مدل یابی معادلات ساختاری یادگیری سیار براساس خودکارآمدی و یادگیری خودتنظیمی با نقش واسطه‌ای مهارت‌های فناوری اطلاعات و ارتباطات

نوع مقاله : مقاله پژوهشی

نویسندگان

1 دانشجوی دکتری روانشناسی تربیتی، دانشکده علوم انسانی، واحد سنندج، دانشگاه آزاد اسلامی، سنندج، ایران

2 استادیار، گروه روانشناسی، دانشکده علوم انسانی، واحد سنندج، دانشگاه آزاد اسلامی، سنندج، ایران

3 استادیار، گروه روانشناسی، دانشکده علوم انسانی، واحد بابل، دانشگاه آزاد اسلامی، بابل، ایران

10.22084/j.psychogy.2025.31265.2814

چکیده

هدف: هدف از انجام این پژوهش تعیین کفایت پیش‌بینی مدل یادگیری سیار براساس خودکارآمدی و یادگیری خودتنظیمی با نقش واسطه‌ای مهارت‌های فناوری اطلاعات و ارتباطات بود.
روش: پژوهش حاضر همبستگی و از نوع مدل معادلات ساختاری بود. جامعه آماری شامل کلیه دانش آموزان دختر دوره دوم متوسطه شهر تهران که به روش نمونه‌گیری تصادفی خوشه‌ای چندمرحله‌ای 386 نفر به‌عنوان نمونه انتخاب شدند. جهت گردآوری داده‌ها از پرسشنامه خودکارآمدی تلفن همراه کامپیو و هیگینز (1995)، مقیاس نگرش‌های یادگیری سیار آل‌عمران و همکاران (2016)، یادگیری خودتنظیمی آنلاین بارنارد و همکاران (2009) و مهارت‌های فناوری اطلاعات و ارتباطات ویلکینسون، رابرتز، وایل (2010) استفاده شد. تجزیه‌وتحلیل داده‌ها از طریق مدل معادلات ساختاری و نرم‌افزار spss و pls انجام شد.
یافته‌ها: براساس نتایج به‌دست‌آمده خودکارآمدی تلفن همراه بر یادگیری سیار تأثیر مثبت و معناداری داشت (0.001>p). مهارت‌های فناوری اطلاعات و ارتباطات بر یادگیری سیار تأثیر مثبت و معناداری داشت (0.001>p). یادگیری خودتنظیمی بر یادگیری سیار تأثیر مثبت و معناداری داشت (0.001>p). براساس نتایج به‌دست‌آمده تأثیر واسطه‌ای مهارت‌های فناوری اطلاعات و ارتباطات در رابطه خودکارآمدی تلفن همراه بر یادگیری سیار تائید نشد (0.001<p).
نتیجه‌گیری: براساس نتایج می توان نتیجه گیری کرد که ارتقای باورهای فردی، مهارت‌های فنی و توانایی تنظیم‌گری فراگیران، مستقیماً منجر به بهبود فرآیند یادگیری سیار می‌شود.

کلیدواژه‌ها

موضوعات

عنوان مقاله [English]

Structural Equation Modeling of Mobile Learning Based on Self-Efficacy and Self-Regulated Learning with the Mediating Role of ICT Skills

نویسندگان [English]

  • Elahehsadat Hamidi 1
  • Ata Shakerian 2
  • Jamal Sadeghi 3
1 PhD student in Educational Psychology, Faculty of human science, Sa. C., Islamic Azad University, Sanandaj, Iran
2 Assistant Professor, Department of Psychology, Faculty of human science, Sa. C., Islamic Azad University, Sanandaj, Iran
3 Assistant Professor, Department of Psychology, Faculty of human science, Bab. C., Islamic Azad University, Babol, Iran
چکیده [English]

Objective: The purpose of this study was to determine the predictive adequacy of a mobile learning model based on self-efficacy and self-regulated learning, with the mediating role of Information and Communication Technology (ICT) skills.
Methods: The present study was descriptive-correlational using Structural Equation Modeling (SEM). The statistical population comprised all female upper secondary school students in Tehran, of whom 386 were selected via multi-stage cluster random sampling. Data collection instruments included the Mobile Phone Self-Efficacy Questionnaire (Compeau & Higgins, 1995), Mobile Learning Attitudes Scale (Al-Emran et al., 2016), Online Self-Regulated Learning Questionnaire (Barnard et al., 2009), and ICT Skills Questionnaire (Wilkinson, Roberts, & While, 2010). Data analysis was performed using SEM via SPSS and SmartPLS software.
Results: The results indicated that mobile phone self-efficacy had a positive and significant effect on mobile learning (p<0.001). ICT skills had a positive and significant effect on mobile learning (p<0.001). Self-regulated learning also had a positive and significant effect on mobile learning (p<0.001). However, the mediating role of ICT skills in the relationship between mobile phone self-efficacy and mobile learning was not confirmed (p>0.001).
Conclusions: Based on the results, it can be concluded that enhancing individual beliefs, technical skills, and the self-regulatory abilities of learners directly leads to the improvement of the mobile learning process.
 

کلیدواژه‌ها [English]

  • Mobile Learning Model
  • Self-efficacy
  • Self-regulated Learning
  • ICT Skills

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