An Analysis of the Use of Transitive and Intransitive Verbs in Arabic: A Quantitative Descriptive Study of University Students in Malaysia: تحليل استخدام الأفعال المتعدية واللازمة في اللغة العربية: دراسة وصفية كمية لطلبة جامعة بماليزيا
Summary
The authors look at how Malaysian university learners of Arabic as a foreign language understand and use transitive verbs (those needing a direct object) and intransitive verbs (those complete without one). Empirical, quantitative evidence on this is scarce, since earlier work mostly offered grammatical or cross-language analysis rather than measuring learner performance. The study used a quantitative descriptive design: a four-part questionnaire on a 5-point Likert scale was given to 91 Universiti Teknologi MARA (UiTM) undergraduates enrolled in LG 242, who were chosen through purposive sampling. The parts covered demographics, usage, comprehension level, and attitudes. Responses were processed in SPSS (version 26), mainly as item means and standard deviations, with dispersion interpreted using Ramli's (1999) bands. The framing draws on Piaget's cognitive learning theory. Students rated their conceptual grasp high, but the item on telling the two verb types apart in practice scored lowest and had the widest spread. The authors trace this to weak command of Arabic morphology. The work points teachers toward practice-heavy, interactive instruction and better-targeted assessment of morphology.
Key points
- Sample: 91 students in course LG 242 at UiTM, Malaysia, selected by purposive sampling; instrument is a four-section Likert questionnaire analysed in SPSS 26.
- Highest item was understanding the definition of transitive vs intransitive verbs (mean 3.96, SD 0.77). Recognising that transitive verbs need an object scored 3.90, and that intransitive verbs do not scored 3.82.
- Practical items were lower: using these verbs in daily communication scored 3.55, and producing example sentences scored 3.70.
- The item on often struggling to decide whether a verb is transitive or intransitive had the lowest mean (3.48) and the highest SD (0.942), indicating uneven applied ability across students.
- The abstract reports a mean range of 3.60–4.14, which does not match the table values (3.48–3.96). The authors link the difficulties to gaps in morphological knowledge and to interference from learners' other languages.
Interesting ideas
- I1. The questionnaire separates knowing a rule (defining transitivity) from applying it (classifying real verbs), and finds a gap between the two; this split exposes shallow competence that a single score would hide. — For Mova (learn): Evaluation could test the verb-class lexicon's stated valency apart from whether the parser actually attaches an
objin running text, so that rule-knowledge and rule-use failures are reported separately. - I2. Standard deviation is read as a diagnostic in its own right: the item with the most spread marks where a population is split, not merely where it is weak. — For Mova (learn): When several witness parsers produce silver data, ranking verbs or constructions by disagreement variance could pinpoint where LLM-teacher review or repair rules are most needed.
- I3. Transitivity is treated as a lexical-morphological property of the verb, not just a surface pattern, and errors are blamed on missing morphological knowledge. — For Mova (world): Compiled verb classes could store expected argument frames (object required or not), letting the parser and frame builder flag trees where an obligatory object is missing or a spurious one appears.
- I4. Fixed interpretive bands (Ramli 1999) are used to turn raw means and dispersions into named levels, which keeps reporting consistent across items. — For Mova (answer): Scores on school-course tests and QA could be mapped onto fixed, documented bands, making progress reports comparable and explainable across reader versions.
- I5. One item is framed as a difficulty statement rather than a competence claim, which acts as a cross-check on optimistic self-ratings. — For Mova (learn): Mixing negative controls into evaluation (inputs where the correct output is "no object" or "cannot answer") would catch systems that score well simply by always predicting the common case.
Cite
@article{ismail2026analysis,
title = {{An Analysis of the Use of Transitive and Intransitive Verbs in Arabic: A Quantitative Descriptive Study of University Students in Malaysia: تحليل استخدام الأفعال المتعدية واللازمة في اللغة العربية: دراسة وصفية كمية لطلبة جامعة بماليزيا}},
author = {Ismail, Mohamad Rofian and Azemaizan, Aiman Hakim and Mat, Azman Che},
journal = {Journal of Arabic Language Learning and Teaching 4},
year = {2026},
doi = {10.23971/jallt.v4i1.402},
url = {https://doi.org/10.23971/jallt.v4i1.402}
}References
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