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Artificial intelligence and cognitive diagnosis based teaching resource recommendation algorithm.

Zhi Mao1, Mingfang Li1

  • 1Xi'an Technological University, Xi'an, China.

Peerj. Computer Science
|December 11, 2023
PubMed
Summary
This summary is machine-generated.

Deep learning enhances educational resource recommendations. A new algorithm, ADCF, integrates deep learning and cognitive diagnosis, outperforming traditional methods for better teaching material suggestions.

Keywords:
Artificial intelligenceCognitive diagnosisDeep learningTeaching resource recommendation

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Area of Science:

  • Artificial Intelligence
  • Educational Technology
  • Computer Science

Background:

  • Deep learning analyzes data for predictions, driving innovation in education.
  • Current teaching resource recommendations can be improved with advanced AI.

Purpose of the Study:

  • To explore deep learning applications in educational resource recommendations.
  • To develop and evaluate a novel recommendation algorithm integrating deep learning and cognitive diagnosis.

Main Methods:

  • A bespoke recommendation algorithm, ADCF, was designed.
  • The ADCF model combines Multi-layer Perceptron (MLP) and Generalized Matrix Factorization (GMF).
  • The model utilizes linear representation and nonlinear learning for interaction functions.

Main Results:

  • The ADCF model achieved a hits ratio (HR) of 0.626.
  • The ADCF model achieved a Normalized Discounted Cumulative Gain (NDCG) of 0.339.
  • These results indicate superior performance compared to traditional models.

Conclusions:

  • The ADCF model demonstrates significant potential for improving teaching resource recommendations.
  • Integrating deep learning and cognitive diagnosis offers a promising approach for educational technology.
  • This advancement can lead to more effective teaching methodologies and curricula.