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|dc.contributor.author||Mohd Taqwudin bin Mohd Yazid||-|
|dc.description.abstract||Despite, having a lot of obstacles laying throughout the path of learning Arabic speaking skill, and facing the intense negative side effects of the obstacles towards achieving the learning objectives, there was a group of non- native Arabic speaker students who had such distinguished and outstanding Arabic proficiency over others, they were the Arabic debaters. Pertaining to the fact, their learning strategies were considered as a strong foundation a learning speaking skill. This study aimed at explaining the type of Self-Regulated Learning Strategies (SRL) applied by those debaters in learning Arabic speaking skill and examining their extensity of engagement in applying the strategies all the way through the learning process. the study was conducted following the quantitative method whereas questionnaire was used as an instrument to gain data, and the data was then analyzed using descriptive analysis method. The questionnaire was designed based on the SRL strategies first model by Pintrinch and others, and the Motivated Strategies for Learning Questionnaire (MSLQ). The sample of this study consisted of 10 debaters whom representing Universiti Sains Islam Malaysia(USIM) debate team in 2017. the result of study showed that (1) those Arabic debaters have employed all SRL strategies first model by Pintrinch and others. (2) their extensity of engagement in applying the strategies throughout the process of learning Arabic speaking skill was high, with the scale of "I employ it almost all the times". Lastly, the study suggested, that every student of Arabic Language in particular and whoever has interest in teaching and learning it in general, to take the selected strategies as their guidline in enhancing speaking skill level.||-|
|dc.publisher||Universiti Sains Islam Malaysia||-|
|dc.title||استخدم إستراتيجيات التعلم المنظم ذاتيا في تعلم مهارة الكلام لدى المتناظرين المثلين الجامعة العلوم الإسلامية المالزية||en_US|
|Appears in Collections:||Master|
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