Learning by Teaching with Humanoid Robot: A New Powerful Experimental Tool to Improve Children’s Learning Ability

Abstract

Browsing the literature shows that an increasing number of authors choose to use the learning by teaching approach in the field of educational robotics. The goal of this paper is, on the one hand, to produce a review of articles describing the effects of this approach on learning and, on the other hand, to review the literature in order to explore the characteristics at the core of this approach. We will only focus on the work using a humanoid robot. The areas of learning studied are writing, reading, vocabulary, and reasoning, but also there are some metacognitive abilities like task commitment and mental state attribution. Their targets are from very young children to preadolescents. We can already notice some studies on pupils with special educational needs. In all of these domains, the results show a nonnegligible effect of learning by teaching both on learning and on metacognitive abilities. If the concept of learning by teaching is clear, a careful investigation of the different studies shows that experimental paradigms do not use the same basic characteristics. For some, it is the robot’s weakness, the care that must be given to it, which is the main requirement for the approach, while for others it is the unbalanced distribution of knowledge which is at the heart of it. The learning by teaching approach we will study has two components: the robot and the child tutor. The characteristics of the robot and what is asked of the child to accomplish his or her task of the tutor will be analyzed.

A Comparison of Social Robot to Tablet and Teacher in a New Script Learning Context

“…It has a clear target to assist children to learn handwriting with a social robot on the basis of learning by using a teaching approach (LbT) (Hood et al, 2015;Jacq et al, 2016;Lemaignan et al, 2016). Since the development of these studies, others have effectively employed the robot-assisted LbT approach to other fields of inquiry (Jamet et al, 2018;Yadollahi et al, 2018).…”

Pragmatics in the False-Belief Task: Let the Robot Ask the Question!

“…The effectiveness of our "mentor-child" context 11 was tested with children between 5 and 6 in the class inclusion task and successfully made the question of class inclusion more relevant for the child when it was asked (Masson et al, 2017a;Jamet et al, 2018). We hypothesize that the "mentor-child" context should similarly decrease the ambiguity of the ToM question to make it clearer that it is a request about the mental states of the character Maxi.…”

“…Politzer observed that when he disambiguated the question of class inclusion the success of participants was significantly improved and came earlier: between 5 and 6 years-old (see also Jamet et al, 2018).…”

“…There is an important literature showing the advantages of using a humanoid NAO robot in social interactions with young children, especially in situations of learning by teaching (see Jamet et al, 2018). Studies have shown that in conversational interaction with an artificial agent, even completely virtual ones, humans automatically detect pragmatic violations of their speaker (Jacquet et al, 2018;Jacquet et al, 2019a,b,c;Lockshin and Williams, 2020).…”

Cooperation in Online Conversations: The Response Times as a Window Into the Cognition of Language Processing

“…does slightly better in some cases, as for example, if it asks about the number of children, siblings or pets, and if then the user answers “I have two,” the chatterbot will answer “What are their names?” which is an expected question, but will say the same thing if the topic is about computers instead of children (clearly indicating that this is a generic and pre-programmed reply). The lack of pragmatic processing is indeed extremely common in artificial social agents in general (Jacquet et al, 2019) despite experimental data showing its importance, including with social robots (Masson et al, 2017b; Jamet et al, 2018).…”