The current research aims to identify "the impact of the round table strategies and the question of self-achievement and self-efficacy among students of the Faculty of Education in research methodology course.” The research sample consisted of (75) male and female-third stage students in the department of Life Sciences / College of Education for Pure Sciences / University of Dhi Qar for the academic year (2018-2019. The researcher adopted the experimental approach to achieving the study objectives. The researcher prepared two tools: the achievement test and the self-efficacy scale were applied to the collected sample to obtain the needed data. The result showed that there was a statistically significant difference at the level (0.05) b

The current research aims to identify the effect of the note-taking strategy (CORNELL) on systematic thinking among second-grade female students in government daytime secondary and intermediate schools. Al-Fadhila School was intentionally chosen to be its student sample for the research affiliated with the First Karkh Directorate for the academic year (2024-2025). Then one of the two sections was randomly chosen to represent the experimental group that studies according to the note-taking strategy (CORNELL) and the other the control group that studies according to the usual method. The equivalence of the two research groups was verified by a set of variables, which were represented by chronological age in months, previous achievement in che

This research aimed to evaluate the level of readiness to teach science in the light of the information, media, and technology skills among undergraduate students at the Faculty of Edu-cation, King Khalid University. To achieve this goal, a descriptive and analytical approach was used. A list of readiness to teach science was prepared in the light of Information, media, and technology skills, and in the light of this list, a cognitive test, observation sheet, and attitude scale were prepared to assess readiness to teach science in its three aspects, cognitive, behavioral, and emotional. The sample of the research consisted of (42) students enrolled in field training courses at the sixth, seventh and eighth levels. Research tools were app

Gender classification is a critical task in computer vision. This task holds substantial importance in various domains, including surveillance, marketing, and human-computer interaction. In this work, the face gender classification model proposed consists of three main phases: the first phase involves applying the Viola-Jones algorithm to detect facial images, which includes four steps: 1) Haar-like features, 2) Integral Image, 3) Adaboost Learning, and 4) Cascade Classifier. In the second phase, four pre-processing operations are employed, namely cropping, resizing, converting the image from(RGB) Color Space to (LAB) color space, and enhancing the images using (HE, CLAHE). The final phase involves utilizing Transfer lea

Analyzing sentiment and emotions in Arabic texts on social networking sites has gained wide interest from researchers. It has been an active research topic in recent years due to its importance in analyzing reviewers' opinions. The Iraqi dialect is one of the Arabic dialects used in social networking sites, characterized by its complexity and, therefore, the difficulty of analyzing sentiment. This work presents a hybrid deep learning model consisting of a Convolution Neural Network (CNN) and the Gated Recurrent Units (GRU) to analyze sentiment and emotions in Iraqi texts. Three Iraqi datasets (Iraqi Arab Emotions Data Set (IAEDS), Annotated Corpus of Mesopotamian-Iraqi Dialect (ACMID), and Iraqi Arabic Dataset (IAD)) col

Sentiment analysis is one of the major fields in natural language processing whose main task is to extract sentiments, opinions, attitudes, and emotions from a subjective text. And for its importance in decision making and in people's trust with reviews on web sites, there are many academic researches to address sentiment analysis problems. Deep Learning (DL) is a powerful Machine Learning (ML) technique that has emerged with its ability of feature representation and differentiating data, leading to state-of-the-art prediction results. In recent years, DL has been widely used in sentiment analysis, however, there is scarce in its implementation in the Arabic language field. Most of the previous researches address other l

The aim of this research is to identify how to employ the social imagination and its representations in the paintings of the students of the Department of Art Education. Students of the Department of Art Education in the light of this tool, and the researcher reached a number of results, the most important of which are:
1. The indicator of the social imagination in the excitement of the artist and the recipient, because the representations are embodied in forms within the painting and close to reality.
2. An artistic sample of the representational imagination and the creative imagination was found to be more than the imaginary imagination.
3. Individual symptoms related to the term.
4. The representations of the socia

Background. The use of modern aids and technology has contributed greatly to football development, the goalkeeper is the most important position in the team, and the use of devices gave objective readings about the goalkeeper's ability in terms of skill and physical aspects. Objectives. The research aimed to prepare exercises using an electronic device to measure the knee bending angle because of its great importance in developing the skill of catching and dimensions of the high ball for football goalkeepers. Methods. The researchers used the experimental method, and the sample consisted of (4) male goalkeepers under 15 years of age, one of the research procedures was to determine the biomechanical variables affecting the development of the

Thyroid disease is a common disease affecting millions worldwide. Early diagnosis and treatment of thyroid disease can help prevent more serious complications and improve long-term health outcomes. However, thyroid disease diagnosis can be challenging due to its variable symptoms and limited diagnostic tests. By processing enormous amounts of data and seeing trends that may not be immediately evident to human doctors, Machine Learning (ML) algorithms may be capable of increasing the accuracy with which thyroid disease is diagnosed. This study seeks to discover the most recent ML-based and data-driven developments and strategies for diagnosing thyroid disease while considering the challenges associated with imbalanced data in thyroid dise