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 .
The hydrological process has a dynamic nature characterised by randomness and complex phenomena. The application of machine learning (ML) models in forecasting river flow has grown rapidly. This is owing to their capacity to simulate the complex phenomena associated with hydrological and environmental processes. Four different ML models were developed for river flow forecasting located in semiarid region, Iraq. The effectiveness of data division influence on the ML models process was investigated. Three data division modeling scenarios were inspected including 70%–30%, 80%–20, and 90%–10%. Several statistical indicators are computed to verify the performance of the models. The results revealed the potential of the hybridized s
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