Background: Scientific education aims to be inclusive and to improve students learning achievements, through appropriate teaching and learning. Problem Based Learning (PBL) system, a student centered method, started in the second half of the previous century and is expanding progressively, organizes learning around problems and students learn about a subject through the experience of solving these problems.Objectives:To assess the opinions of undergraduate medical students regarding learning outcomes of PBL in small group teaching and to explore their views about the role of tutors and methods of evaluation. Type of the study: A cross-sectional study.Methods: This study was conducted in Kerbala Medical Colleges among second year students. A self-administered questionnaire was prepared to evaluate the newly applied teaching system. The study analysis included simple descriptive analysis and determining association through t-test, chi square test and regression analysis and using structural equation models to determine simultaneous association between different students’ demographic characteristics and potential predictors using SPSS-20 and Amos software at a significance level of < 0.05.Results:A total of 131 undergraduate medical students participated in the study with a response rate of 94%. The majority (93%) have indicated that PBL strategy contributed effectively to their knowledge development with a similar majority (92%) considering PBL successful new teaching method. About 86% reported that would choose PBL rather than conventional method and also 86% would advise PBL for others. Similarly, high majority indicated that various PBL activities are essential. Regarding the tutors’ role in PBL, the majority (92%) indicated that this role was positive and fundamental. According to two thirds (68%) of participants PBL application in Kerbala Medical college was very good application while a higher majority described various PBL sessions as successful and positive and fundamental role of tutors was stressed by most students.Conclusions: This study highlighted the benefits of soliciting student impressions of effective small group teaching. The students’ emphasized group atmosphere and facilitation skills of tutor in learning.Key words: Problem Based Learning, Medical Education, Small Group Teaching, Team Based Learning, Kerbala Medical College
In the presence of multi-collinearity problem, the parameter estimation method based on the ordinary least squares procedure is unsatisfactory. In 1970, Hoerl and Kennard insert analternative method labeled as estimator of ridge regression.
In such estimator, ridge parameter plays an important role in estimation. Various methods were proposed by many statisticians to select the biasing constant (ridge parameter). Another popular method that is used to deal with the multi-collinearity problem is the principal component method. In this paper,we employ the simulation technique to compare the performance of principal component estimator with some types of ordinary ridge regression estimators based on the value of t
... Show MoreThis research aims at to identify the extent to which creative thinking skills Impact the change &development of Administrative Leadership styles In Administrative Leadership of the Medical City Department. Identify the nature of the relationship between them, determine the prevailing leadership style, &measure the level of creative thinking skills they have. In order to achieve the objectives of the research, the descriptive analytical method was adopted.
The research tool consisted of a questionnaire consisting of (61) paragraphs, in addition to the interview & observation. The research sample consisted of (170) administrative leaders in the upper &middle organizational levels. The
... Show MoreSentiment 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
... Show MoreObjective: To identification environmental and psychological violence's components among collegians’ students of different stages, and gender throughout creating specific questionnaire, and estimating regression of environmental domain effect on psychological domain, as well as measuring powerful of the association contingency between violence's domains in admixed form with respondent characteristics, such that (Demographics, Economics, and Behaviors), and extracting model of estimates impact of studied domains in studying risks, and protective factors among collegians’ students in Baghdad city. Methodolog
The current research aims to: (know the effectiveness of the harvesting strategy in the achievement of the students of the Institute of Fine Arts in environmental art.
- In order to know this effectiveness, the researcher put a main zero hypothesis and derived six sub-hypotheses from it. the usual way; As the research community reached (120) male and female students of the Fine Arts Institutes for the morning study in Baghdad. As for the research sample, it was chosen by the simple random method, and the number was (75) male and female students for the year 2021-2022 AD. The researcher applied a pre-knowledge test for the four groups of research to find out the level of previous experiences of students in the subject of environmen
This study aims to know the extent of the impact of Strategic Leadership as an independent variable in Strategic Learning as a dependent variable to help the senior leadership in Anbar University to take the right decisions to develop Strategic Learning programs in light of the circumstances of the Covid-19 and the sudden decisions adopted by the university to switch to E-learning and to blend. The survey was conducted by distributing a questionnaire that was adopted as a primary tool in data collection from the study sample represented by the university's senior leaders, An intentional random sample of (105) was selected from our community of (127), the data were analyzed by (SPSS) Depe
... Show MoreThis paper proposes a better solution for EEG-based brain language signals classification, it is using machine learning and optimization algorithms. This project aims to replace the brain signal classification for language processing tasks by achieving the higher accuracy and speed process. Features extraction is performed using a modified Discrete Wavelet Transform (DWT) in this study which increases the capability of capturing signal characteristics appropriately by decomposing EEG signals into significant frequency components. A Gray Wolf Optimization (GWO) algorithm method is applied to improve the results and select the optimal features which achieves more accurate results by selecting impactful features with maximum relevance
... Show MoreThis paper provides a four-stage Trigonometrically Fitted Improved Runge-Kutta (TFIRK4) method of four orders to solve oscillatory problems, which contains an oscillatory character in the solutions. Compared to the traditional Runge-Kutta method, the Improved Runge-Kutta (IRK) method is a natural two-step method requiring fewer steps. The suggested method extends the fourth-order Improved Runge-Kutta (IRK4) method with trigonometric calculations. This approach is intended to integrate problems with particular initial value problems (IVPs) using the set functions and for trigonometrically fitted. To improve the method's accuracy, the problem primary frequency is used. The novel method is more accurate than the conventional Runge-Ku
... Show MoreTo expedite the learning process, a group of algorithms known as parallel machine learning algorithmscan be executed simultaneously on several computers or processors. As data grows in both size andcomplexity, and as businesses seek efficient ways to mine that data for insights, algorithms like thesewill become increasingly crucial. Data parallelism, model parallelism, and hybrid techniques are justsome of the methods described in this article for speeding up machine learning algorithms. We alsocover the benefits and threats associated with parallel machine learning, such as data splitting,communication, and scalability. We compare how well various methods perform on a variety ofmachine learning tasks and datasets, and we talk abo
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