The research aims to identify the educational values prevailing in the small kinetic games for the children of Riyadh, and to categorize the educational values of the kinetic games small children Riyadh. The research analyzed the content of a number of small kinetic games that included studied physical education at the two pre-kindergarten and stipulated by the Platform for kindergartens, which is being applied. The content analysis was used by analysts agreement with themselves over time (21) days. The agreement between the external researcher and analyst. The researcher used the Cooper to extract equation lab agreement between the researcher and the outside analyst, has reached agreement on determining factor idea and label values (0.83). Statistical methods used by Cooper equation to find consistency and the percentage of the agreement, and analysis of variance for the purpose of knowledge value variables. The researcher found a number of results, including high values of fun and excitement and this illustrates the impact of small kinetic games for children in the contribution of fun to create characters far from depression. By displaying search results to reach to the recommendations, including cognitive emphasis on values in the small motor specialized games for children. Either of the proposals suggested by the researcher, including a pilot study on the impact small kinetic games for children in the process of providing the child with information
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New types of modules named Fully Small Dual Stable Modules and Principally Small Dual Stable are studied and investigated. Both concepts are generalizations of Fully Dual Stable Modules and Principally Dual Stable Modules respectively. Our new concepts coincide when the module is Small Quasi-Projective, and by considering other kind of conditions. Characterizations and relations of these concepts and the concept of Small Duo Modules are investigated, where every fully small dual stable R-module M is small duo and the same for principally small dual stable.
Let R be a ring and let M be a left R-module. In this paper introduce a small pointwise M-projective module as generalization of small M- projective module, also introduce the notation of small pointwise projective cover and study their basic properties.
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Let be a commutative ring with unity and let be a non-zero unitary module. In
this work we present a -small projective module concept as a generalization of small
projective. Also we generalize some properties of small epimorphism to δ-small
epimorphism. We also introduce the notation of δ-small hereditary modules and δ-small
projective covers.
Let be a commutative ring with identity , and be a unitary (left) R-module. A proper submodule of is said to be quasi- small prime submodule , if whenever with and , then either or . In this paper ,we give a comprehensive study of quasi- small prime submodules.
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In this paper, we introduce the concept of e-small M-Projective modules as a generalization of M-Projective modules.
Let R be an associative ring with identity and let M be a unitary left R–module. As a generalization of small submodule , we introduce Jacobson–small submodule (briefly J–small submodule ) . We state the main properties of J–small submodules and supplying examples and remarks for this concept . Several properties of these submodules are given . Also we introduce Jacobson–hollow modules ( briefly J–hollow ) . We give a characterization of J–hollow modules and gives conditions under which the direct sum of J–hollow modules is J–hollow . We define J–supplemented modules and some types of modules that are related to J–supplemented modules and int
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Origination for Teaching Physics at Secondary Stage main of the Educational Aims
The increasing amount of educational data has rapidly in the latest few years. The Educational Data Mining (EDM) techniques are utilized to detect the valuable pattern so that improves the educational process and to obtain high performance of all educational elements. The proposed work contains three stages: preprocessing, features selection, and an active classification stage. The dataset was collected using EDM that had a lack in the label data, it contained 2050 records collected by using questionnaires and by using the students’ academic records. There are twenty-five features that were combined from the following five factors: (curriculum, teacher, student, the environment of education, and the family). Active learning ha
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