Structural Equation Modeling for Predicting Academic procrastination Based on Cyberspace Addiction and Executive Functions in First Secondary Female Students in Kerman

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Article Type:
Research/Original Article (دارای رتبه معتبر)
Abstract:

Given that academic procrastination originates from various factors, two of the most important factors in this field are cyberspace addiction and executive functions. Therefore, this research aims to model structural equations for predicting academic procrastination based on cyberspace addiction and executive functions. The research method is a descriptive-correlation type of structural equation modeling. The statistical population of this research includes 8150 female first-secondary school students in the city of Kerman in the academic year 2022-2023; according to Morgan's table, 384 people have been selected as a sample using stratified random sampling. Widyanto and McMorran's (2004) cyberspace dependence, Brief's (2000) executive functions, and Solomon and Rothbloom's (1984) academic procrastination questionnaires were used to collect data. Data analysis was done with SPSS25 and AMOS24 software, and descriptive and inferential statistics such as mean, standard deviation, and structural equation modeling were used. The research results showed that the model for predicting academic procrastination based on dependence on cyberspace and executive functions has an acceptable fit. There is a significant relationship between cyberspace addiction, executive functions, and academic procrastination (p<0.05), so cyberspace addiction and executive functions simultaneously explain 0.70 of the variance of academic procrastination. Therefore, it can be concluded that the high dependence on cyberspace and spending much time aimlessly in this space cause students to procrastinate in doing their homework and to suffer from a kind of procrastination in academic subjects. Also, defects in executive functions aggravate the academic procrastination of students.

Language:
English
Published:
Iranian Journal of Learning and Memory, Volume:6 Issue: 22, Summer 2023
Pages:
43 to 53
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