A Robot-Partner for Preschool Children Learning English Using Sociocognitive Conflict

By Mazzoni, Elvis; Benvenuti, Martina | Educational Technology & Society, October 2015 | Go to article overview

A Robot-Partner for Preschool Children Learning English Using Sociocognitive Conflict


Mazzoni, Elvis, Benvenuti, Martina, Educational Technology & Society


Introduction

This research project deals with a robot (MecWilly) designed to help preschool children (4-6 years) in their learning of a second language (English). The main innovation is the experimental setting chosen in order to obtain an improvement in the children's English language skills. In previous studies of educational contexts, robots have typically been used as "teachers" to "structure" the learning process (Fridin, 2014). This also means that children viewed the robot-teacher as "other" (in the case of humanoid robots) or as an "artifact" like a computer or a book (in the case of robots with non-human features).

The link between Robotics and Psychology has presented a very interesting challenge for many scholars over the last fifty years (Kelley & Cassenti, 2011). Starting from the development of the first types of computer (the first Turingcomplete machine, ENIAC, created by John Von Neumann in the middle of the 20th century), the simulation of human mental processes, and, later, of human decisions and actions, has lead robotics to become one of the most exciting fields for the evolution of human behavioral models. Throughout this process, many of the most important models and theories of Psychology have been examined, in particular Piaget's Constructivism and the related processes of assimilation and accommodation, Skinner's operant conditioning, the Vygotskian Zone of Proximal Development, Cognitivism's Human-Information Processing (HIP) model, as well as the latest models of artificial intelligence, Connectionism and Neural Networks (Dautenhahn & Billard, 1999a; 1999b; Ziemke, 2001). Even though all these models have been looked at as part of the broad field of Human-Robot Interactions (HRI) or in Robot Development/Evolution, each model has also been analyzed in more specific and well-defined scopes of application. We can briefly summarize five areas of application for psychological models in robotics:

(a) The evolution of robot prototypes which simulate human mental and physical behavior;

(b) Robot-robot interaction in complex environments for simulating human social behavior;

(c) Human[right arrow]robot and robots[right arrow]robot interaction in which the human or one of the robots acts as the tutor|teacher|expert and the (other) robot is a learner;

(d) Human[left arrow]robot interaction in which the robot acts as a mechanical "partner" which allows the human to try out (and/or train) certain abilities or social skills so that he/she may make progress;

(e) And finally a situation in which the human-robot interaction is characterized by a human who acts as a "tutor|teacher" for the robot and a robot acting as a counterpart in the learning process which, in the end, sees the convergence of improvement and knowledge acquisition influencing the human's cognitive development.

The first two areas have been principally influenced by Skinner's Operant Conditioning, Cognitivism, and more recently by Artificial Intelligence (AI), Connectionism and neural networks (including their recent evolutions). Behaviorism played an important role in the development of early robots, as this psychological model has simple rules which can be easily fed into a machine. Thanks to these characteristics, Skinner's teaching machines were very successful in learning environments and demonstrated the importance of feedback (reinforcement) and also of coherence and repeated reinforcement over time in maximizing the learning process. Applying behavioral rules to robotics (and informatics), i.e., determining the behavior of a machine on the basis of the effects of a particular type of behavior and its related reinforcement, helped to produce a set of "mechanical" machines that were able to react to their environment by simulating certain types of human behavior. Robots capable of more sophisticated actions (and quasi-decisions) have been developed using behavioral models derived from AI connected to Behaviorism, the socalled Behavior-Based Robotics (Arkin, 1998; Brooks, 1986a). …

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