Understanding teacher ICT usage: Insights from ICILS 2018 Korea data

Abstract

Encouraging the integration of ICT in educational settings remains a significant challenge globally. However, successful enactment is still limited, and more understanding is essential. The International Computer and Information Literacy Study (ICILS) 2018 was conducted to address this challenge systematically. This study focused on exploring the factors influencing teachers' utilization of technology, employing the Republic of Korea data from ICILS 2018as a pilot study. Grounded on the Cognitive Behavioral Theory (CBT) and Technology Acceptance Model (TAM), a three-layer model – comprising external factors, internal factors, and action layers – was formulated to predict teachers' technology usage and verified by using constructs from ICILS 2018. The external factors layer encompassed professional development, school ICT resources, and teachers' collaboration, while the internal factors layer included teachers' experience, ICT self-efficacy, emphasis on ICT and coding tasks in classrooms, and perceptions regarding ICT usage. Within the action layer were constructs measuring the use of ICT for teaching practices, digital learning tools, and general utility software. Partial Least Squares Structural Equation Modeling was used to evaluate the model. The findings strengthen both foundational theories. Specifically, the findings highlighted significant total effects from constructs in the external and internal factors layers on constructs in the action layer. In other words, the three-layer model can successfully explain the influential factors impacting teachers' usage of ICT, including specific causes and effects information. Beyond offering empirical evidence for theoretical frameworks, this study identified specific factors influencing technology usage among teachers and outlined potential pathways for changes in teachers’ practices affecting technology utilization. Further research is essential to delve into causal relationships and validate the model via empirical evidence.