Trajectories of scientific reasoning: A microgenetic study on children's inquiry functioning

Child-Robot Collaborative Problem-Solving and the Importance of Child's Voluntary Interaction: A Developmental Perspective

The emergence and development of cognitive strategies for the transition from exploratory actions towards intentional problem-solving in children is a key question for the understanding of the development of human cognition. Researchers in developmental psychology have studied cognitive strategies and have highlighted the catalytic role of the social environment. However, it is not yet adequately understood how this capacity emerges and develops in biological systems when they perform a problem-solving task in collaboration with a robotic social agent. This paper presents an empirical study in a human-robot interaction (HRI) setting which investigates children's problem-solving from a developmental perspective. In order to theoretically conceptualize children's developmental process of problem-solving in HRI context, we use principles based on the intuitive theory and we take into consideration existing research on executive functions with a focus on inhibitory control. We considered the paradigm of the Tower of Hanoi and we conducted an HRI behavioral experiment to evaluate task performance. We designed two types of robot interventions, "voluntary" and "turn-taking"-manipulating exclusively the timing of the intervention. Our results indicate that the children who participated in the voluntary interaction setting showed a better performance in the problem solving activity during the evaluation session despite their large variability in the frequency of self-initiated interactions with the robot. Additionally, we present a detailed description of the problem-solving trajectory for a representative single case-study, which reveals specific developmental patterns in the context of the specific task. Implications and future work are discussed regarding the development of intelligent robotic systems that allow child-initiated interaction as well as targeted and not constant robot interventions.

The Link between Microdevelopment and Long-Term Learning Trajectories in Science Learning

Long-term learning trajectories evolve through microdevelopmental sequences (i.e., short-term processes of change during learning tasks) and depend on variability during and across learning tasks. The aim of this study is to examine the coupling between short-term teacher-student dynamics and students' long-term learning trajectories, thereby providing empirical support for the link between the short- and long-term time scale in cognitive development. For 31 students (ages 3-5 years) from regular and special education, five teacher-student interactions during science tasks were filmed and coded in real time with regard to the student's level of understanding and the teacher's support throughout the task. A hierarchical cluster analysis resulted in four different learning trajectories over the course of 1.5 years, labeled as a high-scoring, mid-scoring, fluctuating, and low-scoring group of students. When connecting these trajectories to microdevelopmental data, the interactions of the high-scoring students were characterized by more moment-to-moment variations in the teacher's support and student's level of understanding, while the low-scoring group had the least variability compared to the other groups. Students with emotional and behavioral disabilities were represented across all learning trajectories, despite frequent accounts in the literature on their significant academic delays.