Abstract
To investigate how a robot's use of feedback can influence children's engagement and support second language learning, we conducted an experiment in which 72 children of 5 years old learned 18 English animal names from a humanoid robot tutor in three different sessions. During each session, children played 24 rounds in an “I spy with my little eye” game with the robot, and in each session the robot provided them with a different type of feedback. These feedback types were based on a questionnaire study that we conducted with student teachers and the outcome of this questionnaire was translated to three within-design conditions: (teacher) preferred feedback, (teacher) dispreferred feedback and no feedback. During the preferred feedback session, among others, the robot varied his feedback and gave children the opportunity to try again (e.g., “Well done! You clicked on the horse.”, “Too bad, you pressed the bird. Try again. Please click on the horse.”); during the dispreferred feedback the robot did not vary the feedback (“Well done!”, “Too bad.”) and children did not receive an extra attempt to try again; and during no feedback the robot did not comment on the children's performances at all. We measured the children's engagement with the task and with the robot as well as their learning gain, as a function of condition. Results show that children tended to be more engaged with the robot and task when the robot used preferred feedback than in the two other conditions. However, preferred or dispreferred feedback did not have an influence on learning gain. Children learned on average the same number of words in all conditions. These findings are especially interesting for long-term interactions where engagement of children often drops. Moreover, feedback can become more important for learning when children need to rely more on feedback, for example, when words or language constructions are more complex than in our experiment. The experiment's method, measurements and main hypotheses were preregistered.
Highlights
A recent trend in education is to have social robots take on the role of educational tutors to support, for example, second language learning (Westlund and Breazeal, 2015; Belpaeme et al, 2018; Vogt et al, 2019)
This paper describes a study that investigated how preschool children respond to different types of feedback provided by a robot tutor
Most child-robot studies use praise as a motivator in their experiments and are hesitant to use explicit negative feedback. It is not clear what type of negative feedback works best for robots, in educational studies it seems that mentioning the children’s mistake seems to be more effective for language learning. We address this gap in knowledge by investigating the effect of different forms of feedback on both task, robot-engagement and learning gain
Summary
A recent trend in education is to have social robots take on the role of educational tutors to support, for example, second language learning (Westlund and Breazeal, 2015; Belpaeme et al, 2018; Vogt et al, 2019). In educational settings, learning a (second) language typically involves social interactions between the child and the teacher During these interactions, children constantly receive feedback about their performance. Feedback is used to correct children, but for example by teachers to contribute positively to children’s own feeling of competence and success and encourage children to continue with a task (Blumenfeld et al, 2006; Hattie and Timperley, 2007). Hattie and Timperley (2007) propose a combination of these three types as good way of providing feedback. Using explicit negative feedback (i.e., stating explicitly that something is wrong) seems to be most beneficial for children who are struggling with a task, such as novel learners (Kluger and DeNisi, 1996; Shute, 2008)
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