Automatic text readability assessment for educational content based on graph representation learning.

Area of Science:

• Artificial Intelligence
• Natural Language Processing
• Educational Technology

Background:

• Traditional readability assessment models often overlook complex linguistic patterns in educational texts.
• Existing AI-driven readability tools require enhancement, particularly for continuous scoring tailored to diverse learners.
• Current methods may not fully capture the nuances of text structure crucial for educational content.

Purpose of the Study:

• To propose a novel graph-based method for educational text readability assessment.
• To improve the accuracy and robustness of readability scoring using advanced AI techniques.
• To develop a continuous scoring system that better addresses varied learner needs.

Main Methods:

• Utilized Graph Convolutional Networks (GCNs) for readability assessment.
• Developed a novel graph construction technique incorporating syntactic dependencies and part-of-speech tags.
• Employed Bayesian Optimization for hyperparameter tuning and graph configuration refinement.

Main Results:

• Achieved a high performance score (0.9729) on the CLEAR dataset.
• Demonstrated meaningful accuracy when evaluated on a classification-based dataset.
• Validated the effectiveness of the graph-based approach in modeling text complexity.

Conclusions:

• The proposed GCN-based method significantly enhances educational text readability assessment.
• The novel graph construction and optimization techniques improve model accuracy and robustness.
• This approach offers a more effective continuous scoring system for diverse educational contexts.