model development

This study reports on the development and validation of a questionnaire for exploring the different types of EFL teachers’ possible selves. First, a theoretical framework behind possible selves theory and its types was cultivated through an extensive review of the related literature and content analysis of 24 transcribed semi-structured interviews with ELT experts. Second, the questionnaire was developed and validated through collecting three types of evidence: content, reliability and construct. Content validity was insured by submitting the questionnaire to expert judgment, and Cronbach’s alpha was checked to measure the internal consistency reliability of the scale and its subscales. Finally, confirmatory and exploratory factor analyses as well as SEM were used to estimate the construct validity of the instrument administered to 380 EFL teachers. The results indicated that the questionnaire was both a valid and reliable measure of EFL teachers’ possible selves and the resultant model hypothesized based on the data collected from the questionnaire enjoyed acceptable fitness indices. The model of EFL teachers’ possible selves (L2 selves) consists of four types including ideal, ought-to, actual and feared selves. The paper is concluded by presenting the different senses of selves within each type which together constitute the whole model of L2 self-development and how the model can be used in future studies.

This study investigates the crucial components propelling the integration of green technology and blockchain technology within electronic education (e-learning). Employing a mixed-methods research design, the inquiry blends quantitative and qualitative methodologies for comprehensive analysis. The qualitative phase draws upon the expertise of 125 purposefully sampled academic experts, subject-area specialists, and e-learning professionals through semi-structured interviews. Content analysis dissects these qualitative data, while structural equation modeling and factor analysis illuminate the quantitative dimension. The findings unveil five key components orchestrating green technology development in e-learning with blockchain integration: causal conditions, central phenomena, strategies, background conditions, and intervening conditions. Further analysis within each component identifies critical sub-components, informed by expert insights. Collectively, the study's predictor variables, encompassing both green technology development components and blockchain technology utilization, demonstrate a significant predictive influence (0.36, p < 0.01) on the mediator variable, green technology development. Additionally, they exhibit a nuanced yet statistically significant predictive effect (0.16, p > 0.05) on the criterion variable, e-learning and educational software performance enhancement. These positive and direct associations suggest a robust model for integrating blockchain technology to propel green technology advancements within e-learning. The proposed five-component model sheds light on the interplay between key factors, emphasizing the substantial impact of its application in guiding effective integration strategies.