Personalized feedback for open-response mathematical questions using long short-term memory networks

Joshua J. Michalenko, Andrew S. Lan, Richard G. Baraniuk

Research output: Contribution to conferencePaper

Abstract

In this paper, we explore the problem of automatic grading and feedback generation for open-response mathematical questions. We resort to the long short-term memory (LSTM) network to learn the simple task of polynomial factorization and use the trained network for grading and feedback. We use Wolfram Alpha to synthetically generate a training dataset that consists of step-by-step responses to polynomial factorization questions to train the LSTM network. Preliminary results validate the efficacy of LSTMs in learning to factor low-order polynomials; we also demonstrate how to leverage the trained network for automatic grading and personalized feedback generation.

Original languageEnglish (US)
Pages350-351
Number of pages2
StatePublished - Jan 1 2017
Event10th International Conference on Educational Data Mining, EDM 2017 - Wuhan, China
Duration: Jun 25 2017Jun 28 2017

Other

Other10th International Conference on Educational Data Mining, EDM 2017
CountryChina
CityWuhan
Period6/25/176/28/17

Keywords

  • Automatic grading
  • Feedback generation
  • Long short-term memory networks
  • Mathematical expressions

ASJC Scopus subject areas

  • Computer Science Applications
  • Information Systems

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