Table 1 - uploaded by Mrwan Benidris
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... I included all programming lecturers who are interested in participating in the survey. Table 1 shows that the sample size of the students for both universities is 173 students (N1=111 + N2=62) and for the teachers is 45 instructors (N3=15 and N4=30). The samples N1 and N3 were taken from the students and lecturers of the Faculty of IT -the University of Benghazi while the samples N2 and N4 were taken from the students and lecturers of the Department of Computer Science -Omar Al-Mukhtar University. ...
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... There was research done to find out whether the weakness of students in English affects their ability to understand programming and what other factors affect students understanding of programming, where during this paper a study was conducted on students of two universities, at the first university appeared that English has an effect of 27% on learning programming and it turns out that mathematics and logic have a 40% effect and that lecturers have a 13% impact and the lab has a 20% effect, whereas in the second university it appeared that English has an effect of 63%, mathematics and logic by 10% and lecturers by 3% and laboratories by 24%, and It appeared through this paper that lecturers see that the main reason for the weakness of students in learning programming is the lack of proficiency in English language. (Ammar & Ben Idris, 2018) So, Arabic is one of the most complex languages in terms of its structure. Therefore, the process of integrating Arabic into computing has encountered many problems by developers, especially in the process of morphological analysis and spelling, to solve this complexity there are many steps that must be taken to build a simple Arabic manual can be computerized it such as: remove the formation of letters and punctuation and replace some of the characters as stated in the paper in general. ...
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Computer programming languages are becoming more indispensable as our world becomes more computerized. This paper highlights the importance of the factors affecting programming learning difficulties. A survey method was employed to measure the relationships between variables related to programming learning difficulties. A total of 227 students studying for a computer programming diploma at Umm Al-Qura University in Saudi Arabia were surveyed for this purpose. The methodology for the study was structural equation modeling, and AMOS software was used to test the structural and measurement model and study the interlinkages between variables. The theoretical framework employed in this study comprised the technology acceptance model and experiential learning theory. The indirect factors were English language competency, learning materials, educational facilities, and teaching practices. The study concluded that the interactions of these factors explain the programming learning difficulties experienced by students enrolled in programming courses. Moreover, the factors which had the greatest effect on programming learning difficulty were English language competency and educational facilities. Therefore, the factors mentioned above should be considered by educational institutions for enhancement and ongoing development because of their connection with the programming learning difficulties experienced by students.KeywordsProgramming languagelearning difficultieslearning environmentsteachinglearning materials
The banking industry performs credit score analysis as an efficient credit risk assessment method to determine a customer’s creditworthiness. In the banking industry, machine learning could be used for a variety of uses involving data analysis. A method of data analysis that is capable of self-regulation has been made possible by the development of modern techniques, such as classification approaches. The classification method is a form of supervised learning in which the computer acquires knowledge from the provided input data and then utilizes it to classify the dataset, which is used for training purposes. This study presents a comparative analysis of the various machine learning algorithms that are utilized to evaluate credit risk. The methods are used by utilizing the German Credit dataset that was collected from Kaggle, which consists of 1,000 instances and 11 attributes, all of which are used to determine if transactions are good or bad. The findings of data analysis using Logistic Regression, Linear Discriminant Analysis, Gaussian Naive Bayes, K-Nearest Neighbors Classifier, Decision Tree Classifier, Support Vector Machines, and Random Forest are compared and contrasted in this study. The findings demonstrated that the Random Forest algorithm forecasted credit risk effectively.KeywordsCredit RiskBankingMachine LearningPredictionFeatures
