Student Portraits and Their Applications in Personalized Learning: Theoretical Foundations and Practical Exploration

Abstract

As a data-driven analysis and decision-making tool, student portraits have gained significant attention in education management and personalized instruction. This research systematically explores the construction process of student portraits by integrating knowledge graph technology with advanced data analytics, including clustering, predictive modelling, and natural language processing. It then examines the portraits’ applications in personalized learning, such as student-centric adaptation of content and paths, and personalized teaching, especially the educator-driven instructional adjustments. Through case studies and quantitative analysis of multimodal datasets, including structured academic records, unstructured behavioural logs, and socio-emotional assessments, the research demonstrates how student portraits enable academic early warnings, adaptive learning path design, and equitable resource allocation. The findings provide actionable insights and technical frameworks for implementing precision education.

Graphical abstract

Keywords

student portraits / personalized teaching and learning / knowledge graph / learning analytics / academic risk prediction

References

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Acknowledgment

This paper was supported by the Graduate Education and Teaching Reform Research Project of Beijing University of Posts and Telecommunications, China (Grant No. 2023Y028).

Conflicts of Interest

The authors declare that they have no conflicts of interest.

Ethics Statements

The authors declare that their Institutional Ethics Committee confirmed that no ethical review was required for this study. Written informed consent for participation was not required because all participants’ data was anonymized before the statistical analyses were conducted.

Data Availability Statements

The data supporting the findings of this study are available from the corresponding author upon reasonable request.

Authors Contributions

Yawen Li made substantial contributions to the conception and design of the work, participated in the acquisition and analysis of data, and critically revised the manuscript for important intellectual content; Zongxuan Chai contributed to data interpretation, created graphical visualizations, and provided substantial technical translation support; Shuai You conducted critical analysis of policy implications and potential technology applications, performed manuscript translation, and led the final revision process; Guanhua Ye and Qi Liu jointly drafted the initial work and participated in its critical revision through iterative intellectual refinement. All authors approved the version to be published and agree to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.

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