When deictic gestures in a robot can harm child-robot collaboration

This paper describes research aimed at supporting children's reading practices using a robot designed to interact with children as their reading companion. We use a learning by teaching scenario in which the robot has a similar or lower reading level compared to children, and needs help and extra practice to develop its reading skills. The interaction is structured with robot reading to the child and sometimes making mistakes as the robot is considered to be in the learning phase. Child corrects the robot by giving it instant feedbacks. To understand what kind of behavior can be more constructive to the interaction especially in helping the child, we evaluated the effect of a deictic gesture, namely pointing on the child's ability to find reading mistakes made by the robot. We designed three types of mistakes corresponding to different levels of reading mastery. We tested our system in a within-subject experiment with 16 children. We split children into a high and low reading proficiency even-though they were all beginners. For the high reading proficiency group, we observed that pointing gestures were beneficial for recognizing some types of mistakes that the robot made. For the earlier stage group of readers pointing were helping to find mistakes that were raised upon a mismatch between text and illustrations. However, surprisingly, for this same group of children, the deictic gestures were disturbing in recognizing mismatches between text and meaning

From moves to sequences: expanding the unit of analysis in the study of classroom discourse

What is the appropriate unit of analysis for the study of classroom discourse? One common analytic strategy employs individual discourse moves, which are coded, counted and used as indicators of the quality of classroom talk. In this article we question this practice, arguing that discourse moves are positioned within sequences that critically shape their meaning and effect. We illustrate this theoretical claim through exploration of a corpus of over 7000 discourse moves in primary literacy lessons. First, we use conventional measures such as the proportion of open and closed questions, and show how these indicators can be misleading when abstracted from the sequences in which they are embedded. We propose a complementary method, lag sequential analysis, which examines how discourse is sequentially structured-i.e. which discourse moves are followed by which other moves, and which chains of moves occur more frequently than expected by chance. We illustrate this method through re-analysis of our corpus of literacy lessons, examining differences between the sequential patterns found in the different classrooms observed. While lag sequential analysis does not resolve all problems inherent in systematic observation of classroom discourse, it does shed light on critical patterns in the data-set that would have otherwise gone unnoticed