# Research hotspots and frontiers of profile technology applied in education—a visualization analysis based on CiteSpace

## TL;DR

This study uses CiteSpace to analyze 300 papers on profile technology in education, identifying key research areas and future directions.

## Contribution

The paper reveals the evolution of profile technology in education from static analysis to dynamic adaptive systems.

## Key findings

- Three core research clusters were identified: learner analysis, intelligent EDM, and blended learning governance.
- The field is transitioning from static portrayal to dynamic, precise intervention mechanisms.
- The study highlights challenges in algorithmic fairness and ethics in intelligent education systems.

## Abstract

This study takes 300 literatures (from 2014 to 2024) included in the Web of Science Core Collection (mainly SSCI and SCI-E) as the data source and uses CiteSpace 6.3.R1 software to analyze the evolution of Profile Technology Applied in Education (PTAE). The research identified three core clusters: learner analysis and recommendation, intelligent technology-driven Educational Data Mining (EDM), and governance of the blended learning ecosystem. It reveals the deep transformation path of the field from basic feature characterization through data-driven prediction to a panoramic intelligent ecosystem. The research finds that the core research model is transitioning from static portrayal to a dynamic and precise intervention mechanism based on a panoramic learning analytics framework, establishing the underlying logic of modern adaptive systems Based on the findings, this paper explores algorithmic fairness and ethics challenges, and proposes practical strategies for building an ecologically adaptive intelligent educational environment.