A Literature Review on Local Literature as a Medium for Critical and Multicultural Literacy through the Deep Learning Approach

Authors

  • Yayu Anggraini Katili Universitas Negeri Manado
  • Harto S. Malik Universitas Negeri Gorontalo

DOI:

https://doi.org/10.33096/tamaddun.v24i2.1025

Keywords:

local literature, critical literacy, multicultural awareness, deep learning

Abstract

This study investigates how local literature can support the development of critical literacy and multicultural awareness with the deep learning approach. This study uses literature review method by analyzing some relevant studies published between 2020 to 2025. This review analyzed 22 studies published between 2020–2025 from Google Scholar, ERIC, Scopus, and DOAJ. The studies reported about the usage of cultural text, critical literacy practice, multicultural and the principles of deep learning in language learning context. The study presents that local literature provides meaningful context and relate to students’ local traditions, value, norm and social issues. The students can ask questions, differentiate different points of view and reflect their cultural experiences. The studies also indicated that deep learning has significant roles in strengthening the learning process. The elements like character, collaboration and creativity can help students connect with contextual learning. As a result, the students become more active, reflective and have strong cultural awareness. However, this study finds some challenges particularly the weakness of teacher professional training, limitation of learning resources and uneven availabilty of cultural textbook. Even though there were some limitations, the studies reveal that the integrations of local literature and deep learning show strong potential to improve literacy learning and make it more relevant and meaningful for students.

References

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Published

2025-12-12

Issue

Section

Research

How to Cite

A Literature Review on Local Literature as a Medium for Critical and Multicultural Literacy through the Deep Learning Approach. (2025). Tamaddun, 24(2), 397-409. https://doi.org/10.33096/tamaddun.v24i2.1025