A Computational Discourse Analysis of Macmallian ELT Language Tree Textbooks of Grades 3 and 4: Using NIP Standford Core’s Named Entity Recognition Method

Authors

  • Momina Khattak Visiting Lecturer, University of Education Lahore, Attock Campus, Punjab, Pakistan
  • Dr. Muhammad Adnan Maqbool Assistant Professor (Education), University of Education, Lahore, Punjab, Pakistan
  • Dr. Fasiha Altaf Assistant Professor, Department of Education, GC Women University Sialkot, Punjab, Pakistan

DOI:

https://doi.org/10.47205/jdss.2025(6-I)93

Keywords:

ELT, NLP Stanford core (v. 4.5.9), NER, ICC, Macmillan textbooks grades 3 and 4

Abstract

This study is conducted to examine the Macmillan ELT textbooks of language tree grades 3 and 4 of Pakistan which teach at private sector schools. The pre-eminent publishing houses in Pakistan are like "Cambridge University press, Peak publisher, Macmillan publishers and Oxford University. This research also investigates the usage of foreign cultural words in ELT textbooks which alienates learners, from their own culture and they are getting confused and cannot resonate with these cultural items in their own. This study is based on descriptive quantitative analysis and analytical observation methods and to further categorize manually NLP identified entities while using one of its feature NER in ICC categorization. The data is afterwards analyzed by statistical methods, including percentage based line graphs and frequency counts. Consequently, this study framework is based on manifest content based quantitative analysis. Which provides assistance to the educators and policymakers towards more culturally responsive textbook selection.

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Published

2025-03-31

Details

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    PDF Downloads: 5

How to Cite

Khattak, M., Maqbool, M. A., & Altaf, F. (2025). A Computational Discourse Analysis of Macmallian ELT Language Tree Textbooks of Grades 3 and 4: Using NIP Standford Core’s Named Entity Recognition Method. Journal of Development and Social Sciences, 6(1), 1059–1071. https://doi.org/10.47205/jdss.2025(6-I)93