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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
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
Rights Information:Copyright 2019 Meng Lin
Date Available in IDEALS:2019-08-23
Date Deposited:2019-05

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