The Development of Digital Learning Ecosystem Indicators for Basic Education Institutions in Thailand: Model Invariance Analysis
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Abstract
This research aimed to 1) develop and validate the construct validity of a digital learning ecosystem measurement model for basic education institutions in Thailand, and 2) to analyze the measurement invariance of the model between schools located in urban and rural districts. The study sample comprised teachers from basic education institutions across six regions of Thailand, divided into two groups: 212 teachers from urban schools and 208 teachers from rural schools, selected through multi-stage random sampling. Data was collected using a digital learning ecosystem measurement tool consisting of 56 items with a 5-point rating scale, covering two components and eight indicators of digital learning ecosystem skills. The data was analyzed using mean, standard deviation, confirmatory factor analysis, and multi-group analysis. The results revealed that 1) the digital learning ecosystem measurement model consists of two main components. The first component, Digital Learning Community, includes four indicators: teacher role, learner role, supporter role, and stakeholder role. The second component, Digital Learning Support, also comprises four indicators: learning support technology, school infrastructure, digital learning content, and school management. The model demonstrated good fit with empirical data for both urban and rural school locations. All eight indicators showed construct validity with factor loadings ranging from .571 to .898 for urban schools and .612 to .885 for rural schools, significant at the .01 level. This indicates that these indicators can be effectively used to measure the digital learning ecosystem in basic education institutions in Thailand, and 2) the measurement invariance analysis revealed that the model exhibited invariance in form, factor loadings, and intercepts across urban and rural groups. However, non-invariance was observed in measurement errors across the groups.
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