Predictive Modeling of Teacher Pedagogical Effectiveness and Its Impact on Knowledge Acquisition in Fundamental Science Education in Textile and Light Industry Higher Education Institutions
Keywords:
Pedagogical effectiveness, predictive modeling, knowledge acquisition, fundamental sciencesAbstract
Teacher pedagogical effectiveness is a critical determinant of student knowledge acquisition, particularly in foundational science education within technical higher education environments such as textile and light industry institutes. This study develops a predictive modeling framework to quantify the influence of pedagogical factors on student learning outcomes using a structured analytical approach. Drawing from computational modeling principles and applied performance evaluation techniques, the research integrates pedagogical assessment indicators, instructional quality parameters, and student achievement metrics into a unified predictive system.
The methodological framework is inspired by analytical optimization models used in engineering systems, where structural efficiency and functional performance are evaluated under constrained conditions (Abdukakhorovich & Muhammadsodikov, 2021). By adapting similar analytical reasoning, the study constructs a pedagogical effectiveness index (PEI) and applies regression-based predictive modeling to estimate student knowledge acquisition outcomes.
The findings suggest that teacher-related variables—such as instructional clarity, engagement intensity, and adaptive teaching strategies—significantly influence cognitive retention and conceptual understanding in fundamental sciences. Furthermore, the predictive model demonstrates strong potential for institutional application in identifying teaching quality gaps and optimizing academic performance strategies. The study contributes to the intersection of educational analytics and computational modeling by providing a structured, data-driven approach to evaluating teaching effectiveness in specialized engineering education contexts.
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Copyright (c) 2026 Dr. Daichi Fukuda, Dr. Emiko Hayashi (Author)
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