Constructing an adaptive blended teaching model through big data analytics and machine learning

Abstract

This research investigates the development of an adaptive blended teaching model (ABTM) that employs customized instructional strategies to optimize learning outcomes. The approach harnesses data-driven insights derived from student performance, behavior, and engagement to provide a personalized educational experience tailored to each student’s requirements. The integration of big data analytics and machine learning (ML) in education presents significant potential to transform traditional teaching methodologies. Data is collected from open sources, including engagement scores, assessment results, forum participation, attendance, and study hours. Preprocessing steps include data cleaning, normalization, and handling missing values to ensure data reliability. The term frequency-inverse document frequency (TF-IDF) text mining technique is utilized to extract features from student-generated content, highlighting essential phrases. TF-IDF enables the identification of critical learning themes and areas requiring additional support. A hybrid method, namely, the snow leopard optimized-tuned intelligent CatBoost (SLO-ICatBoost), is deployed for predicting student grades, assessing performance, and enhancing the educational process. The SLO improves the selection of relevant features, while the ICatBoost algorithm classifies students based on their performance patterns and learning behaviors. When compared to conventional teaching techniques, the proposed SLO-ICatBoost method significantly improves precision (0.990), accuracy (0.991), F1-score (0.990), and recall (0.991). Due to its flexibility in accommodating various learning environments and individual requirements, this approach can be applied in diverse educational settings.