Validation of the Conceptual Model of Blended Learning Among Nursing Students

The present study was conducted with the aim of validating a conceptual model of blended learning among nursing students at Lorestan University of Medical Sciences. In the qualitative phase, the statistical population included 15 experts and specialists in the field of medical education, as well as nursing department managers and faculty members with specific years of experience in medical education at Lorestan University of Medical Sciences. In the quantitative phase, the population consisted of all nursing students enrolled at Lorestan University of Medical Sciences during the 2022–2023 academic year. In this phase, the sample size was determined to be 209 using Cochran’s formula, and participants were selected through stratified random sampling. This study employed a mixed-methods approach. It was also categorized as fundamental research in terms of its objective and as descriptive-survey research in terms of data collection. The qualitative research tools included semi-structured interviews and a review of the literature and previous studies. In the quantitative phase, the data collection instrument was a researcher-made questionnaire with 41 items derived from the qualitative findings. The qualitative findings were analyzed using document analysis, library study, thematic analysis, and the Delphi method. In the quantitative phase, confirmatory factor analysis was employed to examine convergent validity and structural equation modeling, conducted using SPSS and AMOS software. The results of the qualitative phase revealed that the blended learning model emerged with five components: pedagogical strategies, educational objectives, instructional content, teaching methods, and educational evaluation. The findings from the quantitative data analysis showed that the model fit indices, except for the GFI and NFI indices, were above 0.90, indicating a satisfactory model fit. Furthermore, the model's chi-square value was less than 3, indicating that the empirical data appropriately support the study's conceptual model. Additionally, the Root Mean Square Error of Approximation (RMSEA) was around 0.05, further confirming the model’s fit. The model fit indices collectively indicated that the model achieved an adequate fit to the data. This section also included the visualization of the conceptual model and its evaluation using the Maximum Likelihood Estimation (MLE) method.