To elicit or to tell: Does it matter

Min Chi, Pamela Jordan, Kurt VanLehn, Diane Litman

Research output: Chapter in Book/Report/Conference proceedingConference contribution

12 Scopus citations

Abstract

While high interactivity has been one of the main characteristics of one-on-one human tutoring, a great deal of controversy surrounds the issue of whether interactivity is indeed the key feature of tutorial dialogue that impacts students' learning results. There are two commonly held hypotheses regarding the issue: a widely-believed monotonic interactivity hypothesis and a better supported interaction plateau hypothesis. The former hypothesis predicts increasing in interactivity causes an increase in learning while the latter states that increasing interactivity yields increasing learning until it hits a plateau, and further increases in interactivity do not cause noticeably increase in learning. In this study, we proposed the tactical interaction hypothesis which predicts beyond a certain level of interactivity, further increases in interactivity do not cause increase in learning unless they are guided by effective tutorial tactics. Overall our results support this hypothesis. However, finding effective tactics is not easy. This paper sheds some light on how to apply Reinforcement Learning to derive effective tutorial tactics.

Original languageEnglish (US)
Title of host publicationFrontiers in Artificial Intelligence and Applications
PublisherIOS Press
Pages197-204
Number of pages8
Edition1
ISBN (Print)9781607500285
DOIs
StatePublished - Jan 1 2009

Publication series

NameFrontiers in Artificial Intelligence and Applications
Number1
Volume200
ISSN (Print)0922-6389

Keywords

  • Intelligent tutoring systems
  • Knowledge component
  • Natural language tutoring systems
  • Pedagogical tutorial tactics
  • Reinforcement learning

ASJC Scopus subject areas

  • Artificial Intelligence

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  • Cite this

    Chi, M., Jordan, P., VanLehn, K., & Litman, D. (2009). To elicit or to tell: Does it matter. In Frontiers in Artificial Intelligence and Applications (1 ed., pp. 197-204). (Frontiers in Artificial Intelligence and Applications; Vol. 200, No. 1). IOS Press. https://doi.org/10.3233/978-1-60750-028-5-197