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.

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:

ShareGPT-style dialogues:
- Full discussions between a human and another actor (e.g., an AI) structured as interactive conversations.
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.

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.

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.

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}
}

Contact

For questions or feedback, please contact Ibrahim Belayachi at [email protected].

ibrahimBlyc
/

Llama_be_LA_