An end-to-end grading neural network for middle-school math problems

Mathematics homework grading is a common real-world task where a human grader checks a student's solution to a math problem against the answer key and gives a score. This thesis proposes a deep-learning-powered grader that takes the place of the human grader. The task is formulated as a classification problem. Given an answer key and a student's solution, the classifier needs to predict two metrics: (1) a four-class classification result that measures the completeness of the student's detailed steps and (2) a binary classification result that identifies whether the conclusion of the student's solution is accurate. A new model, Step Comparison Transformer (SCT), is introduced, and its performance is validated on a set of grading data provided by a commercial provider of artificial intelligence products for education.

Name thesis:degree_name

M.S.

Level thesis:degree_level

Thesis

Discipline thesis:degree_discipline

Electrical & Computer Engr

Grantor

University of Illinois at Urbana-Champaign

Year dc:date

2019

Author dc:creator

• Lin, Meng

Contributors dc:contributor

• Jiang, Nan
• Hajek, Bruce

• grading deeplearning

Statement dc:rights

• Copyright 2019 Meng Lin

Language dc:language

en

Handle dc:identifier

http://hdl.handle.net/2142/105239

OAI identifier oai:identifier

oai:www.ideals.illinois.edu:2142/105239