MACHINE LEARNING ALGORITHMS FOR PERSONALIZED LEARNING: IMPROVING STUDENT OUTCOMES IN THE DIGITAL AGE
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The integration of machine learning algorithms into educational practices has the potential to transform personalized learning and improve student outcomes. Traditional one-size-fits-all teaching methods often fail to address the diverse needs of students, resulting in disengagement and unequal academic progress. Machine learning offers an adaptive approach, tailoring educational content to individual students’ needs and providing real-time feedback. This research explores the role of machine learning algorithms in personalized learning environments and their impact on improving student performance. The study employs a mixed-methods design, combining quantitative analysis of academic performance with qualitative interviews of students and teachers to assess the effectiveness of AI-driven learning tools. Results indicate significant improvements in student engagement, achievement, and self-regulated learning, with 80% of students showing increased academic performance. However, concerns regarding the depersonalization of feedback and the limitations of AI in addressing emotional and social aspects of learning were noted. The study concludes that while machine learning algorithms can significantly enhance personalized learning and improve student outcomes, they must be integrated alongside human-centered teaching practices to ensure a balanced and holistic educational experience. AI should serve as a complement to, not a replacement for, human interaction in the learning process.
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