To Elicit Or To Tell: Does It Matter?

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.

[1]  Kurt VanLehn,et al.  Eliminating the Gap between the High and Low Students through Meta-cognitive Strategy Instruction , 2008, Intelligent Tutoring Systems.

[2]  Takashi Yamauchi,et al.  Learning from human tutoring , 2001, Cogn. Sci..

[3]  Arthur C. Graesser,et al.  When Are Tutorial Dialogues More Effective Than Reading? , 2007, Cogn. Sci..

[4]  Claus Zinn,et al.  Generating Tutorial Feedback with Affect , 2004, FLAIRS.

[5]  Kurt VanLehn,et al.  Do Micro-Level Tutorial Decisions Matter: Applying Reinforcement Learning to Induce Pedagogical Tutorial Tactics , 2010, Intelligent Tutoring Systems.

[6]  Joel R. Tetreault,et al.  A Reinforcement Learning approach to evaluating state representations in spoken dialogue systems , 2008, Speech Commun..

[7]  Richard S. Sutton,et al.  Reinforcement Learning: An Introduction , 1998, IEEE Trans. Neural Networks.

[8]  Marilyn A. Walker,et al.  Reinforcement Learning for Spoken Dialogue Systems , 1999, NIPS.

[9]  Richard S. Sutton,et al.  Introduction to Reinforcement Learning , 1998 .

[10]  Sandra Katz,et al.  Going Beyond the Problem Given: How Human Tutors Use Post-Solution Discussions to Support Transfer , 2003, Int. J. Artif. Intell. Educ..

[11]  Kurt VanLehn,et al.  Developing pedagogically effective tutorial dialogue tactics: experiments and a testbed , 2007, SLaTE.

[12]  M. Chi,et al.  Can Tutors Monitor Students' Understanding Accurately? , 2004 .

[13]  Kenneth R. Koedinger,et al.  Learning Factors Analysis - A General Method for Cognitive Model Evaluation and Improvement , 2006, Intelligent Tutoring Systems.