What question did this study set out to answer?

The aim is to create a dynamic framework for evaluating professional competencies and developing personalized career plans.

March 31, 2026Open Access

Value-added assessment of career planning for vocational competence based on deep learning

Puntos clave

The aim is to create a dynamic framework for evaluating professional competencies and developing personalized career plans.
Constructed the Deep learning-driven Value-added Career Analytics Model (DV-CAM).
Integrated Bidirectional Encoder Representations from Transformers (BERT) and Long Short-Term Memory (LSTM) to update competency profiles.
Developed a value-added evaluation index system for diagnosing development potential.
Utilized Deep Reinforcement Learning (DRL) to optimize career development actions based on competency and job requirements.
Achieved a mean squared error of 0.0312 and mean absolute error of 0.1289 for competency assessment.
Demonstrated a 185.3% improvement in long-term cumulative reward in planning tasks.
Achieved a 42.7% improvement in job matching degree, outperforming benchmark models.

Resumen

To address the static limitations of current career assessment methods and the lack of a long-term perspective in planning strategies, this study constructs an intelligent analysis framework named Deep learning-driven Value-added Career Analytics Model (DV-CAM). Integrating Deep Learning (DL) with value-added assessment, the framework aims to realize dynamic and accurate evaluation of individual professional competencies and generate personalized career planning paths that maximize the benefits of long-term development. Specifically, DV-CAM first leverages Bidirectional Encoder Representations from Transformers (BERT) and Long Short-Term Memory (LSTM) to extract and dynamically update individual competency profiles from multi-source time-series data. Second, it designs a value-added evaluation index system covering the absolute level of competency, growth slope, growth curvature and stability to quantitatively diagnose development potential. Finally, under the framework of Deep Reinforcement Learning (DRL), this study takes competency status, value-added potential and job requirements as joint state inputs, and solves for the optimal sequence of career development actions by optimizing long-term cumulative rewards. Experiments based on the public Occupational Information Network (O*NET) dataset show that DV-CAM achieves a mean squared error of 0.0312 and a mean absolute error of 0.1289 for competency assessment on the test set. In planning tasks, its long-term cumulative reward and target job matching degree improvement rate reach 185.3% and 42.7% respectively, both outperforming various benchmark models. The results demonstrate that DV-CAM can diagnose competency status more accurately, and effectively guide individuals to achieve long-term career development goals through its forward-looking planning strategies. By combining the concept of value-added assessment with advanced deep learning technologies, this study provides a data-driven, dynamically perceivable and long-term oriented new paradigm for the fields of career planning and competency assessment.

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Cite This Study

Wen Zhang (Sun,) studied this question.

synapsesocial.com/papers/69cb650ee6a8c024954b9136 https://doi.org/https://doi.org/10.1038/s41598-026-46485-y

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