Connecting the dots to understand complex systems in education: A systematic review of psychological networks
Ahmed Tlili, Yan Wang, Ronghuai Huang, Dejian Liu, Thomas K. F. ChiuAbstract
Psychological networks have emerged as a robust methodology for examining complex systems. Although their use has become increasingly important in psychology and behavioural research, they remain underutilized in the field of educational research. Consequently, limited information is available regarding their adoption and the techniques employed to investigate various educational dimensions. To address this research gap, this systematic review study analysed 43 studies to examine the application of psychological networks in education, focusing on their methodologies and the educational objectives or phenomena explored. The findings revealed that psychological networks have been used to investigate six primary educational dimensions: cognitive, psychological, motivational, sociological and cultural, behavioural and knowledge and performance. Additionally, we identified several challenges associated with this approach, including the complexity of network interpretation, methodological complexity and integrity.
What is already known about this topic
Psychological networks are frequently used in psychology. The use of psychological networks in education is still in its infancy. Limited information exists on how psychological networks are applied in education and for which purposes.Practitioner notes
What this paper adds
This study conducts a systematic review to provide an in‐depth understanding of psychological networks in education. It reveals that psychological networks are mostly applied in language learning. Psychological networks are capable of modelling six educational dimensions, namely cognitive, psychological, motivational, sociological and cultural, behavioural and knowledge and performance. Psychological networks in education rely on several core models, including Gaussian Graphical Model, Mixed Graphical Model, Markov Random Field, Bayesian Gaussian graphical model and Vector Autoregression.