Technological self-efficacy, motivation, and contextual factors in advanced EFL e-learning: a mixed-methods study of strategy use and satisfaction

Abstract This mixed-methods study, informed by Social Cognitive Theory, investigates the interplay of technological self-efficacy, intrinsic motivation, and contextual factors in shaping e-learning strategy use and overall satisfaction among advanced EFL university students. A quantitative survey of 147 students provided broad insights, which were enriched by a qualitative case study of three learners offering detailed perspectives. Quantitative results revealed significant positive correlations between technological self-efficacy and both e-learning strategy use and overall satisfaction, as well as between contextual factors and these same outcomes. While intrinsic motivation showed significant correlations with strategy use and satisfaction, it did not directly predict overall satisfaction in the regression analysis. However, this highlights its role in shaping the learners’ choice and application of strategies. The regression model also showed that both technological self-efficacy and positive perceptions of contextual factors were significant direct predictors of overall satisfaction. The analysis of qualitative data underscored the importance of diverse learning strategies (cognitive, metacognitive, and social-affective) and highlighted key contextual factors, such as structured curricula, formative assessments, interactive instruction, and opportunities for peer interaction. Furthermore, findings indicated that years of study influenced e-learning strategy use, and that prior online learning experience was linked to overall satisfaction. These findings emphasize the need for e-learning environments that foster technological self-efficacy and positive perceptions of contextual factors, and that support intrinsic motivation through engagement and challenge. Further research is recommended to explore these complex interactions in varied learning contexts and with larger and more diverse samples to identify specific patterns and developmental trajectories in e-learning.