A Method for Promoting Vicarious Learning in an Online Conversation-Based Intelligent Tutoring System
Keith Shubeck and Xiangen Hu
Conversation-based intelligent tutoring systems (ITSs), like
AutoTutor, are highly effective at promoting learning across a
wide variety of domains. However, not all students benefit
equally from these ITSs. Some evidence suggests vicarious
learning and learning alongside a peer agent (i.e., trialogues) are
most appropriate for learners with lower levels of domain
knowledge. Other studies suggest that high domain knowledge
learners benefit more by teaching other student agents. There is
a need for careful observations of interactions between learner
aptitude and various conversation frameworks. In an ongoing
online study, participants are assigned to either an interactive
condition where they hold a conversation with a tutor agent, or a
yoked-vicarious condition where participants observe the
interaction. The results of this experiment will shed light on the
varying effectiveness of vicarious learning on students with
different levels of domain knowledge.