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Related Experiment Videos

A multi-factor data mining and transformer-based predictive modeling approach for career success using educational

Zhao Zihan1,2

  • 1Faculty of Education, Shaanxi Normal University, Xi'an, Shaanxi, 710062, China. zzh@xafy.edu.cn.

Scientific Reports
|November 11, 2025
PubMed
Summary

This study shows Bidirectional Encoder Representations from Transformers (BERT) model accurately predicts student career satisfaction using academic and behavioral data. The BERT model achieved 98% accuracy, outperforming traditional methods.

Keywords:
Artificial intelligenceAutomationBERTBehavioral traitsCareer satisfactionData miningDeep learningEducational traitsMachine learningStudent performance.Transformer

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Area of Science:

  • Educational Data Mining
  • Artificial Intelligence in Education
  • Career Development

Background:

  • The evolving job market driven by AI and automation requires enhanced career guidance for students.
  • Data mining offers powerful tools for analyzing educational and behavioral data to understand career satisfaction factors.

Purpose of the Study:

  • To investigate the effectiveness of data mining, specifically a transformer-based Bidirectional Encoder Representations from Transformers (BERT) model, in predicting student career satisfaction.
  • To compare the performance of the BERT model against traditional machine learning and deep learning approaches.

Main Methods:

  • Utilized a dataset encompassing students' academic achievements and behavioral traits.
  • Implemented a transformer-based BERT model with embedding layers and feed-forward networks.
  • Compared BERT performance with support vector machines, logistic regression, random forest, and gated recurrent units.

Main Results:

  • The BERT model achieved a classification accuracy of 98% in predicting career satisfaction.
  • Traditional machine learning and deep learning models achieved accuracies ranging from 80% to 85%.

Conclusions:

  • The BERT model significantly outperforms baseline methods in predicting career satisfaction.
  • The BERT model's ability to integrate complex, multifaceted features makes it a valuable tool for educational and career guidance.