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# Requirements
## Intelligent Recommendation System for Coding Courses Based on Learner Behavior
This document describes the technical, computational, and organizational requirements necessary to reproduce, extend, and evaluate the proposed intelligent recommendation system.
---
## 1. Programming Language
- **Python ≥ 3.9**
Python is selected due to its strong ecosystem for:
- Machine Learning and Deep Learning
- Sequential modeling
- Scientific computing
- Research reproducibility
---
## 2. Core Libraries
### 2.1 Machine Learning & Deep Learning
- **PyTorch**
- Implementation of GRU-based Knowledge Tracing model
- Training and evaluation of sequential neural networks
- **NumPy**
- Numerical computations
- Efficient array operations
- **Scikit-learn**
- Evaluation metrics (AUC, accuracy, precision, recall)
- Data preprocessing utilities
---
### 2.2 Data Processing & Analysis
- **Pandas**
- Handling large-scale interaction logs
- Dataset manipulation and aggregation
---
### 2.3 Visualization & Learning Analytics
- **Matplotlib**
- Learning progress curves
- EMA and cumulative accuracy plots
- **Seaborn**
- Knowledge State Heatmap visualization
- High-level statistical graphics for interpretability
---
## 3. Project Structure Requirements
The project follows a modular and research-oriented structure to ensure clarity, scalability, and academic reproducibility.
4. Dataset Requirements
4.1 Dataset Characteristics
The system assumes an educational interaction dataset with the following schema:
- student_id
- problem_id
- timestamp
- correctness (binary: 0 / 1)
4.2 Dataset Properties
- Sequential and time-ordered
- Sparse interaction matrix
- Implicit feedback (behavior-based inference)
4.3 Data Availability Policy
Important Notice:
The data/ directory is intentionally excluded from the public GitHub repository due to:
- Learner privacy protection
- Ethical research standards
- Dataset licensing restrictions
Researchers wishing to reproduce results must:
- Use a licensed educational dataset
- Or generate a synthetic dataset with identical structure
This decision aligns with best practices followed by top-tier academic institutions.
5. Hardware Requirements
Minimum Requirements:
- CPU-based execution
- 8 GB RAM
Recommended for Research-Scale Experiments:
- GPU (NVIDIA CUDA-enabled)
- ≥ 16 GB RAM
> GPU acceleration significantly reduces training time for GRU-based models on long learner sequences.
6. Reproducibility Considerations
- Fixed random seeds are recommended
- Consistent train-validation-test splits should be used
- Model checkpoints should be saved during training
- Hyperparameters should be documented in the experiment logs
7. Ethical & Academic Compliance
This project is designed in compliance with:
- Educational data mining ethics
- Reproducible research principles
- Transparent machine learning standards
> The system is intended for research and educational use, not for automated high-stakes decision-making.
8. Summary
The requirements outlined above ensure that the project:
- Meets academic research standards
- Can be reliably reproduced and extended
- Is suitable for evaluation in top-tier university environments
- Aligns with ethical and legal data handling practices
This foundation enables future researchers to build upon the system with confidence and clarity.