Self-Regulation as a Key Factor in Academic Motivation for Undergraduate Students in the United States | ||
| مجلة الدراسات التربوية والإنسانية | ||
| Volume 16, Issue 4 - Serial Number 1, October 2024, Pages 807-829 PDF (635.78 K) | ||
| Document Type: المقالة الأصلية | ||
| DOI: 10.21608/jehs.2024.424045 | ||
| Author | ||
| Fatimah Aljuaid | ||
| college of Arts, Department:Psychology, Educational Psychology | ||
| Abstract | ||
| Some undergraduate students exhibit a lack of academic motivation, which adversely affects their engagement and persistence in higher education (Busse & Walter, 2017; Rizkallah & Seitz, 2017; Dresel & Grassinger, 2013). Students lacking motivation are more likely to withdraw from school or disengage from learning activities, leading to underachievement (Wang & Pomerantz, 2009). Although there is a correlation between low academic motivation and deficiencies in self-regulation, relatively few studies have explored the impact of self-regulation on academic motivation, particularly in the U.S. This study seeks to investigate the role of self-regulation in fostering academic motivation. The sample comprised 349 undergraduate students from U.S. universities, recruited through the online platform QuestionPro. Participants completed the Academic Motivation Scale (AMS) and the Motivated Strategies for Learning Questionnaire (MSLQ) online, providing insights into their levels of academic motivation and self-regulation. Structural equation modeling was utilized to assess the influence of self-regulation on academic motivation. Data analysis revealed that the initial model did not fit the data well, with a Chi-square value of 271.569 (df = 40, p = .000) and poor fit indices (GFI = .875, NFI = .874, CFI = .889, RMSEA = .129, SRMR = .090). An exploratory analysis was conducted, and modifications were made based on modification indices and theoretical considerations to enhance the fit indices. The revised model demonstrated an acceptable fit between the theoretical and empirical covariance matrices (GFI = .918, NFI = .913, CFI = .928, RMSEA = .108, SRMR = .072), indicating that the data aligned with the hypothesized model. The overall adjusted model accounted for 41% of the variance in academic motivation, with self-regulation (β = .24; p < .01) identified as a significant predictor. The findings suggest that self-regulation can effectively predict students' academic motivation. Specifically, students employing advanced self-regulation strategies—such as time management, study environment optimization, and effort regulation—demonstrated higher levels of academic motivation. Further research is needed to identify additional factors that may influence academic motivation among students. This study provides recommendations for future research and professional practice. | ||
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