Modeling the Satisfaction of Data Literacy Online Training for High School Teachers Using PLS SEM

Background: The gap between before and after the pandemic is incredibly noticeable, especially in education. It mainly changes how schools operate their teaching and learning activities from offline to online. Indonesia must also implement online learning. The basic needs for data literacy in administration are strongly needed, such as inputting data for students' attendance, scores, and many more. Teachers need to improve their data literacy skills to help them evaluate and design new content structures for online teaching to meet students' required satisfaction. Therefore, the teachers’ training program in data literacy always needs to be updated. Objective: This study aims to determine the factors that influence teacher satisfaction in data literacy online training programs Methods: This study employs partial least square structural equation modelling (PLS-SEM) to analyze the factors influencing teacher satisfaction in online data literacy training programs for high school teachers. Results: The results show that the instructor's guidance, support, module content, and experience positively influence learner satisfaction in online data literacy training. The PLS-SEM can explain 62.53% of learner satisfaction Conclusion: Online training providers can consider these variables their primary focus when providing high-quality online training, especially in data literacy. The instructor guidance and support include instructor expertise, the assistance provided, and many more, and the module content and experience include a suitable syllabus for the learner and the ease of use of the learning system.