THE ROLE OF .NET CORE MACHINE LEARNING AND AI IN EDUCATION: PERSONALIZED LEARNING
Keywords:
Personalized Learning, Machine Learning Analytics, Educational Technology, Adaptive Assessment, Student Performance PredictionAbstract
This comprehensive article explores the transformative impact of .NET Core, Machine Learning (ML), and Artificial Intelligence (AI) in revolutionizing personalized learning within educational environments. The article examines various aspects of AI-driven educational technology, including content adaptation, data-driven insights for teachers, predictive modeling for early intervention, automated content recommendations, real-time assessment systems, gamification strategies, and support for diverse learners. Through extensive research across multiple educational institutions, the analysis demonstrates how ML.NET's capabilities have enhanced student engagement, improved learning outcomes, reduced achievement gaps, and enabled more effective teaching methodologies. The findings highlight the significant advancements in personalized education through intelligent content delivery, adaptive assessment systems, and real-time intervention strategies.
References
Amit Das et al., "The Impact of AI-Driven Personalization on Learners' Performance," International Journal of Computer Sciences and Engineering, 11(08):15-22, August 2023. [Online]. Available: https://www.researchgate.net/publication/373424876_The_Impact_of_AI-Driven_Personalization_on_Learners'_Performance
Milad Shahvaroughi Farahani, Ghazal Ghasemi, "Artificial Intelligence in education: A comprehensive study," ResearchGate, June 2024. [Online]. Available: https://www.researchgate.net/publication/381612818_Artificial_Intelligence_in_education_A_comprehensive_study
Yu,P. (2024).The future prospects of deep learning and neural networks: Artificial intelligence's impact on education.Applied and Computational Engineering,33,94-101. [Online]. Available: https://www.ewadirect.com/proceedings/ace/article/view/9961#
N Morze et al., "Implementation of adaptive learning at higher education institutions by means of Moodle LMS," J. Phys.: Conf. Ser. 1840 012062, 2021. [Online]. Available: https://iopscience.iop.org/article/10.1088/1742-6596/1840/1/012062
Sdenka Zobeida Salas-Pilco, Kejiang Xiao, and Xinyun Hu, "Artificial Intelligence and Learning Analytics in Teacher Education: A Systematic Review," Educ. Sci. 2022, 12(8), 569, 6 September 2023. [Online]. Available: https://www.mdpi.com/2227-7102/12/8/569
Abdulgaffar Abubakar Yahaya et al., "Application of Machine Learning in Education: Recent Trends Challenges and Future Perspective.," African - British Journals, Volume 7 , Issue 3 (2024). [Online]. Available: https://abjournals.org/bjcnit/papers/volume-7/issue-3/application-of-machine-learning-in-education-recent-trends-challenges-and-future-perspective/
Christothea Herodotou et al., "A large-scale implementation of predictive learning analytics in higher education: the teachers’ role and perspective," Educational Technology Research and Development, 02 July 2019. [Online]. Available: https://link.springer.com/article/10.1007/s11423-019-09685-0
Kristen Tollan et al., "A Review on Early Intervention Systems," Curr Dev Disord Rep. 2023 Feb 18;10(2):147–153. [Online]. Available: https://pmc.ncbi.nlm.nih.gov/articles/PMC9937857/
Ngai Cheong, "Review and Analysis of Intelligent Recommendation System Using Machine Learning Approaches," ICDTE '22: Proceedings of the 6th International Conference on Digital Technology in Education, Pages 88 - 92, 12 January 2023. [Online]. Available: https://dl.acm.org/doi/10.1145/3568739.3568756
Shristi Shakya Khanal et al., "A systematic review: machine learning based recommendation systems for e-learning," Education and Information Technologies, 14 December 2019. [Online]. Available: https://link.springer.com/article/10.1007/s10639-019-10063-9
D.U. Ebem et al., "Machine Learning-Based Real-Time Feedback Assessment System for Student Performance Prediction in Tertiary Institution," ResearchGate, August 2024. [Online]. Available: https://www.researchgate.net/publication/384276830_Machine_Learning-Based_Real-Time_Feedback_Assessment_System_for_Student_Performance_Prediction_in_Tertiary_Institution
Hassan A. El-Sabagh, "Adaptive e-learning environment based on learning styles and its impact on development students' engagement," International Journal of Educational Technology in Higher Education volume 18, Article number: 53 (2021). [Online]. Available: https://educationaltechnologyjournal.springeropen.com/articles/10.1186/s41239-021-00289-4
Sanmugam, Mageswaran et al., "Gamification as an Educational Technology Tool in Engaging and Motivating Students; An Analyses Review," Advanced Science Letters, Volume 21, Number 10, October 2015, pp. 3337-3341(5). [Online]. Available: https://www.ingentaconnect.com/content/asp/asl/2015/00000021/00000010/art00087;jsessionid=b1418ije3nqsa.x-ic-live-01
Alireza Khakpour & Ricardo Colomo-Palacios, "Convergence of Gamification and Machine Learning: A Systematic Literature Review," Technology, Knowledge and Learning, Volume 26, pages 597–636, (2021). [Online]. Available: https://link.springer.com/article/10.1007/s10758-020-09456-4
Wambui David, "Adaptive Learning Technologies: Customizing Education to Individual Needs," ResearchGate, August 2024. [Online]. Available: https://www.researchgate.net/publication/383273049_Adaptive_Learning_Technologies_Customizing_Education_to_Individual_Needs
Olaf Zawacki-Richter, "Systematic review of research on artificial intelligence applications in higher education – where are the educators?" International Journal of Educational Technology in Higher Education, 28 October 2019. [Online]. Available: https://educationaltechnologyjournal.springeropen.com/articles/10.1186/s41239-019-0171-0
