OpenTutor: Designing a Rapid-Authored Tutor that Learns as you Grade

Authors

  • Benjamin D Nye University of Southern California
  • Rushit Sanghrajka University of Utah
  • Vinit Bodhwani University of Southern California
  • Martin Acob California State University, Sacramento
  • Daniel Budziwojski University of Southern California
  • Kayla Carr University of Southern California
  • Larry Kirshner University of Southern California
  • William R Swartout University of Southern California

DOI:

https://doi.org/10.32473/flairs.v34i1.128576

Keywords:

Intelligent Tutoring Systems, Natural Language Processing, Authoring Tools, Online Learning, Usability

Abstract

Despite strong evidence that dialog-based intelligent tutoring systems (ITS) can increase learning gains, few courses include these tutors. In this research, we posit that existing dialog-based tutoring systems are not widely used because they are too complex and unfamiliar for a typical teacher to adapt or augment. OpenTutor is an open-source research project intended to scale up dialog-based tutoring by enabling ordinary teachers to rapidly author and improve dialog-based ITS, where authoring is presented through familiar tasks such as assessment item creation and grading. Formative usability results from a set of five non-CS educators are presented, which indicate that the OpenTutor system was relatively easy to use but that teachers would closely consider the cost benefit for time vs. student outcomes. Specifically, while OpenTutor grading was faster than expected, teachers reported that they would only spend any additional time (compared to a multiple choice) if the content required deeper learning. To decrease time to train answer classifiers, OpenTutor is investigating ways to reduce cold-start problems for tutoring dialogs.

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Published

2021-04-18

How to Cite

Nye, B. D., Sanghrajka, R., Bodhwani, V., Acob, M., Budziwojski, D., Carr, K., Kirshner, L., & Swartout, W. R. (2021). OpenTutor: Designing a Rapid-Authored Tutor that Learns as you Grade. The International FLAIRS Conference Proceedings, 34. https://doi.org/10.32473/flairs.v34i1.128576

Issue

Section

Special Track: Intelligent Learning Technologies