The present research aims at the following:
- Measure Classroom environment of university Student (research sample)- Measure
Cognitive motive and Reflective Thinking of university Student (research Sample) .
- In order to Know the relationship between the Scores of Classroom environment and
Scores of cognitive motive and Scores of Reflective Thinking of university Student
(research Sample) .
Sample of research consisted of (500) Students for both genders distrivbted upon ten
colleges in Baghdad University , (5) Scientific Colleges , and (5) human Colleges , Sample
was chosen by using radom method Research instruments were three , Prepared by the researcher ,The first is to measure
classroom environment . The Second to measure Cognitive motive . The third to measure
Reflective Thinking .
The research verified the Validity of measurements and their items were analyzed
Statistically upon the sample consisted of (500) Students for both genders to extract the
discrimination power and the Validity of items . Reliadility was calculated by method
The Alpha kronbache method for internal constanc.
After fulfilling measurements preparations , the final application was applied upon the major
research Sample Consisted of (500) students of both genders. Number of Statistical methods
were used to answer of the research aims and hypotheses like person Coeffisient m (t-test) for
one sample two vaeiance analysis , multiple declivity analysis , Scheffe .
The most important result achievd by the researcher is that there is a Positive Conjunctive
relation of statistical significance between Classroom environment and cognitive motive
, also there is a Positive relationship of statistical significance between classroom
environmentand Reflectiv thinking .
That the erea of interachtion of Students With each other and the Organaztion of the Lesson
and Provide a list Supplies and teaching field interaction with Students and Contribute to its
clear and Signicant . The researcher set some recommendations and suggestions .
Obtaining the computational models for the functioning of the brain gives us a chance to understand the brain functionality thoroughly. This would help the development of better treatments for neurological illnesses and disorders. We created a cortical model using Python language using the Brian simulator. The Brian simulator is specialized in simulating the neuronal connections and synaptic interconnections. The dynamic connection model has multiple parameters in order to ensure an accurate simulation (Bowman, 2016). We concentrated on the connection weights and studied their effect on the interactivity and connectivity of the cortical neurons in the same cortical layer and across multiple layers. As synchronization helps us to mea
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