Potential Abstract:
Abstract: In educational research, the use of causal models and artificial intelligence has become increasingly prevalent in providing deeper insights into the complex dynamics of learning and teaching. Traditional empirical analyses often rely on standard statistical methods that may struggle to capture the intricate relationships and causality within educational data. This study proposes a novel approach that integrates causal models with artificial intelligence techniques to enhance empirical analyses in education. By leveraging the power of causal models, such as structural equation modeling and Bayesian networks, alongside machine learning algorithms, this research aims to uncover hidden patterns, infer causal relationships, and make more accurate predictions in educational settings.
The proposed methodology will be applied to a large dataset of student performance measures, instructional strategies, and demographic information to demonstrate its effectiveness in informing educational practice and policy decisions. By comparing the results with those obtained through traditional statistical methods, we aim to showcase the added value of incorporating causal models and artificial intelligence in educational research. This study contributes to the ongoing discourse on methodological advancements in educational research and provides a roadmap for researchers looking to adopt innovative approaches in their empirical analyses.
Potential References:
- The case for evaluating causal models using interventional measures and empirical data
- Causal artificial intelligence for high-stakes decisions: The design and development of a causal machine learning model
- An empirical study of encoding schemes and search strategies in discovering causal networks
- A Data-driven Causality Modeling Framework: An Empirical Study of Modeling the Effect of Indoor Air Quality Perception on Students’ Cognitive Performance
- Causal diagrams for empirical research
