Designing Mathematics Teaching through Deep Learning Pedagogy: Toward Meaningful, Mindful, and Joyful Learning

Resumo

Mathematics education in the 21st century requires an approach that focuses not only on procedural mastery but also on deep conceptual understanding, critical thinking skills, and the ability to transfer knowledge across various life contexts. This article discusses the design of mathematics learning based on a deep learning approach not in the context of artificial intelligence, but as a pedagogical approach integrating three core principles: meaningful, mindful, and joyful. Meaningful learning is built through contextual activities and open-ended problems that connect mathematical concepts to real-world situations. Reflective and process-aware learning is facilitated through discussion, metacognitive reflection, and evaluation. Meanwhile, a joyful learning environment is created through active participation, open exploration, and recognition of diverse student thinking styles. The theoretical framework includes constructivism, transfer theory, metacognition, and process-based assessment. The implementation of this approach requires a transformation of the teacher's role into a facilitator, a flexible and meaning-oriented curriculum, and a supportive, collaborative learning environment. It is concluded that the deep learning approach, through the principles of meaningful, mindful, and joyful learning, can enhance student engagement, deepen understanding, and foster a positive attitude toward mathematics.

Dados da obra

Tipo

Artigo

Periódico

Journal of Deep Learning, p. 188-202

Ano

2025 (2025-10-31)

Palavras-chave

deep learning pedagogy; mathematics education; meaningful learning; mindful learning; joyful learning; metacognition; transfer of learning; constructivism; process-based assessment; teacher as facilitator; student engagement; student thinking styles; open-ended problems

Temas

Avaliação, Currículo

DOI

10.23917/jdl.v1i2.11969

ISSN

3110-7591

Licença

cc-by

Acesso

Download disponível

Fontes

OpenAlex

Na Biblioteca desde

01/10/2026 · verificada em 03/10/2026