The aim of this study is to compare the effects of three methods: problem-based learning (PBL), PBL with lecture method, and conventional teaching on the understanding of thermodynamics, group work and self-directed learning skills among physics undergraduates. The actual sample size comprises of 122 students, who were selected randomly from the Physics Department, College of Education in Iraq, for academic year 2011-2012. In this study, the pre and posttest were done and the instruments were administered to the students for data collection. Inferential statistics were employed to analyze data. The independent variables were the PBL, the PBL with lecture method, and the conventional teaching. Dependent variables of statistical analysis were posttest scores on the understanding of thermodynamics, group work skills, and self-directed learning skills. Covariates of statistical analysis were pretest scores of the understanding of thermodynamics, group work skills, and self-directed learning skills. The data were analyzed using statistical package social sciences (SPSS) version 19. Overall, the statistical results rejected all null hypotheses of this study. Thus, the use of PBL with lecture method enhances the understanding of thermodynamics better than using the PBL alone or using conventional teaching method. Using the PBL without or with lecture method promotes the skills of group work, and self-directed learning better than using the conventional teaching, among physics undergraduate.
The present research aims to identify thecorrelation between cognitive motivation andthe trend towards the teaching profession among students of the Department of Chemistry in theFaculty of Education for Pure Sciences - Ibn al-Haytham, as well as to identify the differences in the relationship according to the variable type (male, female). The measures of cognitive motivation and the trend towards the teaching profession were applied, using pearson's correlation coefficient,t-testfor one sample, andthe t-test of two separate samples.
The printing designer's creative thinking is a deliberate mental process based on specific skills that stimulate the motivation of the student to learn and call for new information for the investigation and research to discover the problems and attitudes and through reformulating the experience in new patterns depending on the active imagination and the flexible scientific thinking through providing the largest number possible of various unfamiliar printing design models, and testing their suitability and then readjusting the results with the availability of suitable educational, learning and academic atmosphere.
The designer's creative thinking depends on main skills. Fluency skill is to put t
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The current research aims to find out the extent to which students of the Faculty of Education for Pure Sciences\/Ibn al-Haitham have owned laboratory academic skills, the researcher adopted a descriptive research approach to conform to the goal of the research, the research sample the consisted of 140 students from the Department of Chemistry Phase II, The research tool, which consisted of a measure of laboratory academic skills, which consisted of seven skills and consisted of 28 paragraphs (four paragraphs per field), was prepared and the pent-up scale was chosen because the selected sample were university students, and the results showed the ownership of students' skills of laboratory academic skills other than skill The use of the libr
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The aim of the current research is to identify the level of availability of written expression skills included in the Arabic language curriculum document among middle school students from the teachers' point of view. The researcher used the descriptive approach. To analyze the data and access the research results, he used the (SPSS) program. The research was conducted during the first semester of the academic year 1442/1443 AH on a random sample of Arabic language teachers in the Bisha Education Department. They reached about (213) male and female teachers. The results revealed a number of indicators: the level of availability of written expression skills among middle school students in Bisha governorate
... Show MoreSome of the main challenges in developing an effective network-based intrusion detection system (IDS) include analyzing large network traffic volumes and realizing the decision boundaries between normal and abnormal behaviors. Deploying feature selection together with efficient classifiers in the detection system can overcome these problems. Feature selection finds the most relevant features, thus reduces the dimensionality and complexity to analyze the network traffic. Moreover, using the most relevant features to build the predictive model, reduces the complexity of the developed model, thus reducing the building classifier model time and consequently improves the detection performance. In this study, two different sets of select
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To date, comprehensive reviews and discussions of the strengths and limitations of Remote Sensing (RS) standalone and combination approaches, and Deep Learning (DL)-based RS datasets in archaeology have been limited. The objective of this paper is, therefore, to review and critically discuss existing studies that have applied these advanced approaches in archaeology, with a specific focus on digital preservation and object detection. RS standalone approaches including range-based and image-based modelling (e.g., laser scanning and SfM photogrammetry) have several disadvantages in terms of spatial resolution, penetrations, textures, colours, and accuracy. These limitations have led some archaeological studies to fuse/integrate multip
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The deep learning algorithm has recently achieved a lot of success, especially in the field of computer vision. This research aims to describe the classification method applied to the dataset of multiple types of images (Synthetic Aperture Radar (SAR) images and non-SAR images). In such a classification, transfer learning was used followed by fine-tuning methods. Besides, pre-trained architectures were used on the known image database ImageNet. The model VGG16 was indeed used as a feature extractor and a new classifier was trained based on extracted features.The input data mainly focused on the dataset consist of five classes including the SAR images class (houses) and the non-SAR images classes (Cats, Dogs, Horses, and Humans). The Conv
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This paper presents a hybrid energy resources (HER) system consisting of solar PV, storage, and utility grid. It is a challenge in real time to extract maximum power point (MPP) from the PV solar under variations of the irradiance strength. This work addresses challenges in identifying global MPP, dynamic algorithm behavior, tracking speed, adaptability to changing conditions, and accuracy. Shallow Neural Networks using the deep learning NARMA-L2 controller have been proposed. It is modeled to predict the reference voltage under different irradiance. The dynamic PV solar and nonlinearity have been trained to track the maximum power drawn from the PV solar systems in real time.
Moreover, the proposed controller i
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Wireless Body Area Sensor Networks (WBASNs) have garnered significant attention due to the implementation of self-automaton and modern technologies. Within the healthcare WBASN, certain sensed data hold greater significance than others in light of their critical aspect. Such vital data must be given within a specified time frame. Data loss and delay could not be tolerated in such types of systems. Intelligent algorithms are distinguished by their superior ability to interact with various data systems. Machine learning methods can analyze the gathered data and uncover previously unknown patterns and information. These approaches can also diagnose and notify critical conditions in patients under monitoring. This study implements two s
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