Successful students are more than just those who have more effective and efficient learning techniques for acquiring and applying information. They can also motivate, evaluate, and adjust their behavior if they are not learning properly. Thus, the objective of this study was to investigate the influence of university students’ self-management during their learning experience and their self-efficacy on their academic achievement. Additionally, the study investigated the differences between the Egyptian and Saudi students’ perceptions of self-management skills and self-efficacy in their academic achievement within the two countries. A total of 889 students from two different Arab countries took part in the study (Egypt and the Kingdom of Saudi Arabia). The sample was given an online questionnaire to evaluate their self-management abilities, perceived self-efficacy, and academic achievement. A quantitative approach using SmartPLS-SEM was deployed. The findings demonstrate that self-management and self-efficacy have positive influences on students’ academic achievement in both countries. Further, self-management skills have been proven to influence self-efficacy, which in turn highly influences academic achievement. Moreover, the findings of the Multi-Group Analysis (MGA) did not report significant differences between the Egyptian and Saudi students in terms of their perception of self-management, self-efficacy, and academic achievement.

Purpose This study investigates the relationship between self-compassion and life satisfaction, and there is a significant statistical correlation between some dimensions of the self-compassion scale (family, self-kindness, common human feelings and mental alertness). Design/methodology/approach The researcher used the Self-Compassion scale prepared by Neff (2003) translated by the researchers, in a sample of 150 students in Egypt, and Multidimensional Student’s Life Satisfaction Scale, developed by Huebner et al. (1998) translated by the researchers. Findings The results of the study showed that self-compassion is high in university students. The study also showed a negative correlation with the dimension of psychological self-judgment and life satisfaction, as it indicated the possibility of predicting life satisfaction through the dimensions of self-compassion, except for the dimensions of isolation and autism, and excessive communication with the self. It also indicated that there are no differences between males and females as far as the variable of self-compassion, as well as the absence of differences between males and females as far as the variable of satisfaction with life is concerned. However, the family dimension showed a difference in favor of males. Originality/value The inclusion of extension programs to develop self-compassion for various segments of society in light of the continuing corona pandemic, and paying attention to religious counseling programs that support the use of spiritual values in self-strengthening which is reflected in the strengthening of psychological resilience and thus a sense of satisfaction with life.

Several problems remain, despite the evident advantages of sentiment analysis of public opinion represented on Twitter and Facebook. On complicated training data, hybrid approaches may reduce sentiment mistakes. This research assesses the dependability of numerous hybrid approaches on a variety of datasets. Across domains and datasets, we compare hybrid models to singles. Text tweets and reviews are included in our deep sentiment analysis learning systems. The support vector machine (SVM), Long Short-Term Memory (LSTM), and ghost model convolution neural network (CNN) are combined to get the hybrid model. The dependability and computation time of each approach were evaluated. On all datasets, hybrid models outperform single models when deep learning and SVM are combined. The traditional models were less trustworthy, and deep learning algorithms have recently shown their enormous promise in sentiment analysis. Linear transformations are used in feature maps to eliminate duplicate or related features. The ghost unit makes ghost features by taking away attributes that are both similar and duplicated from each intrinsic feature. LSTM produces higher results but takes longer to process, while CNN needs less hyperparameter adjusting and monitoring. The effectiveness of the integrated model varies depending on the work, and all performed better than the others. For hybrid deep sentiment analysis learning models, LSTM networks, CNNs, and SVMs are needed. Hybrid models are used to compare SVM, LSTM, and CNN, and we tested each method’s accuracy and errors. Deep learning-SVM hybrid models improve sentiment analysis accuracy. Experimental results have shown the accuracy of the proposed model shown 91.3 percent and 91.5 percent for datasets type 1 and 8, respectively.