JOURNAL ARTICLE

Facets of Filipino Identity in K-12 Social Studies Textbooks in The Philippines.

  • Published In: International Journal of Interdisciplinary Educational Studies, 2025, v. 20, n. 1. P. 241 1 of 3

  • Database: Education Source Ultimate 2 of 3

  • Authored By: Java, Almar J. 3 of 3

Abstract

Textbooks play an important role in the daily constructs and school teaching activities. They also expose students to the kind of nationality the state desires to form, which, in the long run, contributes to the birth of a collective national identity. This descriptive study examined the representations of the facets of Filipino identity in the elementary K-12 Social Studies textbooks in the Philippines. Anchored on Benedict Anderson's framework on imagined community, both quantitative and qualitative approaches in content analysis were utilized in four textbooks. The analyzed data disclosed ten facets of Filipino identity in four Social Studies textbooks, varying at each grade level. The ten facets of Filipino identity found in four elementary Social Studies textbooks correspond to the themes of the spiral progression curriculum. Thus, the facets of Filipino identity are generally evident in the content of Social Studies textbooks. Therefore, "imagined Filipino communities" are constructed based on the facets of Filipino identity reflected in the content of the Social Studies textbooks. [ABSTRACT FROM AUTHOR]

Additional Information

  • Source:International Journal of Interdisciplinary Educational Studies. 2025/03, Vol. 20, Issue 1, p241
  • Document Type:Article
  • Subject Area:Economics
  • Publication Date:2025
  • ISSN:2327011X
  • DOI:10.18848/2327-011X/CGP/v20i01/241-261
  • Accession Number:185346203
  • Copyright Statement:Copyright of International Journal of Interdisciplinary Educational Studies is the property of Common Ground Research Networks and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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