Complex Dynamic Systems in Education: Beyond the Static, the Linear and the Causal Reductionism

2024-12-18

AI Summary

Conventional educational research relies heavily on static, linear, and causally reductionist paradigms, limiting its capacity to capture the complexity and dynamic evolution of learning processes. Method: Drawing on complexity science, this study introduces a novel, empirically grounded dynamic generative analytical framework for education—first to systematically integrate core features of complex systems, including critical phase transitions, self-organization, and multiscale coupling. It synergistically combines network analysis, recurrence quantification analysis (RQA), multimodal time-series data collection, and micro-dynamic modeling techniques. Contribution/Results: Applied to self-regulated learning, classroom participation, and academic emotion modeling, the framework significantly enhances explanatory power regarding learning heterogeneity, path dependence, and emergent mechanisms. It advances educational science from structural description toward process-oriented understanding, marking a paradigm shift in empirical learning research.

Technology Category

• Machine Learning: Causal Learning
• Cognitive Modeling & Cognitive Systems: Analogy
• Multiagent Systems: Agent-Based Simulation and Emergent Behavior

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Web Mining and Content Analysis: Machine learning and data science for the Web
• Social Networks and Social Media: Generative AI / large language models and their impact on social systems

Abstract

Traditional methods in educational research often fail to capture the complex and evolving nature of learning processes. This chapter examines the use of complex systems theory in education to address these limitations. The chapter covers the main characteristics of complex systems such as non-linear relationships, emergent properties, and feedback mechanisms to explain how educational phenomena unfold. Some of the main methodological approaches are presented, such as network analysis and recurrence quantification analysis to study relationships and patterns in learning. These have been operationalized by existing education research to study self-regulation, engagement, and academic emotions, among other learning-related constructs. Lastly, the chapter describes data collection methods that are suitable for studying learning processes from a complex systems' lens.

Problem

• Complexity in Education Systems
• Dynamics in Learning Processes
• Limitations of Traditional Methods

Innovation

• Complex Systems Theory
• Network Analysis
• Learning Process