Pathways to enhancing educational quality: Analyzing combinations of key factors in smart education systems

• In Event: Topics in educational research on schools: smart educational systems, digital technologies in Indian schools, school counselling and harnessing process data

Proposal

1 Research Abstract
Amidst rapid global education changes, technology is viewed as a key driver of innovation. UNESCO’s Futures of Education initiative emphasizes that while technology reshapes learning, education’s true potential lies in fostering a just and sustainable future. However, the 2023 Global Education Monitoring Report raises concerns about whether technology has truly transformed education, particularly regarding equity and scalability (UNESCO, 2023). To address these challenges, smart education, proposed by Ronghuai Huang (2014), aims to innovate learning environments and modernize education. Since 2018, China has promoted smart education through 18 "Smart Education Demonstration Zones," using technology to improve quality and equity. China's success has gained global recognition, with UNESCO publishing annual reports since 2020 and the Commonwealth of Learning’s 2022 framework spreading these practices. The five constructive features of smart education—student competency, teacher development, technology use, policy, and cross-sector collaboration—align with global educational trends.
This study employs fuzzy-set qualitative comparative analysis to explore how different combinations of conditions synergize within the framework of smart education to enhance educational quality. In particular, this research will examine how these indicators interact in practice across various countries and regions globally, identifying the optimal combinations of factors such as technology, teacher professional development, and policy support. This, in turn, will provide empirical evidence for promoting equity, sustainability, and digital transformation in global education.
2 Relevance
This study aligns with CIES by addressing how smart education, as a key component of educational digital transformation, integrates advanced technologies like AI, IoT, and connectivity to reshape educational systems. Smart education focuses on optimizing the use of these technologies to enhance teaching, learning, and administrative processes, directly engaging with the conference's emphasis on the role of technology in transforming education while tackling challenges such as equity, privacy, and security. This study reveals how factors like technology, teacher development, and policy support interact across different global contexts to promote educational equity and sustainability. This directly aligns with the conference's core focus on reimagining the future of education in the context of technological influence, offering forward-thinking policy and practical insights for educational reform in a digital society.
3 Theoretical framework
Our research is grounded in two key theoretical frameworks: Bronfenbrenner’s Ecological Systems Theory (1979) and Huang Ronghuai’s Three-Domain Theory (2014) of smart education. Bronfenbrenner’s Ecological Systems Theory posits that an individual's development is influenced by multiple, interacting environmental systems. These include the microsystem (direct interactions with family, school, etc.), the mesosystem (connections between microsystems), and the macrosystem (the broader sociocultural context), along with the chronosystem, which accounts for changes over time. This theory emphasizes that development is shaped by the complex, multi-layered interactions within social environments. Similarly, Huang’s Three-realm Theory of smart education, a key framework for understanding digital transformation in education, operates at three levels: at the micro level, it focuses on the integration of technology in teacher-student interactions; the meso level emphasizes cross-departmental collaboration and infrastructure development; and the macro level addresses national policies and resource allocation.
In this research, ecological systems theory provides a foundational lens for analyzing how various factors interact within smart education. For example, teacher professional development, student competencies, and technology integration function at the microsystem level, while cross-sectoral collaboration and infrastructure fall under the mesosystem, and national policies and resource allocation shape the macro system. This layered framework allows us to explore the dynamic interplay between different factors influencing smart education outcomes.
4 Research method and data collection
This study uses fuzzy-set Qualitative Comparative Analysis as the main research method, ideal for exploring complex social phenomena by identifying the interdependencies between multi-level conditions through fuzzy-set membership scores (Ragin, 2008). This research applies fsQCA to model the five constructive features of smart education and their sub-indicators, analyzing how condition combinations improve education quality by calibrating data, generating a truth table, and simplifying combinations to reveal key patterns.
The data for this study are drawn from several international datasets, primarily including PISA, TALIS, World Bank, and ITU, covering 81 countries and regions. To assess the effectiveness of smart education implementation, the study extends the five CFs by incorporating relevant sub-indicators aligned with key elements outlined in the Global Smart Education framework. Specifically, PISA data measure student learning outcomes, while TALIS data evaluate teacher preparedness and the use of technology, providing a robust empirical foundation for the study.
5 Findings and conclusions
Through this analysis, several key condition combinations were identified. Path 1 shows that the combination of high teacher development, technology application, and policy support is the most effective pathway for enhancing educational quality, as these core elements of smart education work together to drive significant improvements. Path 2 underscores the role of multi-sector collaboration, demonstrating that even with moderate levels of policy support and student development, collaboration across sectors can enhance educational outcomes. Path 3 reveals that, despite moderate teacher development, strong technology application and policy support can still lead to significant improvements, suggesting that robust support in technology and policy can compensate for weaknesses in other areas, optimizing the educational system. In conclusion, the optimal combination for improving smart education quality involves high levels of teacher development, technology, and policy support. An alternative pathway shows that strong technology and policy can offset lower teacher development. Additionally, multi-sector collaboration plays a vital role, especially in systems with strong policy backing, helping to overcome other weaknesses and enhance educational quality.
6 Contribution and significance
This study significantly contributes to both originality and existing knowledge. First, it addresses a literature gap by conducting a multi-dimensional interaction analysis. It examines the combined effects of student abilities, teacher development, technology integration, policy planning, and cross-sector collaboration, uncovering the complex mechanisms that drive education quality improvement. Second, it extends ecological systems theory within the context of smart education by analyzing interactions across macro, meso, and micro levels, addressing the lack of a comprehensive multi-level framework in current research. Additionally, the study responds to concerns in the GEM report by providing empirical evidence on how technology enhances education quality, thereby enriching both the theoretical and practical foundations of smart education.

Authors

• Haoyue Wang, Columbia University Teachers College

• mengyu liu