Global Citizenship Competencies of Filipino Students: Using Machine Learning to Explore the Structure of Cognitive, Affective, and Behavioral Competencies in the 2019 Southeast Asia Primary Learning Metrics

Abstract

While the Philippines is still building its global citizenship curriculum, there are global citizenship competencies already articulated in existing curriculum guides. Using data from a nationally representative sample of Grade 5 students in the Southeast Asia Primary Learning Metrics (SEA-PLM) assessment, we explored Filipino learners’ current global competencies. We used machine learning approaches to determine the best models to predict the six SEA-PLM global competency indices; models generated by Multilayer Perceptrons performed better than other techniques. Shapley Additive Explanations approach was applied to identify variables that had the most impact on the model of each global competency index. Some variables were important predictors across the indices: concern about pollution, feeling connected to people from other countries, beliefs about the importance of learning about other countries, how countries relate to each other, and how natural disasters in other countries affect the Philippines are variables that were associated with global competency indices. Willingness to participate in classroom debates also positively predicted the indices but willingness to participate in classroom elections negatively predicted indices related to knowledge and behavior intention indices. We discuss how patterns in Filipino students’ emerging global competencies can guide curriculum development.

Keywords

• global citizenship education
• global competencies
• education for sustainable development
• Southeast Asia Primary Learning Metrics
• Philippines
• machine learning