Question Difficulty Predictor

Inspiration

The inspiration for the Question Difficulty Predictor project stemmed from my observation of the increasing emphasis on personalized learning in education. I recognized that understanding the difficulty level of quiz questions could significantly aid educators in tailoring assessments to individual student needs. By leveraging machine learning, I aimed to create a tool that provides valuable insights into student performance, ultimately enhancing the learning experience.

What it does

The Question Difficulty Predictor is a machine learning application designed to predict the difficulty level of quiz questions based on various student performance metrics. Educators can input data such as user ID, question ID, time taken to answer, and correctness of answers into the web application. The model then analyzes these inputs to provide a predicted difficulty level for each question, helping educators create more balanced and effective assessments.

How I built it

A Random Forest model was trained to predict the difficulty level of quiz questions. The model was evaluated using appropriate metrics to ensure high accuracy and reliability.

Challenges I ran into

Model Selection: Choosing an appropriate model that balanced accuracy and interpretability was critical. After experimenting with several models, we selected the Random Forest for its robust performance.

What I learned

The importance of data preprocessing and feature engineering in building accurate machine learning models

Built With

• python