Files in this item

FilesDescriptionFormat

application/pdf

application/pdfLIN-THESIS-2019.pdf (418kB)Restricted Access
(no description provided)PDF

Description

Title:An end-to-end grading neural network for middle-school math problems
Author(s):Lin, Meng
Advisor(s):Jiang, Nan
Contributor(s):Hajek, Bruce
Department / Program:Electrical & Computer Eng
Discipline:Electrical & Computer Engr
Degree Granting Institution:University of Illinois at Urbana-Champaign
Degree:M.S.
Genre:Thesis
Subject(s):grading deeplearning
Abstract: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.
Issue Date:2019-04-22
Type:Text
URI:http://hdl.handle.net/2142/105239
Rights Information:Copyright 2019 Meng Lin
Date Available in IDEALS:2019-08-23
Date Deposited:2019-05


This item appears in the following Collection(s)

Item Statistics