practises and student performance. These approaches give teachers access to information about the behaviour, learning patterns, and performance of their students by analysing big datasets gathered from diverse educational resources. Personalising learning experiences, offering early interventions and assistance, informing curriculum and instructional design, utilising predictive analytics for preventative measures, improving assessment and feedback systems, and guiding institutional decision-making are all possible uses for this information. To achieve proper and efficient implementation, ethical issues like prejudice, consent, and data protection must be properly considered. Overall, educational data mining and AI Learning analytics have enormous potential to alter education and give teachers the tools they need to optimise teaching learning Process.
@inproceedings{agarwal_exploring_2025,
address = {Cham},
title = {Exploring the {Use} of {Educational} {Data} {Mining} and {Learning} {Analytics} {Through} {AI} to {Improve} {Instructional} {Practices} and {Student} {Performance}},
isbn = {978-3-031-84394-5},
doi = {10.1007/978-3-031-84394-5_6},
abstract = {AI learning analytics (AI LA) and educational data mining (EDM) are two new fields that use the power of data analysis to better educational practises and student performance. These approaches give teachers access to information about the behaviour, learning patterns, and performance of their students by analysing big datasets gathered from diverse educational resources. Personalising learning experiences, offering early interventions and assistance, informing curriculum and instructional design, utilising predictive analytics for preventative measures, improving assessment and feedback systems, and guiding institutional decision-making are all possible uses for this information. To achieve proper and efficient implementation, ethical issues like prejudice, consent, and data protection must be properly considered. Overall, educational data mining and AI Learning analytics have enormous potential to alter education and give teachers the tools they need to optimise teaching learning Process.},
language = {en},
booktitle = {Artificial {Intelligence} and {Its} {Applications}},
publisher = {Springer Nature Switzerland},
author = {Agarwal, Nidhi and Babu, Yogendra and Awadh, Ram and Mishra, Vikas},
editor = {Gupta, Anish and Hinchey, Michael and Zalevsky, Zeev},
year = {2025},
keywords = {AI Learning analytics, Educational Data Mining, Instructional Practices, Performance},
pages = {60--71},
}
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