Authors

Department of Arabic Language and Literature, Faculty of Theology and Islamic Studies, Hakim Sabzevari University, Sabzevar, Iran.

10.48310/alle.2026.4836

Abstract

The present study aims to analyze the content of final exam questions in the course "Arabic, Quranic Language 1" for 10th-grade Nazari branch students from the perspective of Benjamin Bloom's taxonomy of educational objectives. This topic is significant because final exams, as one of the formal and decisive tools of the evaluation system, play a key role in assessing the extent to which curriculum objectives and student learning outcomes are achieved. The study was conducted using a quantitative approach and inferential content analysis method. Thirteen administered final exam papers from the years 1402 and 1403 (Iranian calendar) were selected using a census method, and their questions were classified based on Bloom's six cognitive levels: knowledge, comprehension, application, analysis, synthesis, and evaluation. The data were analyzed using frequency tables, percentage charts, and allocated marks. Findings indicated that out of 241 questions, %51 were at the knowledge level and %34 at the comprehension level, while only %2 were at the application level, 9% at the analysis level, and %4 at the synthesis level; no questions were observed at the evaluation level. The mean allocated marks were also highest at the knowledge and comprehension levels. The results by academic track showed that all three tracks—Mathematics-Physics and Experimental Sciences, Literature and Humanities, and Islamic Sciences and Knowledge—exhibited a similar pattern of focus on lower cognitive levels. Based on the findings, it can be concluded that the design of final exam questions in Arabic is primarily memory-oriented and does not provide sufficient opportunity to assess higher-order thinking skills. It is recommended that question design be revised with an emphasis on analysis, synthesis, and evaluation levels to align assessments with curriculum objectives and a deep learning approach.

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