Self-Directed Learning in Digital Environments: A Mixed-Methods Study on Learner Autonomy, Digital Competence, and Academic Engagement in Post-Pandemic Education
Description
This study uses a convergent mixed-methods research design, combining quantitative and qualitative methods to provide a comprehensive understanding of self-directed learning in digital environments. Quantitative data were collected through survey questionnaires to measure students’ levels of self-directed learning, digital competence, and academic engagement, while qualitative data were collected through documentary analysis and related academic records to provide contextual insights and support the interpretation of quantitative findings. Both datasets were collected and analyzed simultaneously, and the results were combined and compared to identify areas of convergence, complementarity, or divergence. This design strengthened the validity and depth of the study by enabling triangulation of multiple sources of evidence.
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This study employed a convergent mixed-methods design to ensure systematic and reproducible data collection and analysis. Quantitative data were gathered through a structured Self-Directed Learning (SDL) questionnaire, a digital competence scale, and an academic engagement survey, all using a five-point Likert scale. These instruments were distributed to 100 randomly selected student respondents via Google Forms and printed copies, depending on accessibility and availability. Prior to full administration, the instruments were reviewed by experts for content validity and pilot-tested to ensure clarity and reliability. Secondary data were obtained through formal requests to the participating institutions, with permission secured to access academic records, learning management system (LMS) logs, and relevant institutional reports. These documents were systematically reviewed using a documentary analysis checklist to extract indicators related to student engagement, task completion, and academic performance. For data analysis, quantitative responses were encoded and processed using statistical software (e.g., SPSS or equivalent spreadsheet-based tools) to compute descriptive statistics (mean and standard deviation) and inferential statistics (Pearson correlation). Qualitative and documentary data were analyzed using thematic analysis, involving coding, categorization, and theme development. Finally, results from both datasets were integrated through triangulation to identify convergences and divergences, ensuring a comprehensive interpretation of findings.