Classification of Children's Handwriting Errors for the Design of an Educational Co-writer Robotic Peer

Abstract

In this paper, we propose a taxonomy of handwriting errors exhibited by children as a way to build adequate strategies for integration with a co-writing peer. The exploration includes the collection of letters written by children in an initial study, which were then revised in a second study. The second study also analyses the "peer-learning" (PL) and "peertutoring" (PT) learning methods in an educational scenario, where a pair of children perform a collaborative writing activity in the presence of a robot facilitator. The data obtained in the first two studies allowed us to create a "taxonomy of handwriting errors". A set of writing errors were selected and implemented in an educational activity for validation. This activity constituted a third study, wherein we systematically induced the errors into a Nao robot's handwriting using the PT method -A teacher-child corrects the handwriting errors of the learner-robot. The preliminary results suggest that the children in general showed awareness to the writing errors and were able to perceive the writing abilities of the robot.

Social/dialogical roles of social robots in supporting children’s learning of language and literacy—A review and analysis of innovative roles

“…In another study known as CoWriter ( Hood et al, 2015 ; Chandra et al, 2017 ), a NAO robot with poor handwriting skills was used in a “learning-by-teaching” interaction with children to stimulate their metacognition, empathy, and self-esteem in addition to their handwriting skills. The interaction consisted of the following sequence: The child first selects the letter to help NAO practice on.…”

“…What seems particularly difficult is to concurrently and consistently ensure that 1) the robot’s incorrect behavior triggers the desired effects in terms of engagement and metacognition without generating frustration in the human tutor ( Biswas et al, 2005 ), 2) the robot improves over time, thus incorporating the tutor’s scaffolding behavior and making it interactive ( Chi, 2009 ) while 3) never surpassing the tutor’s own competence, which would negatively impact the tutor’s self-esteem. Tanaka and Matsuzoe (2012) circumvented this problem by remotely tele-operating the robot with a Wizard-of-Oz approach, whereas Hood et al (2015) and Chandra et al (2017) relied on a dataset of adult handwriting samples to define shape deformations to apply to the letters’ models, thereby ensuring the robot’s “bad handwriting.” Upon merging the robot’s own poor letter with the example provided by the child, errors are either mitigated (if they do not appear in the example) or reinforced: This enables the child to see their own mistakes in the robot’s handwriting and reflect on them. Exporting such a sophisticated interaction to more complex contexts, possibly involving social and verbal interaction, is an open research challenge.…”

Cognitive Learning and Robotics: Innovative Teaching for Inclusivity

“…While Jacq et al ( 2016) [65] and Le Denmat et al (2018) [69] provide little or no evidence for the enactment of cognitive abilities in a human-robot learning scenario, Chandra, Dillenbourg and Paiva (2017) [70] expanded on that matter by explaining that complex sensorimotor and cognition skills take part in mastering handwriting. Moreover, once these cognitive skills and handwriting are acquired, they are unlikely to respond to further changes; therefore, much effort should be channeled toward developing the sensorimotor and cognition skills while teaching handwriting [70].…”

An adaptive robot teacher boosts a human partner’s learning performance in joint action

“…In the context of humanrobot interaction, Vollmer et al [15] found that human participants produce motionese in demonstrations directed towards a robot learner, and Nagai and Rohlfing [18] showed that a robot observer could be designed to pick up on, and extract information from, motionese produced by a human. Moreover, Chandra et al [16] showed that the interaction with an adaptive robot enabled children not only to teach but also to improve their own learning of letters.…”