Data-Based Teacher Continuing Professional Development Governance Model and Evaluation Protocol for Intelligent Triangulation System Implementation in Elementary Schools

Abstract

The problem of pedagogical and professional competence of elementary school teachers is still high and the use of learning data is ineffective. The urgency of this research is because teachers are data-based decision makers, while the practice of Continuous Professional Development (CPD) in elementary schools is still administrative and lacks real impact. The novelty is the development of a data-based teacher personal knowledge governance model integrated with the Intelligent Triangulation System (ITS), which combines student learning outcome data, learning observations and teacher reflections in a single decision-making framework. The objectives are to analyse the initial condition of teacher competence, develop and implement the model, test its effectiveness, and compile a continuous evaluation protocol. The research approach is a mixed method with a quantitative quasi-experimental design. The research sample consisted of 37 elementary school teachers, 37 students, 3 principals, and 3 supervisors. Data collection techniques include pre-test, post-test, observation, interviews, documentation and ITS usage logs. Data analysis was carried out descriptively, inferentially (paired sample t-test) and triangulation. The results showed an increase in pedagogical competence by 13.85% and professional competence by 14.65% (p<0.05), as well as an increase in student literacy by 13.40% and numeracy by 14.15%. The research findings indicate that the data-driven model is effective in building reflective practices and objective decision-making. In conclusion, the data-driven teacher knowledge governance model with ITS has proven effective, systematic, and sustainable in improving competence. The limitations of the study are that the trial was only conducted in a limited location and there was no control group.