{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/105239"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/105239","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"An end-to-end grading neural network for middle-school math problems","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.","abstract_html":"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. 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