Learner characteristics and dialogue: recognising effective and student-adaptive tutorial strategies
by Christopher M. Mitchell; Eun Young Ha; Kristy Elizabeth Boyer; James C. Lester
International Journal of Learning Technology (IJLT), Vol. 8, No. 4, 2013

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.

Online publication date: Mon, 31-Mar-2014

The full text of this article is only available to individual subscribers or to users at subscribing institutions.

 
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.

Pay per view:
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.

Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Learning Technology (IJLT):
Login with your Inderscience username and password:

    Username:        Password:         

Forgotten your password?


Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.

If you still need assistance, please email subs@inderscience.com