This model is a fine-tuned version of Qwen/Qwen3-8B for identifying and classifying student mathematical misconceptions. The model analyzes student explanations of math problems and predicts the specific misconception category they exhibit.
Model Architecture: Qwen3-8B (8 billion parameters) Task: Multi-class Text Classification (65 misconception classes) Performance: MAP@3 Score of 0.944
Intended Use
Primary Use Cases
Identifying mathematical misconceptions from student explanations
Educational assessment and personalized learning
Automated feedback systems for math education
Research in mathematics education
Out-of-Scope Use
General text classification tasks outside of math education
Real-time production systems without human oversight
Any application where misclassification could lead to harm
Training Details
Training Data
The model was trained on the MAP Charting Student Math Misunderstandings dataset, which includes:
Mathematical questions with multiple choice answers
Student explanations for their answer choices
Labels indicating whether the answer was correct
Misconception categories and specific misconceptions
This structure provides the model with full context: the question, the student's answer choice, whether it's correct, and their reasoning.
Preprocessing Steps:
Created target labels by combining Category and Misconception columns
Transformed labels into numerical format using label encoding
Identified correct answers and merged this information into the training data
Training Configuration:
Model: Qwen 3 8B
Method: Full Fine-tuning
Learning Rate: 2e-5
Epochs: 3
Batch Size: 16
Precision: Mixed precision (FP16/BF16)
Model Evaluation
The model was evaluated using the MAP@3 metric on the validation set from the competition, achieving a score of 0.944.
Evaluation Procedure:
Predictions were generated for the validation set
MAP@3 score was calculated based on the competition's evaluation script
Limitations & Bias
The model is specifically tuned for the MAP competition dataset and may not generalize to other text classification tasks.
There may be biases present in the training data that could affect the model's predictions.
Misclassifications could occur, especially in cases of ambiguous or unclear student explanations.
Acknowledgments
This model was developed as part of the MAP (Misconception Annotation Project) competition on Kaggle. Special thanks to the competition hosts and the Kaggle community for their support and collaboration.
How to Use This Model
To use this model for predicting math misconceptions:
