Deep Learning PCA Correlation Analysis of Teacher’s Educational Technology Quality and Teacher’s Professional Competence

Abstract

In contemporary education, the integration of educational technology has become crucial for enhancing teaching quality and fostering student learning outcomes. This study employs a novel approach by combining deep learning techniques with Principal Component Analysis (PCA) to explore the relationship between teachers' educational technology proficiency and their professional competence. The research leverages a dataset comprising various metrics related to teachers' usage of educational technology tools and their performance in professional development programs. Initially, the study preprocesses the data and applies deep learning models to extract high-level features from the complex and heterogeneous dataset. Subsequently, PCA is employed to reduce the dimensionality of the feature space while preserving the underlying structure and variability of the data. Through this process, latent factors representing different aspects of educational technology proficiency and professional competence are identified. The correlation analysis conducted on the reduced feature space reveals intricate relationships between teachers' competency in educational technology and their overall professional capabilities. The findings indicate that certain dimensions of educational technology quality, such as adaptability to new tools and innovative pedagogical approaches, positively correlate with measures of professional competence, including classroom management skills, student engagement, and instructional effectiveness.