Content, Social, and Metacognitive Statements: An Empirical Study Comparing Human-Human and Human-Computer Tutorial Dialogue

We present a study which compares human-human computer-mediated tutoring with two computer tutoring systems based on the same materials but differing in the type of feedback they provide. Our results show that there are significant differences in interaction style between human-human and human-computer tutoring, as well as between the two computer tutors, and that different dialogue characteristics predict learning gain in different conditions. We show that there are significant differences in the non-content statements that students make to human and computer tutors, but also to different types of computer tutors. These differences also affect which factors are correlated with learning gain and user satisfaction. We argue that ITS designers should pay particular attention to strategies for dealing with negative social and metacognitive statements, and also conduct further research on how interaction style affects human-computer tutoring.

[1]  B. Bloom The 2 Sigma Problem: The Search for Methods of Group Instruction as Effective as One-to-One Tutoring , 1984 .

[2]  S. Messick Assessment in Context: Appraising Student Performance in Relation to Instructional Quality , 1984 .

[3]  Albert T. Corbett,et al.  Intelligent Tutoring Systems , 1985, Science.

[4]  Michelene T. H. Chi,et al.  Eliciting Self-Explanations Improves Understanding , 1994, Cogn. Sci..

[5]  Ramzan Khuwaja,et al.  Architecture of CIRCSIM-Tutor (v.3): a smart cardiovascular physiology tutor , 1994, Proceedings of IEEE Symposium on Computer-Based Medical Systems (CBMS).

[6]  Clifford Nass,et al.  The media equation - how people treat computers, television, and new media like real people and places , 1996 .

[7]  Arthur C. Graesser,et al.  AutoTutor: A simulation of a human tutor , 1999, Cognitive Systems Research.

[8]  Ann L. Brown,et al.  How people learn: Brain, mind, experience, and school. , 1999 .

[9]  Vincent Aleven,et al.  Towards Tutorial Dialog to Support Self- Explanation: Adding Natural Language Understanding to a Cognitive Tutor * , 2001 .

[10]  Chris Barker,et al.  An Experiment on Public Speaking Anxiety in Response to Three Different Types of Virtual Audience , 2002, Presence: Teleoperators & Virtual Environments.

[11]  Vincent Aleven,et al.  Pilot-Testing a Tutorial Dialogue System That Supports Self-Explanation , 2002, Intelligent Tutoring Systems.

[12]  Kurt VanLehn,et al.  Interactive Conceptual Tutoring in Atlas-Andes , 2002 .

[13]  Nicole Shechtman,et al.  Media inequality in conversation: how people behave differently when interacting with computers and people , 2003, CHI '03.

[14]  Larry F. Hodges,et al.  Effects of Virtual Human Presence on Task Performance , 2004 .

[15]  Pamela W. Jordan Using Student Explanations as Models for Adapting Tutorial Dialogue , 2004, FLAIRS Conference.

[16]  Brady Clark,et al.  Advantages of Spoken Language Interaction in Dialogue-Based Intelligent Tutoring Systems , 2004, Intelligent Tutoring Systems.

[17]  Carolyn Penstein Rosé,et al.  Interactivity and Expectation: Eliciting Learning Oriented Behavior with Tutorial Dialogue Systems , 2005, INTERACT.

[18]  Erica Melis,et al.  Interactivity of Exercises in ActiveMath , 2005, ICCE.

[19]  Carolyn Penstein Rosé,et al.  Spoken Versus Typed Human and Computer Dialogue Tutoring , 2006, Int. J. Artif. Intell. Educ..

[20]  Claus Zinn,et al.  Using dialogue to learn math in the LeActiveMath project , 2006 .

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

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

[23]  Diane J. Litman,et al.  Content-Learning Correlations in Spoken Tutoring Dialogs at Word, Turn, and Discourse Levels , 2008, FLAIRS Conference.

[24]  Johanna D. Moore,et al.  Diagnosing Natural Language Answers to Support Adaptive Tutoring , 2008, FLAIRS Conference.

[25]  Rodney D. Nielsen,et al.  Learning to Assess Low-Level Conceptual Understanding , 2008, FLAIRS Conference.

[26]  Arthur C. Graesser,et al.  What Students Expect May Have More Impact Than What They Know or Feel , 2009, AIED.

[27]  Charles B. Callaway,et al.  Metacognitive Awareness versus Linguistic Politeness: Expressions of Confusion in Tutorial Dialogues , 2009 .

[28]  Ioannis Stamelos,et al.  The Impact of Prompting in Technology-Enhanced Learning as Moderated by Students' Motivation and Metacognitive Skills , 2009, EC-TEL.

[29]  Ulrike Cress,et al.  Learning in the Synergy of Multiple Disciplines, 4th European Conference on Technology Enhanced Learning, EC-TEL 2009, Nice, France, September 29 - October 2, 2009, Proceedings , 2009, EC-TEL.

[30]  Johanna D. Moore,et al.  Using Natural Language Processing to Analyze Tutorial Dialogue Corpora Across Domains Modalities , 2009, AIED.

[31]  Johanna D. Moore,et al.  Dealing with Interpretation Errors in Tutorial Dialogue , 2009, SIGDIAL Conference.

[32]  Diane J. Litman,et al.  Adapting to Student Uncertainty Improves Tutoring Dialogues , 2009, AIED.

[33]  Johanna D. Moore,et al.  The Impact of Interpretation Problems on Tutorial Dialogue , 2010, ACL.

[34]  Johanna D. Moore,et al.  Comparing Human-Human to Human-Computer Tutorial Dialogue , 2010 .