Identify the effect of an educational design according to the repulsive (allosteric) learning model on the achievement of chemistry and lateral thinking. The sample consisted of (59) students from third-grade intermediate students. They were randomly distributed into two groups (experimental and control), and the equivalence was done in (chronological age, previous achievement in chemistry, intelligence, lateral thinking). The (30) students from experimental group were taught according to the instructional design, other 29 students from the (control) group were taught according to the usual method. Two tests done, one of them is an achievement test consisted of (30) items of the type of multiple choice, the other was a lateral thinking test consisted of (26) items, of (multiple choice). Both of the validity and reliability to the two tools were verified. The results revealed the superiority of the experimental group who studied by the instructional design according to the repulsive learning model in achievement and lateral thinking.
Students' passive listening to their teacher's reading is one of main
reasons behindtheir weakness in the reading skill which in its turn may
hinderachieving the in desired objectives.
When exploiting critical thinking, which will lead to deeper
understanding of the intellectual content, in learning and accurate and
correct students' outcomes.
Active listening allows paying attention to the speaker, asking him,
arguing with him, judging what he says, and making decision on what
he says. For this reason, the researcher felt the need for preforming a
study to identify the effect of critical listening on developing students'
critical thinking at reading in the Kurdish language department.
The researcher has
In the current worldwide health crisis produced by coronavirus disease (COVID-19), researchers and medical specialists began looking for new ways to tackle the epidemic. According to recent studies, Machine Learning (ML) has been effectively deployed in the health sector. Medical imaging sources (radiography and computed tomography) have aided in the development of artificial intelligence(AI) strategies to tackle the coronavirus outbreak. As a result, a classical machine learning approach for coronavirus detection from Computerized Tomography (CT) images was developed. In this study, the convolutional neural network (CNN) model for feature extraction and support vector machine (SVM) for the classification of axial
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