Instructional Framework for the Integration of Peeragogy and Higher-Order Thinking Skills (HOTS) in Chinese Higher Vocational Data Analysis Courses

Abstract

This paper proposes an instructional framework that integrates Peeragogy and Higher-Order Thinking Skills (HOTS) in the design of data analysis courses in Chinese higher vocational education. Grounded in social constructivist theory and Marzano’s higher-order thinking theory, the framework emphasises collaborative learning and authentic data analysis tasks as key mechanisms for fostering advanced cognitive processes. The framework development is guided by three research questions: (1) What peer learning tendency patterns exist among students in Chinese higher vocational data analysis courses? (2) What is the level of students’ HOTS in these courses? and (3) Does the integration of Peeragogy and HOTS significantly improve students’ post-test HOTS performance? The framework contributes theoretically by showing how social and cognitive dimensions of learning integrate to enhance vocational pedagogy. Practically, it provides a model that aligns vocational education with the analytical and collaborative demands of data-driven workplaces. By embedding peer-supported learning and higher-order reasoning into curricula, it transforms teacher-centred practices into learner-centred approaches that develop analytical reasoning, collaborative problem-solving and transferable cognitive skills. For further application, the framework can adopt a mixed-methods research design. This design observes peer learning behaviours, evaluates HOTS through pre- and post-tests, and applies an experimental approach to measure instructional impact.