AI Learning Psychology Analyzer

Three specialized machine learning models for educational psychology analysis, trained on real educational datasets.

Models

Model	Accuracy	Purpose
Attention Tracker	67.8%	Monitor student attention levels
Cognitive Load Assessor	37.6%	Evaluate cognitive burden
Learning Style Detector	21.3%	Identify learning preferences

Quick Start

pip install huggingface-hub scikit-learn pandas numpy

from huggingface_hub import hf_hub_download
import pickle

# Download and load model
model_path = hf_hub_download(
    repo_id="Lazzaran/ai-learning-psychology-analyzer",
    filename="attention_tracker_model.pkl"
)

with open(model_path, 'rb') as f:
    model = pickle.load(f)

# Make prediction
prediction = model.predict([[0.8, 0.7, 0.9, 0.6, 0.8]])

Model Details

Attention Tracker

Type: Random Forest Classifier
Features: 5 educational engagement metrics
Classes: Critical, Low, Medium, High
Training: 1,840 samples

Cognitive Load Assessor

Type: Random Forest Classifier
Features: 5 cognitive load indicators
Classes: Optimal, Moderate, Overloaded, Critical
Training: 1,840 samples

Learning Style Detector

Type: Random Forest Classifier
Features: 5 learning behavior metrics
Classes: Visual, Auditory, Reading, Kinesthetic, Multimodal
Training: 1,840 samples

Technical Specifications

Framework: Scikit-learn
Preprocessing: StandardScaler normalization
Validation: Stratified K-fold cross-validation
Data: Real educational datasets (2,300 records)
Regularization: Applied to prevent overfitting

Use Cases

Personalized Learning: Adapt content based on student psychology
Early Intervention: Identify struggling students
Learning Optimization: Match teaching methods to learning styles
Educational Research: Study attention patterns and cognitive load

Citation

@software{ai_learning_psychology_analyzer,
  title={AI Learning Psychology Analyzer},
  author={Lazzaran},
  year={2024},
  url={https://huggingface.co/Lazzaran/ai-learning-psychology-analyzer}
}

License

MIT