Abstract: This research article presents a novel approach to personalized education by incorporating the concept of grit and leveraging educational data mining techniques. The study aims to explore how an engineered perspective can enhance the effectiveness of personalized learning interventions in promoting student grit, perseverance, and academic success.
Personalized education has gained significant attention in recent years due to its potential to tailor instruction to individual student needs. However, limited research has investigated the role of non-cognitive factors, such as grit, in personalized learning environments. Grit, defined as the ability to persevere and maintain long-term goals despite obstacles, has been identified as a critical predictor of student success.
Drawing on the principles of educational data mining, this study seeks to identify patterns and develop models for personalized learning that integrate grit measurements. By examining large-scale educational datasets, including student demographic information, academic performance, and self-reported grit measures, this research aims to uncover the relationships between personalized instruction, grit, and student outcomes.
The study employs a mixed-methods design, combining quantitative analyses of educational data with qualitative insights from student and teacher interviews. Through this multi-perspective approach, the research seeks to provide a comprehensive understanding of how personalized education can foster grit in students.
The findings of this research have implications for both researchers and practitioners in the field of education. By exploring the role of grit in personalized learning environments, educators can gain insights into how to design tailored interventions that promote grit development. Additionally, educational policymakers can utilize the results to inform the integration of non-cognitive skills within personalized learning initiatives.
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