The current research aims to investigate the effect of a specimen of Daniel in the acquisition of concepts for the Arabic language curricula material to the students of the third phase of the Faculty of Basic Education Department of Arabic Language. The sample consists of (93) applications and a student of (47) students in the Division (A), which represents the experimental group which studied the use of a specimen of Daniel, and (46) students in the Division (B), which represents the control group, which studied the traditional way. The subject of unified two groups, which subjects the Arabic language curricula which includes six chapters.
The duration of the experiment is a full semester. The researchers also prepared a tool for measuring the test is the acquisition of concepts for students (sample), prepared by researchers in the light of the goals of behavioral classification (Bloom) of the domain knowledge. After analyzing the responses and calculate the grades, not serve the educational Administration researchers test (T-Test) for independent samples to determine the significance of differences between the achievement of students in the two groups. The results showed the emergence of a statistically significant difference at the level (0.05) between the average degrees of the two groups and in the interest of the experimental group which studied the use of the model of Daniel. In light of the above researchers to reach a number of recommendations including: First: the use of interest in modern teaching models, in particular a specimen of Daniel. Second: Preparation of a nest containing the mock-teaching modern distributed to teachers.
Keywords provide the reader with a summary of the contents of the document and play a significant role in information retrieval systems, especially in search engine optimization and bibliographic databases. Furthermore keywords help to classify the document into the related topic. Keywords extraction included manual extracting depends on the content of the document or article and the judgment of its author. Manual extracting of keywords is costly, consumes effort and time, and error probability. In this research an automatic Arabic keywords extraction model based on deep learning algorithms is proposed. The model consists of three main steps: preprocessing, feature extraction and classification to classify the document
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