Preschoolers’ Understanding of a Teachable Agent-Based Game in Early Mathematics as Reflected in their Gaze Behaviors – an Experimental Study

Abstract

This study investigated how preschool children processed and understood critical information in Magical Garden, a teachable agent-based play-&-learn game targeting early math. We analyzed 36 children's (ages 4-6 years) real-time behavior during game-use to explore whether children: (i) processed the information meant to support number sense development; (ii) showed an understanding of the teachable agent as an entity with agency. An important methodological goal was to go beyond observable behavior and shed some light on how cognitive processing and understanding in children of such young age can be studied. First, the children played Magical Garden for three weeks to get acquainted with the game. Second, in an experimental part of the study, the children's gaze behaviors were measured during 5 rounds of interaction with an experimental version of one of the sub-games. The analyses suggest that two of the gaze behaviors were positively correlated with the game performance measure, as hypothesized. Another result was that children looked at the teachable agent significantly more often when the teachable agent had been in charge of gameplay than when it had not. This can be interpreted as an indication that the children had an understanding of their teachable agent as an entity that, like themselves and unlike other dynamic visual elements in the game, made decisions based on own 'knowledge'. In a broader context, the findings are important in showing the potential gains of combining log data with eye-tracking data for developing and refining AI algorithms for adaptive individual feedback and scaffolding.

Young children’s mathematics learning from same-gender and other-gender intelligent character prototypes.

“…The other-gender characters were not socially meaningful in terms of gender but were socially contingent partners for children through PSIs, which support the body of work on the positive role of social contingency in children's learning from educational digital media (Barr, 2019;Calvert, 2017;Calvert et al, 2020;Kirkorian, 2018;Roseberry et al, 2014). Additionally, these results contribute to research on young children's early math learning from pedagogical agents (Gulz, Londos, et al, 2020) and support findings that peer pedagogical agents can drive learning experiences for children and facilitate the processing of on-screen content when they engage in more PSIs.…”

“…When children do not know, or do not share salient similarities with the character, like gender, it may be particularly important for characters to provide ample opportunities for a conversational back-and-forth interaction about STEM content, which has been found to support learning from peer agents (Kim & Baylor, 2006). Media characters may be effective and motivating for young children to learn math from pedagogical agents (Gulz, Kjällander, et al, 2020;Gulz, Londos, et al, 2020;Husain et al, 2015).…”

“…Previous research finds that for preschool children, age predicts children's average latency, or efficiency, on the add-1 rule which reflects that this is a developmental time frame when they are becoming fluent with the add-1 rule (Calvert et al, 2020). Media interventions have been one route to developing add-1 fluency (Baroody et al, 2013;Calvert et al, 2020) as well as other early math skills (Gulz, Londos, et al, 2020). Media interventions designed to include elements that are grounded in early math research and include meaningful feedback and scaffolded support as well as a focus on motivational aspects relevant to the target age group (e.g., media characters) may offer preschoolers effective remote learning opportunities (Husain et al, 2015).…”

Mechanisms of social evaluation in infancy: A preregistered exploration of infants’ eye‐movement and pupillary responses to prosocial and antisocial events

“…The final sample had an average tracking ratio of 47.17% with an SD of 11.97%. Considering the young age of our participants and the long durations of each of our videos (21.8 s) and picture stimuli (30 s), we considered a tracking ratio of 47.17% to be satisfactory (see, for comparison, Gulz et al, 2020, in which preschoolers had an average tracking ratio of 50.4%, and LoBue et al, 2017, who used a tracking ratio cut‐off of 15% with 4‐ to 24‐month‐olds; average tracking ratio was not reported). We also note that the main findings of the study remained largely unchanged when infants with tracking ratios above and below the median are analyzed separately (see Supplemental Material 5) and that no correlations were found between tracking ratio and any main measures of the study.…”

Robotic Chinese language tutor: personalising progress assessment and feedback or taking over your job?

“…The recent two years of educational AI lies on mobile platform, game-based learning or online analytics system on a computer machine, VR/AR or mixed reality (IJAIED, 2020). For examples, both the research of smartphone Python tutor (Fabic, Mitrovic and Neshatian, 2019) and Teachable Agent-based game with eye-tracking capabilities (Gulz, Londos, and Haake, 2020) showed that the AI are effective in enhancing learning and teaching. Limited literatures, nevertheless, are focused in educational humanoid robotics.…”