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Evaluation of Blended Learning in Nursing Education at the Middle Region in Iraq
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Abstract

Objective(s): To evaluate blended learning in nursing education at the Middle Region in Iraq.

Methodology: A descriptive study, using evaluation approach, is conducted to evaluate blended learning in nursing education in Middle Region in Iraq from September 26th, 2021 to March 22nd, 2022. The study is carried out at two Colleges of Nursing at the University of Baghdad and University of Tikrit in Iraq. A convenient, non-probability, sample of (60) undergraduate nursing students is selected. The sample is comprised of (30) student from each college of nursing, Self-report questionnaire is constructed from the literature, for evaluating the blended learning in nursing education at these colleges of nursing. The instrument consists of two parts which they include students’ socio-demographic data and evaluation of blended learning in nursing education. A pilot study is conducted for the determination of the study instrument's content validity and internal consistency reliability.

Results: The findings indicate that Colleges of Nursing at the Middle Region in Iraq have experienced fair performance of blended learning relative to its domains in nursing education.

Conclusion: It is discovered, in the present study, that the blended learning program

application is not influenced by learners’ demographic characteristics of age, gender, grade, family monthly income and residency.

Recommendations: The study recommends that the implementation blended learning in nursing education should be seriously monitored for the benefits of the colleges of nursing, instructors and learners, supportive alternatives should be presented to both of the instructors and learners.

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Publication Date
Mon Dec 20 2021
Journal Name
Baghdad Science Journal
Recurrent Stroke Prediction using Machine Learning Algorithms with Clinical Public Datasets: An Empirical Performance Evaluation
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Recurrent strokes can be devastating, often resulting in severe disability or death. However, nearly 90% of the causes of recurrent stroke are modifiable, which means recurrent strokes can be averted by controlling risk factors, which are mainly behavioral and metabolic in nature. Thus, it shows that from the previous works that recurrent stroke prediction model could help in minimizing the possibility of getting recurrent stroke. Previous works have shown promising results in predicting first-time stroke cases with machine learning approaches. However, there are limited works on recurrent stroke prediction using machine learning methods. Hence, this work is proposed to perform an empirical analysis and to investigate machine learning al

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consciousness of green nanotechnology among chemistry scholers at the College of Education for Pure Sciences, Ibn al-Haytham in Iraq
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Receipt date:12/7/2020 accepted date:24/1/2021 Publication date:31/12/2021

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The constant characteristic of international relations is the constant change due to political, economic and military developments in addition to technology, and this in turn has led to many transformations in the concept of power, its uses, and the elements that form power and its distribution, and according to those variables, the concept of power has shifted from hard to soft, up to smart powe

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