دانلود مقاله تاثیر افسردگی، خودکارآمدی و مدیریت منابع بر مشارکت یادگیری در یادگیری ترکیبی
خلاصه
1. مقدمه
2. بررسی ادبیات
3. روش ها
4. نتایج
5. بحث
6. محدودیت ها و پیشنهادات برای تحقیقات آتی
ضمیمه الف. موارد اندازه گیری مورد استفاده در این تحقیق
منابع
Abstract
1. Introduction
2. Literature review
3. Methods
4. Results
5. Discussion
6. Limitations and suggestions for future research
Appendix A. Measurement items used in this research
References
چکیده
این مطالعه روابط ساختاری بین خودکارآمدی، مدیریت منابع و مشارکت یادگیری را در دوران کووید-19 بر اساس نظریه خود تنظیمی بررسی کرد. ما همچنین بررسی کردیم که آیا سطح افسردگی روابط ساختاری بین عوامل را با مقایسه یک گروه غیر افسرده و یک گروه افسرده متوسط به بالا تعدیل می کند یا خیر. این مطالعه تأیید کرد که مدیریت منابع بدون توجه به سطح افسردگی بر تعامل یادگیری تأثیر می گذارد. خودکارآمدی برای یادگیری نیز بر مدیریت منابع تأثیر گذاشت. پیامدهای این مطالعه این است که خودکارآمدی پیش نیاز مدیریت منابع برای یادگیری است. با این حال، تأثیر مستقیم خودکارآمدی بر درگیری یادگیری تنها در گروه غیر افسرده مشاهده شد. خودکارآمدی برای یادگیری به طور غیرمستقیم بر مشارکت یادگیری از طریق مدیریت منابع در گروه افسرده تأثیر گذاشت. رفتارهای خودتنظیمی، مانند مدیریت منابع، باید برای افزایش مشارکت یادگیری دانش آموزان افسرده تشویق شوند. افسردگی دانش‌آموزان نیز باید به‌طور منظم کنترل شود تا به بهبود تعامل یادگیری در دوران کووید-19 و همچنین پس از آن کمک کند.
توجه! این متن ترجمه ماشینی بوده و توسط مترجمین ای ترجمه، ترجمه نشده است.
Abstract
This study examined the structural relationships among self-efficacy, resource management, and learning engagement during the COVID-19 era based on self-regulation theory. We also investigated whether the level of depression moderates the structural relationships among the factors by comparing a non-depressed group and a moderate-to-high depressed group. This study confirmed that resource management influenced learning engagement regardless of the depression level. Self-efficacy for learning also influenced resource management. The implications of this study are that self-efficacy is a prerequisite for resource management for learning. However, the direct influences of self-efficacy on learning engagement were observed only in the non-depressed group. Self-efficacy for learning indirectly influenced learning engagement through resource management in the depressed group. The self-regulated behaviors, such as resource management should be encouraged to enhance learning engagement of depressed students. Students' depression should also be monitored on a regular basis to help improve learning engagement during as well as after the COVID-19 era.
Introduction
The world has experienced the consequences of the COVID-19 pandemic since early 2020. This unprecedented pandemic has fundamentally changed our lives, including our educational pursuits. Most notably, how we teach and learn has changed from the dominance of face-to face classes to predominantly or fully online learning (i.e., synchronous or asynchronous) or blended learning due to social distancing. Many people have become highly stressed and uneasy due to the radical changes in the educational landscape.
As might be expected, students have encountered high levels of stress in the new learning environments that have emerged. Educators around the world have expressed tremendous concern about students' psychological well-being because they have been restricted from meeting their friends and teachers at school (Birmingham et al., 2021; Lischer, Safi, & Dickson, 2021). As an example, Villani et al. (2021) surveyed 501 Italian university students to examine their psychological well-being during COVID-19. They found that 72.93% of these students were depressed and 35.33% of the participants were anxious.
Results
Descriptive analysis
Descriptive analysis indicated that the participants scored above neutral (i.e., above 3 points) on a 5-point Likert scale for self-efficacy for learning (M = 3.55, SD = 0.80), effort regulation (M = 3.46, SD = 0.71), time and environment management (M = 3.72, SD = 0.59), and learning engagement (M = 3.53, SD = 0.65). The range of skewness and kurtosis was between −1 and 1, indicating that the four major variables were normally distributed. The correlations between variables ranged from 0.33 to 0.66 and they were statically significant at p < .001 (see Table 5).
To estimate the influences of three independent variables (i.e., self-efficacy, time and study environment management, and effort regulation) on learning engagement, multiple regression analysis was performed. The results indicated that self-efficacy (β = 0.38, t = 16.81 p < .001), effort regulation (β = 0.25, t = 9.60, p < .001), and time and study environment management (β = 0.20, t = 7.98, p < .001) significantly predicted learning engagement. The three variables predicted about 49.3% of learning engagement, R2 = 0.49, F (3, 1431) = 463.46, p < .001.