A time-lagged study of two possible routes from personal innovativeness to life satisfaction in adolescents: Learning and social interaction on mobile phones

Students’ Perceptions of the Actual Use of Mobile Learning during COVID-19 Pandemic in Higher Education

Mobile learning (M-learning) has become a significant component of higher education technology. Moreover, M-learning allows students to study, collaborate, and exchange ideas while using the internet and technology. Furthermore, an acceptance of M-learning is necessary for students and educators when it comes to using M-learning systems. However, in Saudi Arabia, not enough studies have been conducted to address students’ perceptions of their actual use of mobile learning for the purpose of education during the COVID-19 pandemic. Therefore, the current research aims to examine students’ satisfaction with their behavioral intention to use mobile learning, as well as their perceptions of their actual use of mobile learning during the COVID-19 pandemic in higher education. The research is based on the Technology Acceptance Model (TAM). The satisfaction and behavioral intentions of students to utilize mobile learning to make real use of mobile education was measured using four separate variables. A TAM survey was used to collect the majority of the data, with questionnaires being randomly distributed to 300 students from King Saud University. SPSS and Smart-PLS3.3.3 were used to analyze the data. The results in terms of the students’ satisfaction and behavior in using M-learning show that M-learning has a good and constructive influence on the actual usage of M-learning during the COVID-19 pandemic in higher education. As a result, we encourage students to use M-learning in their classrooms and to collaborate with their peers at higher education institutions. The study’s empirical findings aid in the integration of the TAM model in order to increase students’ M-learning performance.

Exploration of the Relationships Among Narcissism, Life Satisfaction, and Loneliness of Instagram Users and the High- and Low-Level Features of Their Photographs

We examined the associations between the characteristics of Instagram users and the features of their photographs. Narcissism, life satisfaction, and loneliness were employed for user variables and the features at high- (content) and low-levels (pixel) were employed to analyze the Instagram photographs. An online survey was conducted with 179 university students, and their Instagram photographs, 25,394 in total, were collected and analyzed. High-level features were extracted using Computer Vision and Emotion Application Programming Interfaces (APIs) in Microsoft Azure Cognitive Services, and low-level features were extracted utilizing the program written by the authors. The results of correlation analysis indicate that narcissism, life satisfaction, and loneliness were significantly associated with a part of photograph features at high- and low-levels. The results of the predictive analysis suggest that narcissism, loneliness in total, and social loneliness could be predicted with acceptable accuracy from Instagram photograph features, while characteristics such as life satisfaction, family loneliness, and romantic loneliness could not be predicted. Implications of this research and suggestions for further research were presented.