ibrahimBlyc/Llama_be_LA_

Model Card: Fine-tuned LLaMA 3.2 Model

Model Description

This model is a fine-tuned version of LLaMA 3.2, designed specifically for tasks in the domain of learning analytics and education systems improvement. It has been trained on a carefully curated dataset that includes question-answer pairs and dialogue data, ensuring high-quality responses tailored to educational and analytical contexts.

Key Features:

• Base Model: LLaMA 3.2
• Fine-tuning Approach: Supervised fine-tuning with a question-answer structured dataset.
• Domains Covered: Education systems, learning analytics, review/meta-analysis literature, and strategies for academic success.

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Training Data

The fine-tuning dataset was crafted with precision to ensure the quality and relevance of the model's responses. The dataset contains thousands of entries with two primary formats:

1. ShareGPT-style dialogues:

   • Full discussions between a human and another actor (e.g., an AI) structured as interactive conversations.

2. Alpaca-style question-answer pairs:

   • Data structured with concise input and output information in a Q&A format.

Dataset Creation Process:

1. Literature-Based Question-Answer Pairs:

• Lens.org Collection:

  • Papers filtered using keywords such as "review" and "meta-analysis".
  • Abstract sections were extracted for concise summaries of objectives, methods, and conclusions.
  • A Python program utilizing the Gemini API was used to generate relevant questions for each abstract.
  • Data Size: 14,000 question-answer pairs.

• Scopus.com Collection:

  • Focused on the keyword "learning analytics."
  • An additional 8,000 question-answer pairs were generated using the same methodology.

2. ChatGPT Recommendations for Education System Improvements:

• High-quality recommendations generated by ChatGPT on topics such as:
  • Reducing dropout rates.
  • Combating academic failure.
  • Supporting student success.
• Data Size: 544 question-answer pairs.

Example of Dataset:

[
  {
    "instruction": "What are the key factors influencing student success?",
    "output": "Key factors include teacher effectiveness, parental involvement, and access to educational resources."
  },
  {
    "instruction": "How can dropout rates be reduced?",
    "output": "Dropout rates can be reduced by implementing early intervention programs, providing mentorship opportunities, and addressing socio-economic barriers."
  }
]

Dataset Highlights:

• Over 22,500 entries spanning multiple sub-domains within education and learning analytics.
• Data curated to ensure clarity, relevance, and high-quality question-answer pairs.

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Model Performance

Intended Use Cases

• Education Research: Assisting researchers and educators in analyzing learning trends and strategies.
• Learning Analytics: Providing insights into educational systems, success factors, and intervention strategies.
• Academic Assistance: Answering domain-specific questions in education.

Limitations

• The model is fine-tuned for education and learning analytics; its performance in unrelated domains may vary.
• Limited coverage of topics outside the dataset's scope.

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Ethical Considerations

• The model may reflect biases present in the training data, such as those inherent in academic literature or AI-generated content.
• Users should verify critical outputs, especially in high-stakes scenarios such as policy-making or educational interventions.

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Citation

If you use this model in your research or applications, please cite:

@misc{llama3_finetuned_education,
  title={Fine-tuned LLaMA 3.2 for Learning Analytics},
  author={Ibrahim Belayachi},
  year={2025},
  howpublished={\url{https://huggingface.co/ibrahimBlyc/Llama_be_LA_}},
  note={Fine-tuned on education and learning analytics datasets}
}

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Contact

For questions or feedback, please contact Ibrahim Belayachi at ibrahim.belayachi@etu.utc.fr.