Learner characteristics and dialogue: recognising effective and student-adaptive tutorial strategies

  • Authors:

  • Christopher M. Mitchell;Eun Young Ha;Kristy Elizabeth Boyer;James C. Lester

  • Affiliations:

  • Department of Computer Science, North Carolina State University, 890 Oval Dr., Campus Box 8206, Raleigh, NC 27695, USA;Department of Computer Science, North Carolina State University, 890 Oval Dr., Campus Box 8206, Raleigh, NC 27695, USA;Department of Computer Science, North Carolina State University, 890 Oval Dr., Campus Box 8206, Raleigh, NC 27695, USA;Department of Computer Science, North Carolina State University, 890 Oval Dr., Campus Box 8206, Raleigh, NC 27695, USA

  • Venue:
  • International Journal of Learning Technology
  • Year:
  • 2013

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Abstract

In recent years, there have been significant advances in tutoring systems that engage students in rich natural language dialogue. With the goal of further understanding what makes tutorial dialogue successful, this article presents a corpus-based approach to modelling the differential effectiveness of tutorial dialogue strategies with respect to learning. We present results of a study in which task-oriented, textual tutorial dialogue was collected from remote one-on-one human tutoring sessions. This article extends a previous study which found that certain dialogue acts were correlated with learning and student characteristics in the corpus. The predictive models presented here demonstrate important differences between the dialogue sequences that were correlated with learning for different groups of students. The models demonstrate that tutor directives, a type of bottom-out hint, were negatively associated with learning for students with low incoming knowledge or low self-efficacy. The findings signal the importance of tutorial dialogue that adapts to learner characteristics.