Teacher's AI aide: Code for analyzing multiple choice test results Link to Website

Project Overview

This repo has python scripts for data mining multiple choice test results. It has been designed to work on top of existing flubaroo output. It is hoped that additional analytics can help teachers design better multiple choice exams and learn something more about their students. This project is under sporadic development.

Install (For Developers)

TODO

Install (For Teachers)

Here are instructions for teachers to get the code to run on their own text outputs.

• Windows TODO
• Mac TODO

Features

Simple Statistics

• Distribution of scores TODO
• Percentile distribution TODO

Question Analysis

• Question Difficulty TODO
  • What percent of students got this questions wrong?
  • If students were randomly guessing, how likely would the observed answer be?
• Misconception Analysis TODO
  • Is a student's overall performance predictive of what they answered on a question?
• Question Discrimination TODO
  • How predictive is a question of the student's overall performance?
• Frequent Patterns TODO
  • Is there an association rule between questions? Ex) answering c on question B means students answer a on question 2 with very high confidence.

Student Analysis

• Student Clusters TODO
  • Student's who have similar understandings will likely have similar answers on the test. We can try to automatically group these students.
• Visualization TODO
  • Similarities among students can be visualized in a 2D Plot via a class of methods known as manifold embedding and dimensionality reduction

Wholistic Analysis

• Co-Clustering TODO

Usage (For Developers)

todo = 'in progress'

Usage (For Teachers)

TODO