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

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

Title: An end-to-end grading neural network for middle-school math problems
Author(s): Lin, Meng
Issue Date: 2019-04-22
Director of Research (if dissertation) or Advisor (if thesis): Jiang, Nan
Committee Member(s): Hajek, Bruce
Department of Study: Electrical & Computer Eng
Discipline: Electrical & Computer Engr
Degree Granting Institution: University of Illinois at Urbana-Champaign
Degree Name: M.S.
Degree Level: Thesis
Keyword(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.
Graduation Semester: 2019-05
Type of Resource: text
Permalink: http://hdl.handle.net/2142/105239