Sleep Disorders and Electronic Games Addiction among Jordanian Adolescents

This study aimed to examine the relationship between electronic game addiction and sleep disorders among Jordanian adolescents. Sample of the study were 576 adolescents (325 males and 251 females) their age between 14 to 17 years. Lemmens., Valkenburg, and Peter scale for electronic games addiction and the Albana scale for adolescents' sleep disorder were used. The study considers the frequent links between severe internet addiction and the emergence of addictive-like traits in adolescents. The results suggested a higher level of electronic game addiction which was significantly associated with a higher level of sleep disorder. Results also showed that withdrawal, mood modification, and silence explained 69.30% of sleep disorders among Jordanian adolescents. Moreover, total electronic game addiction explained 40% of the variance in sleep disorder among Jordanian adolescents. Keywords: sleep disorder, electronic games, addiction, Jordanian adolescents. DOI: 10.7176/JEP/14-13-04 Publication date:May 31st 202

Unleashing the Power of Learning Technology: Exploring the Nexus between EFL Education, Cognitive Processes, and Metacognition

This study aims to explore the types of learning strategies used by language teachers in EFL classrooms. Additionally, it investigates how language teachers utilize technology to engage EFL students in cognitive and metacognitive activities that facilitate foreign language learning. The study employed a mixed-methods approach, involving 48 language teachers (29 males and 19 females) from two private universities in southern Amman. The participants were divided into two groups based on their specialty majors (English Language and Literature and English Language and Translation). They completed a survey and participated in a focus-group interview. The findings of the study indicated that language teachers in EFL classrooms found tablets and laptops to be the most effective learning strategies. Additionally, the study did not identify any statistically significant gender-related differences in the application of these strategies. However, it was observed that summarizing and evaluating were the most frequently utilized strategies by language teachers in EFL classrooms. Furthermore, the study highlighted that the absence of learning technology, such as tablets and laptops, in EFL classrooms has a negative impact on instructional delivery and consumes time. These learning technologies are considered cognitive tools that enhance language learning. &nbsp

A data mining technique for detecting malignant mesothelioma cancer using multiple regression analysis

Lung cancer is a substantial health issue globally, and it is one of the main causes of mortality. Malignant mesothelioma (MM) is a common kind of lung cancer. The majority of patients with MM have no symptoms. In the diagnosis of any disease, etiology is crucial. MM risk factor detection procedures include positron emission tomography, magnetic resonance imaging, biopsies, X-rays, and blood tests, which are all necessary but costly and intrusive. Researchers primarily concentrated on the investigation of MM risk variables in the study. Mesothelioma symptoms were detected with the help of data from mesothelioma patients. The dataset, however, included both healthy and mesothelioma patients. Classification algorithms for MM illness diagnosis were carried out using computationally efficient data mining techniques. The support vector machine outperformed the multilayer perceptron ensembles (MLPE) neural network (NN) technique, yielding promising findings. With 99.87% classification accuracy achieved using 10-fold cross-validation over 5 runs, SVM is the best classification when contrasted to the MLPE NN, which achieves 99.56% classification accuracy. In addition, SPSS analysis is carried out for this study to collect pertinent and experimental data