Factor Analysis Affecting the Implementation of the Generative Learning Model with a Cognitive Conflict Strategy in the Computational Physics Course during the COVID-19 Pandemic Era

Abstract

This study aimed to analyze the factor model affecting the implementation of the Generative Learning Model with a Cognitive Conflict Strategy in the Computational Physics Course during the COVID-19 pandemic era. This study used quantitative descriptive data. The research respondents were 105 Physics study program students who took the Computational Physics course for the 2020/2021 academic year. A questionnaire with the Likert Scale used for the survey has been tested for validity by experts and limited tryout. The questionnaire used has high validity and reliability. Data were used for modeling structural equations through Exploration Factor Analysis (EFA). EFA results were used to determine the level of Confirmatory Factor Analysis (CFA) to obtain a complete Structural Equation Modeling. The results show that dynamic interactions and interdependent correlations are formed between variables that affect the implementation of Computational Physics learning. After analyzing the 20 (twenty) variables, it was formed 5 (five) factors affecting the implementation of the Generative Learning Model with a Cognitive Conflict Strategy in the Computational Physics Course. The five influencing factors are 1) the learning syntax and teaching materials used (x1); 2) the activity of expressing ideas (disclosure) and model practice (x2); 3) learning styles and creative thinking (x3); 4) Attitudes and final target score of learning (x4); 5) attitude towards learning materials and learning methods (x5). The five factors produce a model F = 0.366 x1 + 0.161 x2 + 0. 959 x3 + 0.682 x4 + 0. 549 x5.