MODELING OF METACOGNITIVE AND MOTIVATIONAL CHARACTERISTICS ON SECONDARY SCHOOL STUDENTS' CONCEPTUAL CHANGE IN LIGHT

Abstract

This study modeled metacognitive and motivational characteristics on secondary school students' conceptual change in light. This study employed a model-based correlational survey research design. The sample size was 384 SS3 Students taking Physics, which was determined using the Taro Yamane formula, but selected using a multi-stage sampling procedure. The students' Metacognitive Characteristics Inventory (SMCCI), the Students' Motivational Characteristics Inventory (SMCI), and the Light Phenomena Concept Test (LPCT), were used for data collection. The estimate of internal consistency for the SMCCI, SMCI, and LPCT was determined using Cronbach's alpha. The internal consistency indices obtained were 0.91, 0.84, and 0.86, respectively. The collected data were analyzed using RStudio version 4.4.3. Specifically, the SEMinR package and plspm. R is a statistical foundation of AI, suited for developing and validating AI models. R contains packages that implement machine learning algorithms that AI often depends on. The research questions were answered using path coefficients (β), effect size (f²), and coefficient of determination (R²). The hypothesis was tested using a bootstrapping procedure. The findings of this study revealed the most parsimonious path model that explains the effects of metacognitive and motivational characteristics on students' conceptual change in light. The metacognitive and motivational characteristics also had significant predictive power for students' conceptual change in light. It implies that, if the students' metacognitive and motivational characteristics are not considered in designing conceptual change instruction for students taking Physics in senior secondary school, students' conceptual change may be hampered, thus promoting poor performance in the subject.