An Analysis of Morphological Errors in Arabic Thesis Abstracts at a University Level

DOI: https://doi.org/10.33650/ijatl.v9i1.10924

Authors (s)


(1) * Muhamad Fuad Hasim   (Univesitas Islam Negeri Sunan Ampel, Surabaya)  
        Indonesia
(2)  Muflihah Muflihah   (Univesitas Islam Negeri Sunan Ampel, Surabaya)  
        Indonesia
(3)  M. Baihaqi   (Univesitas Islam Negeri Sunan Ampel, Surabaya)  
        Indonesia
(4)  Muhammad Iqbal Fuadhi   (Univesitas Islam Negeri Sunan Ampel, Surabaya)  
        Indonesia
(5)  Nur Islamiyah   (Univesitas Islam Negeri Sunan Ampel, Surabaya)  
        Indonesia
(*) Corresponding Author

Abstract


Morphological competence plays a crucial role in producing academically sound Arabic writing, especially for non-native learners in higher education. This study aims to analyze and categorize morphological errors found in the thesis abstracts of students in the Arabic Language Education Study Program at IAIN Kediri. Using a qualitative descriptive method with a content analysis approach, the research focuses on identifying dominant patterns of morphological mistakes and offering strategic insights to improve linguistic accuracy in students’ final academic projects. The data consisted of three randomly selected abstracts from 2023–2024 graduates, which served as the primary source, supported by secondary sources such as books and articles related to Arabic morphology. The analysis process involved four stages: data collection, error identification, explanation, and evaluation. The results revealed four major categories of morphological errors: incorrect word formation, misapplication of wazan (word patterns), inaccurate use of various types of hamzah, and improper placement of ḍamīr (pronouns). These findings suggest the need for targeted instructional interventions to reinforce students’ understanding of Arabic morphological structures in academic contexts.

Keywords

Morphology, Error Analysis, Arabic Education



Full Text: PDF



References


Abdalla, F., & Mahfoudhi, A. (2023). Verb Agreement Production in Arabic-speaking Children with Developmental Language Disorder. Language Acquisition, 31(3), 224–245. https://doi.org/10.1080/10489223.2023.2231924

Al-Rfooh, B., Abandah, G., & Al-Rfou, R. (2023). Fine-Tashkeel: Fine-Tuning Byte-Level Models for Accurate Arabic Text Diacritization. In 2023 IEEE Jordan International Joint Conference on Electrical Engineering and Information Technology, JEEIT 2023 (pp. 199–204). https://doi.org/10.1109/JEEIT58638.2023.10185725

Al-Sultan, M. S. A., & Alzain, I. A. A. (2024). Analysis of Language Errors in Drafting Health Instructions in Arabic: Maternity and Children Hospital in Buraydah as an Example. Dirasat: Human and Social Sciences, 51(2), 572–583. https://doi.org/10.35516/hum.v51i2.3572

Al-Thubaity, A., Alkhalifa, A., Almuhareb, A., & Alsanie, W. (2020). Arabic Diacritization Using Bidirectional Long Short-Term Memory Neural Networks with Conditional Random Fields. IEEE Access, 8, 154984–154996. https://doi.org/10.1109/ACCESS.2020.3018885

Alhafni, B., Inoue, G., Khairallah, C., & Habash, N. (2023). Advancements in Arabic Grammatical Error Detection and Correction: An Empirical Investigation. In EMNLP 2023 - 2023 Conference on Empirical Methods in Natural Language Processing, Proceedings (pp. 6430–6448). https://doi.org/10.18653/v1/2023.emnlp-main.396

Alkhatlan, A., Kateb, F., & Kalita, J. (2020). Attention-Based Sequence Learning Model for Arabic Diacritic Restoration. In Proceedings - 2020 6th Conference on Data Science and Machine Learning Applications, CDMA 2020 (pp. 7–12). https://doi.org/10.1109/CDMA47397.2020.00007

Alkhurayyif, Y., & Sait, A. R. W. (2023). Developing an Open Domain Arabic Question Answering System Using a Deep Learning Technique. IEEE Access, 11, 69131–69143. https://doi.org/10.1109/ACCESS.2023.3292190

Asadi, I. A., Vaknin‑Nusbaum, V., & Taha, H. (2023). The Role of Morphological Decomposition in Reading Complex Words in Arabic in Elementary School Years. Journal of Psycholinguistic Research, 52(6), 2863–2876. https://doi.org/10.1007/s10936-023-10024-x

Baharun, H. (2023). The Implementation of Augmented Reality in Science Education in Secondary Schools. International Journal of Instructional Technology, 2(1), 34–45. https://doi.org/10.33650/ijit.v2i1.9325

Belkebir, R., & Habash, N. (2021). Automatic Error Type Annotation for Arabic. In CoNLL 2021 - 25th Conference on Computational Natural Language Learning, Proceedings (pp. 596–606). https://doi.org/10.18653/v1/2021.conll-1.47

Boraik, O. A., Ravikumar, M., & Saif, M. A. N. (2022). Character Segmentation from Arabic Handwritten Document Images: A Hybrid Approach. International Journal of Advanced Computer Science and Applications, 13(4), 395–403. https://doi.org/10.14569/IJACSA.2022.0130447

Dheif, A., El Ghany, A. A., & Al Ansary, S. (2022). The Guidelines of Building a Treebank for Modern Standard Arabic. In Proceedings of the 20th Conference on Language Engineering, ESOLEC 2022 (pp. 97–102). https://doi.org/10.1109/ESOLEC54569.2022.10009330

Elkhayati, M., & Elkettani, Y. (2021). Arabic Handwritten Text Line Segmentation using a Multi-agent System and a Directed CNN. In the 5th International Conference on Intelligent Computing in Data Sciences, ICDS 2021. https://doi.org/10.1109/ICDS53782.2021.9626747

Elmallah, M., Reda, M., Darwish, K., El-Sheikh, A., Elneima, A., Aljubran, M., Alsaeed, N., Mohammed, R., & Al-Badrashiny, M. (2024). Arabic Diacritization Using Morphologically Informed Character-Level Model. In 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation, LREC-COLING 2024 - Main Conference Proceedings (pp. 1446–1454).

Finn, V. (2022). A Qualitative Assessment of QCA: Method Stretching in Large-N Studies and Temporality. Quality and Quantity, 56(5), 3815–3830. https://doi.org/10.1007/s11135-021-01278-5

Hejazi, H., & Shaalan, K. (2021). Deep Learning for Arabic Image Captioning: A Comparative Study of Main Factors and Preprocessing Recommendations. International Journal of Advanced Computer Science and Applications, 12(11), 37–44. https://doi.org/10.14569/IJACSA.2021.0121105

Ibrahim, M. A. E. (2024). Morphological Aspects of a Translation Text Among Students. Theory and Practice in Language Studies, 14(3), 748–755. https://doi.org/10.17507/tpls.1403.15

Issa, I. (2022). Correction to: Morphological Complexity in Arabic Spelling and Its Implication for Cognitive Processing. In Journal of Psycholinguistic Research (Vol. 51, Issue 5, pp. 1199–1201). https://doi.org/10.1007/s10936-022-09904-5

Issa, I. (2023). Morphological Complexity in Arabic Spelling and Its Implication for Cognitive Processing. Journal of Psycholinguistic Research, 52(1), 331–357. https://doi.org/10.1007/s10936-022-09896-2

Jaradat, M., & Martini, H. (2022). Linguistic Issues according to Dia AL-Din Ibn Al-Atheer in his Book (The Exemplar in the Literature of the Writer and the Poet). Dirasat: Human and Social Sciences, 49(5), 52–64. https://doi.org/10.35516/HUM.V49I5.2779

Kassad, N. H., & Shaheen, M. R. H. (2024). An Algorithm to Analyze Arabic Verbs Morphologically. Scientific Journal of King Faisal University Basic and Applied Sciences, 25(1), 41–44. https://doi.org/10.37575/b/cmp/230061

Khalifa, S., Qaddoumi, A., Broselow, E., & Rambow, O. (2024). Picking Up Where the Linguist Left Off: Mapping Morphology to Phonology through Learning the Residuals. In Proceedings of the Second Arabic Natural Language Processing Conference (pp. 258–264). https://aclanthology.org/2024.arabicnlp-1.22

Kharsa, R., Elnagar, A., & Yagi, S. (2024). BERT-Based Arabic Diacritization: A State-of-the-art Approach for Improving Text Accuracy and Pronunciation. Expert Systems with Applications, 248. https://doi.org/10.1016/j.eswa.2024.123416

Kodner, J., & Khalifa, S. (2022). SIGMORPHON-UniMorph 2022 Shared Task 0: Modeling Inflection in Language Acquisition. In SIGMORPHON 2022 - 19th SIGMORPHON Workshop on Computational Research in Phonetics, Phonology, and Morphology, Proceedings of the Workshop (pp. 157–175). https://doi.org/10.18653/v1/2022.sigmorphon-1.18

Nisa’, K., & R A. H. A. (2024). Empowering Educators: A Comprehensive Human Resources Framework for Improving Islamic-based Schools. Journal of Islamic Education Research, 5(1), 31–44. https://doi.org/10.35719/jier.v5i1.385

Sain, Z. H. (2025). From Chalkboards to Chatbots: Revolutionizing Education with AI-Driven Learning Innovations. Educative: Jurnal Ilmiah Pendidikan, 3(1), 1–10. https://doi.org/10.70437/educative.v3i1.823

Seker, A., & Tsarfaty, R. (2020). A Pointer Network Architecture for Joint Morphological Segmentation and Tagging. In Findings of the Association for Computational Linguistics, Findings of ACL: EMNLP 2020 (pp. 4368–4378). https://doi.org/10.18653/v1/2020.findings-emnlp.391

Tallas-Mahajna, N. (2024). Exploring Subject Markers and Object Clitics Errors in Spoken Arabic: A Case Study of Children with and without Developmental Language Disorder. Forum for Linguistic Studies, 6(4), 140–154. https://doi.org/10.30564/fls.v6i4.6635

Wesely, P. M., Vyn, R., & Neubauer, D. (2024). Teacher Beliefs about Instructional Approaches: Interrogating the Notion of Teaching Methods. Language Teaching Research, 28(1), 29–51. https://doi.org/10.1177/1362168821992180

Zalmout, N., & Habash, N. (2020). Joint Diacritization, Lemmatization, Normalization, and Fine-grained Morphological Tagging. In Proceedings of the Annual Meeting of the Association for Computational Linguistics (pp. 8297–8307). https://doi.org/10.18653/v1/2020.acl-main.736


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Copyright (c) 2025 Muhamad Fuad Hasim, Muflihah Muflihah, M. Baihaqi, Muhammad Iqbal Fuadhi, Nur Islamiyah

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