Lexical diversity and CEFR vocabulary in critical academic reading passages: A conceptual replication

Authors

  • Anealka Aziz Akademi Pengajian Bahasa, Universiti Teknologi MARA, 40450 Shah Alam, Selangor, Malaysia
  • Tuan Sarifah Aini Syed Ahmad Akademi Pengajian Bahasa, Universiti Teknologi MARA, Cawangan Negeri Sembilan, Kampus Seremban, 70300 Seremban, Negeri Sembilan, Malaysia.
  • Suryani Awang Akademi Pengajian Bahasa, Universiti Teknologi MARA, Cawangan Kelantan, Kampus Machang, 18500 Machang, Kelantan, Malaysia.
  • Roslina Abdul Aziz Akademi Pengajian Bahasa, Universiti Teknologi MARA, Cawangan Pahang, Kampus Jengka, 26400 Jengka, Pahang, Malaysia.
  • Nurul Afifah Azlan Akademi Pengajian Bahasa, Universiti Teknologi MARA, 40450 Shah Alam, Selangor, Malaysia
  • Siti Nurshafezan Ahmad Akademi Pengajian Bahasa, Universiti Teknologi MARA, 40450 Shah Alam, Selangor, Malaysia

DOI:

https://doi.org/10.24200/jonus.vol11iss2pp228-242

Abstract

Background and Purpose: Critical academic reading skills demand learners to engage in higher-order reading skills which require advanced lexical knowledge. The two dimensions of lexical knowledge, namely lexical diversity and vocabulary variety, however, are mostly assessed in isolation using unstable measurement instruments, hence giving misleading results. The purpose of this study was to examine these two dimensions in critical academic reading passages through an integrated approach using a more reliable measurement instrument.

Methodology: The corpus consisted of 32 selected reading passages. They were first cleaned to remove any interference, such as numbering that might affect the lexical calculation, before being converted to plain text format. The Text Inspector was then used to generate the Measure of Textual Lexical Diversity (MTLD) and CEFR vocabulary difficulty levels for all passages. The scores were then analysed descriptively to examine the patterns of lexical diversity and CEFR vocabulary distributions across the passages. Additionally, Spearman’s rho correlational analysis was conducted to examine the relationship between MTLD scores and CEFR-based vocabulary levels.

Findings: All 32 examined passages generally exhibit high lexical diversity, with many common and basic words. Lexical diversity was found to increase when advanced vocabulary was added to the passage, indicating a significant positive correlation between the two dimensions.

Contributions: The integrated approach in lexical analysis adopted in this study results in a comprehensive picture of lexical complexity rather than using the single metric alone, hence offering a clearer and fairer approach for instructors in evaluating and selecting reading passages to be used with students.

Keywords: CEFR profiling, critical academic reading, lexical difficulty, lexical diversity, Measure of Textual Lexical Diversity (MTLD).

Cite as: Aziz, A., Syed Ahmad, T. S. A., Awang, S., Abdul Aziz, R., Azlan, N. A., & Ahmad, S. N. (2026). Lexical diversity and CEFR vocabulary in critical academic reading passages: A conceptual replication. Journal of Nusantara Studies, 11(2), 228-242. https://dx.doi.org/10.24200/jonus.vol11iss2pp228-242

References

Allagui, B., & Naqbi, S. A. (2024). The contribution of vocabulary knowledge to summary writing quality: Vocabulary size and lexical richness. Teaching English as a Second or Foreign Language, 28(1), 1-27. https://doi.org/10.55593/ej.28109a5

Aylin, Ü., & Bekir, A. (2025). Evaluating text suitability for English for academic purposes reading assessment: Teachers versus Lexile, Coh-Metrix and ChatGPT. British Council.

Bannò, S., Knill, K. M., & Gales, M. (2025). Exploiting the English vocabulary profile for L2 word-level vocabulary assessment with LLMs. Journal of Latex Class Files, 18(9), 1-24. https://doi.org/10.48550/arxiv.2506.02758

Chen, C., & Liu, Y. (2020). The role of vocabulary breadth and depth in IELTS academic reading tests. Reading in a Foreign Language, 32(1), 1-27. https://doi.org/10.17863/cam.51399

Churunina, A. A., Solnyshkina, M., & Yarmakeev, I. (2023). Lexical diversity as a predictor of complexity in textbooks on the Russian language. Russian Language Studies, 21(2), 212-227. https://doi.org/10.22363/2618-8163-2023-21-2-212-227

Dujardin, É., Auphan, P., Bailloud, N., Écalle, J., & Magnan, A. (2021). Tools and teaching strategies for vocabulary assessment and instruction: A review. Social Education Research, 3(1), Article 34. https://doi.org/10.37256/ser.3120221044

Escobar-Acevedo, A., Guerrero-García, J., & Guzmán-Cabrera, R. (2022). A model text recommendation system for engaging English language learners: Facilitating selections on CEFR. Rupkatha Journal on Interdisciplinary Studies in Humanities, 14(3), 1-8. https://doi.org/10.21659/rupkatha.v14n3.17

García-Ostbye, I. C., & Martínez-Sáez, A. (2023). Reading challenges in higher education: How suitable are online genres in English for medical purposes? ESP Today, 11(1), 53-74. https://doi.org/10.18485/esptoday.2023.11.1.3

Gizatulina, D., Ismaeva, F., Solnyshkina, M. I., Мартынова, Е. В., & Yarmakeev, I. (2020). Fluctuations of text complexity: The case of basic state examination in English. SHS Web of Conferences, 88(1), Article 02001. https://doi.org/10.1051/shsconf/20208802001

Hasan, M. K., & Shabdin, A. A. (2016). Conceptualization of depth of vocabulary knowledge with academic reading comprehension. PASAA, 51(1), 235-268. https://doi.org/10.58837/CHULA.PASAA.51.1.9

Hijazi, H., Gomes, M., Castelhano, J., Castelo‐Branco, M., Praça, I., Carvalho, P., & Madeira, H. (2024). Dynamically predicting comprehension difficulties through physiological data and intelligent wearables. Scientific Reports, 14(1), 1-17. https://doi.org/10.1038/s41598-024-63654-z

Jiménez, W. C. (2023). Vocabulary complexity in EFL university students’ academic texts. Revista de Lenguas Modernas, 37(1), 1-22. https://doi.org/10.15517/rlm.v0i37.50826

Kurdi, M. Z. (2020). Text complexity classification based on linguistic information: Application to intelligent tutoring of ESL. Journal of Data Mining & Digital Humanities, 2020(1), 1-40. https://doi.org/10.46298/jdmdh.6012

Liu, H., Shi, X., Qiu, J., Shi, Y., Hao, Y., Zhu, L., Yan, C., & Li, H. (2023). Academic word coverage and language difficulty of reading passages in college English test and test of English for Academic Purposes in China. Frontiers in Psychology, 14(1), 01-11. https://doi.org/10.3389/fpsyg.2023.1171227

McCarthy, P. M., & Jarvis, S. (2010). MTLD, VOCD-D, and HD-D: A validation study of sophisticated approaches to lexical diversity assessment. Behavior Research Methods, 42(2), 381-392. https://doi.org/10.3758/brm.42.2.381

Nation, I. S. P. (2006). How large a vocabulary is needed for reading and listening? The Canadian Modern Language Review, 63(1), 59–82. https://doi.org/10.3138/cmlr.63.1.59

Ng, Y. J., Hussin, A. A., Roslim, N., & Abidin, D. Z. (2023). Vocabulary benchmarking for the comprehension of CEFR-aligned assessment reading texts. Journal of Language and Communication, 10(2), 241-256. https://doi.org/10.47836/jlc.10.02.06

Owen, N., Shrestha, P., & Bax, S. (2021). Researching lexical thresholds and lexical profiles across the common European framework of reference for languages (CEFR) levels assessed in the Aptis test. British Council.

Parashchuk, V., Yarova, L., & Parashchuk, S. (2021). Automated complexity assessment of English informational texts for EFL pre-service teachers and translators. Arab World English Journal, 7(1), 155-164. https://doi.org/10.24093/awej/call7.11

Qiao, Y., Zhou, W., Kerz, E., & Schlüter, R. (2021). The impact of ASR on the automatic analysis of linguistic complexity and sophistication in spontaneous L2 speech. arXiv, 1(1), 1-7. https://doi.org/10.48550/arXiv.2104.08529

Schmitt, N. (2014). Size and depth of vocabulary knowledge: What the research shows. Language Learning, 64(4), 913-951. https://doi.org/10.1111/lang.12077

Solnyshkina, M., Gatiyatullina, G. M., Kupriyanov, R., & Ziganshina, C. (2023). Lexical density as a complexity predictor: The case of Science and Social Studies textbooks. Research Result: Theoretical and Applied Linguistics, 9(1), 11-26. https://doi.org/10.18413/2313-8912-2023-9-1-0-2

Su, Y., Liu, K., Liu, F., Lee, J., & Jin, T. (2023). Lexical complexity in exemplar EFL texts: Towards text adaptation for 12 grades of basic English curriculum in China. International Review of Applied Linguistics in Language Teaching, 62(1), 137-165. https://doi.org/10.1515/iral-2022-0236

Teng, M. F. (2025). Cross-lagged panel analysis of reciprocal effects of metacognitive knowledge and breadth of vocabulary knowledge in a foreign language context. Studies in Second Language Learning and Teaching, 16(1), 133-164. https://doi.org/10.14746/ssllt.44595

Westbrook, C. (2022). The impact of input format on written performance in a listening-into-writing assessment. Journal of English for Academic Purposes, 61(1), Article 101190. https://doi.org/10.1016/j.jeap.2022.101190

Yasin, Z. M., & Ahamad Shah, M. I. (2019). The interactive roles of lexical knowledge and reading strategies on reading comprehension performance. Journal of Nusantara Studies, 4(1), 273-299. https://doi.org/10.24200/jonus.vol4iss1pp273-299

Zhang, X., & Lu, X. (2024). Testing the relationship of linguistic complexity to second language learners’ comparative judgment on text difficulty. Language Learning, 74(3), 672-706. https://doi.org/10.1111/lang.12633

Zhang, X., & Lu, X. (2025). Aligning linguistic complexity with the difficulty of English texts for L2 learners based on CEFR levels. Studies in Second Language Acquisition, 47(5), 1407-1434. https://doi.org/10.1017/s0272263125101125

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Published

2026-07-31

How to Cite

Lexical diversity and CEFR vocabulary in critical academic reading passages: A conceptual replication. (2026). Journal of Nusantara Studies (JONUS), 11(2), 228-242. https://doi.org/10.24200/jonus.vol11iss2pp228-242