Journal of Research in Educational Systems

Abstract The present study was conducted with the aim of presenting prediction of dependence on social networks based on differentiation and emotional communication patterns in teenagers of Tehran city in 2022. This research was descriptive-correlational in terms of the type of applied goal and the method of data collection. The statistical population was 90,000 students of the second secondary level in the 22nd district of Tehran, and 300 people were selected by multi-stage cluster sampling based on Morgan's table. In order to collect data, the social network dependence questionnaire of Kajbaf, Sashabani, Nikbakht and Farahani (2016), the Revised Scale of Family Communication Pattern (RFCP) Kothner and Fitzpatrick (2002) and the self-differentiation questionnaire of Skavron, Friedland (1998) were used. In order to analyze the data, Pearson correlation and stepwise regression tests were used. The correlation results showed that all the correlation coefficients are significant (p<0.01). Based on the obtained results, the correlation between differentiation and dependence on social networks is significant (p<0.01). The correlation between conformity orientation with dependence on positive social networks is significant and the correlation between dialogue orientation with dependence on negative social networks is significant. According to the obtained results, the dependence on social networks can be explained by using predictive variables of differentiation and communication patterns. The results of this study can help educational specialists in examining adolescent issues.

Abstract In this study, a model for validation and validation of social network-based mobile learning in higher education in Iran has been investigated. Mobile learning is a concept that is composed of a combination of two forms of education or e-learning and distance learning and is generally done via mobile and social media. The research method in this article is a mixed one. Firstly, based on qualitative method and specifically based on interviews with subject matter experts, the dimensions, components and indicators of mobile learning concept are extracted and then in the quantitative phase, by formulating a questionnaire. Based on the qualitative stage indicators and after implementing a WhatsApp social networking 'statistical basics' learning course, 300 participants in this questionnaire-based survey were recruited based on 281 data. Questionnaire was valid - validation and factor analysis of mobile learning model in higher education were conducted. The findings of the qualitative research showed that the mobile learning model has 4 components and 24 components. These dimensions include the "learner or learner" dimension, "interaction and communication", "tools and technology" and "infrastructure". Quantitative results also showed that in exploratory and confirmatory factor analysis, factor loadings of all indices were higher than 0.3, and goodness of fit indices showed good fit. Also, the indices had good reliability (Cronbach's alpha) in the model.