Reframing Mathematical Learning Through Computational Thinking

Source abstract

This chapter explores computational thinking (CT) as both a cognitive tool and a pedagogical lens for enhancing self-directed learning (SDL) among university students in mathematics education. Using a qualitative integrative literature review of peer-reviewed studies published between 2010 and 2024 and sourced from Scopus, ERIC, and Web of Science, the literature was thematically analysed. Findings indicate that core CT practices - abstraction, decomposition, algorithmic reasoning, and pattern recognition align strongly with mathematical thinking and support SDL by enabling students to set goals, select strategies, monitor progress, and reflect on problem-solving processes. Empirical evidence further suggests that CT enhances metacognitive regulation and adaptive problem-solving in undergraduate mathematics contexts. The chapter proposes a conceptual framework positioning CT as a catalyst for self-directed and metacognitively informed mathematical learning in higher education and recommends global curriculum design and autonomy-supportive pedagogy.