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Related Experiment Video

Updated: Jun 29, 2026

Artificial Intelligence-Based System for Detecting Attention Levels in Students
06:37

Artificial Intelligence-Based System for Detecting Attention Levels in Students

Published on: December 15, 2023

Academic early warning model based on machine learning and model application.

Qiang Li1,2, Yihan Liu3, Rui Ma1

  • 1College of Engineering, Hebei Normal University, Shijiazhuang, China.

Scientific Reports
|June 4, 2026
PubMed
Summary
This summary is machine-generated.

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This study developed a machine learning-based academic early warning system to predict and prevent student academic crises. The dynamic system achieved 96.32% accuracy, offering a valuable tool for higher education institutions.

Area of Science:

  • Educational Technology
  • Machine Learning in Education
  • Higher Education Management

Background:

  • Expansion of higher education presents challenges in managing student academic completion and crises.
  • Need for proactive interventions to address diverse academic challenges faced by students.
  • Existing systems lack dynamic prediction and personalized intervention capabilities.

Purpose of the Study:

  • To design and implement a dynamic academic early warning system using machine learning.
  • To predict and intervene in students' academic crises effectively.
  • To enhance the management of higher education through data-driven insights.

Main Methods:

  • Constructed an academic early warning indicator system using fuzzy comprehensive evaluation and analytic hierarchy process (AHP) with 10 key indicators.
Keywords:
Academic early warningEducational managementFCE-AHPMachine learningNeural networkRadial basis function

Related Experiment Videos

Last Updated: Jun 29, 2026

Artificial Intelligence-Based System for Detecting Attention Levels in Students
06:37

Artificial Intelligence-Based System for Detecting Attention Levels in Students

Published on: December 15, 2023

  • Developed a predictive model using a radial basis function (RBF) neural network, comparing its performance against recurrent neural network (RNN) and Softmax regression.
  • Built a user-friendly system interface using web technologies (HTML, CSS, JavaScript) and Python for personalized academic alerts.

    • Main Results:

    • The RBF neural network model demonstrated superior prediction accuracy and convergence speed compared to RNN and Softmax regression.
    • The implemented system achieved a high accuracy rate of 96.32% and a root mean square error (RMSE) of 0.2926 on large-scale student datasets.
    • The system showed high sensitivity and accurate recognition capabilities, meeting practical academic early warning requirements.

    Conclusions:

    • The developed machine learning-based academic early warning system provides an effective tool for higher education institutions.
    • The system supports the development of smart and digital campuses by offering personalized academic alert services.
    • Future research should validate findings across multi-disciplinary and gender-balanced student populations for broader applicability.