Analysis of Metacognitive Profiles to Reduce Dropout in Distance Learning: a Data Science-Based Approach
DOI:
https://doi.org/10.18264/eadf.v15i1.2377Keywords:
Neuroeducation, Metacognition, Data science, Digital education, Cognitive profilesAbstract
This study investigates the integration of neuroeducation, metacognitive analysis, and data science to enhance the digital education (DE) experience. We collected and analyzed data from 1,391 postgraduate students at Estratego College, utilizing a comprehensive questionnaire and the Metacognitive Awareness Inventory (MAI) to assess academic, personal, socioeconomic, and metacognitive dimensions. Data preprocessing and analysis were conducted using Python in Jupyter Notebook, employing K-Means clustering and Support Vector Machine (SVM) classification to identify and categorize metacognitive profiles. Our findings reveal four distinct metacognitive profiles among students, which correlate significantly with their academic performance and engagement in DE. The clustering process, validated through the Elbow Method and Silhouette Analysis, confirmed the optimal formation of these profiles, enhancing our understanding of the diverse learning strategies within the student population. Principal Component Analysis (PCA) was utilized to further refine the data, focusing on the most significant metacognitive attributes that influence learning outcomes. This analysis highlighted specific metacognitive strategies that are pivotal in fostering effective learning environments and reducing dropout rates. The study underscores the potential of combining neuroeducation principles, metacognitive insights, and data science techniques to tailor educational strategies that accommodate diverse learner profiles. By aligning educational content and methodologies with students' cognitive and metacognitive capacities, educators can significantly enhance engagement and efficacy in DE settings, ultimately improving academic success and retention rates. This research contributes to the field by demonstrating how data-driven insights can inform and transform educational practices, offering a model for leveraging technology and cognitive science to meet the evolving demands of digital education.
Keywords: Neuroeducation. Metacognition. Data science. Digital education. Cognitive profiles.
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