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1. A Model-free Affective Reinforcement Learning Approach to Personalization of an Autonomous Social Robot Companion for Early Literacy

   Park, H.W.; Grover, I.; Spaulding, S.; Gomez, L.; Breazeal, C. (January 2019, Proceedings of the ... AAAI Conference on Artificial Intelligence)

   Personalized education technologies capable of delivering adaptive interventions could play an important role in addressing the needs of diverse young learners at a critical time of school readiness. We present an innovative personalized social robot learning companion system that utilizes children’s verbal and nonverbal affective cues to modulate their engagement and maximize their long-term learning gains. We propose an affective reinforcement learning approach to train a personalized policy for each student during an educational activity where a child and a robot tell stories to each other. Using the personalized policy, the robot selects stories that are optimized for each child’s engagement and linguistic skill progression. We recruited 67 bilingual and English language learners between the ages of 4–6 years old to participate in a between-subjects study to evaluate our system. Over a three-month deployment in schools, a unique storytelling policy was trained to deliver a personalized story curriculum for each child in the Personalized group. We compared their engagement and learning outcomes to a Non-personalized group with a fixed curriculum robot, and a baseline group that had no robot intervention. In the Personalization condition, our results show that the affective policy successfully personalized to each child to boost their engagement and outcomes with respect to learning and retaining more target words as well as using more target syntax structures as compared to children in the other groups. 

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2. Fortitude in frustration, failure: Exploring emotional responses within an at-home elementary engineering program

   Knox, P.; Simpson, A.; Maltese, A. V. (January 2023, American Society for Engineering Education)

   Research on social, emotional, and academic development of children often notes the critical role of parents. Yet, how parents perceive and engage with children’s reactions to difficulty and perceived failure, to then shape their perspective and engagement with learning remains under investigated. The current study explored children and parents’ perceptions of and reactions to frustration and failure within an out-of-school, home-based engineering program. Specifically, we asked 1) How was failure perceived by participating families? and 2) What was the subsequent action/reaction to that failure? Data were derived from post-program interviews with children and parents who participated in a home-based, elementary engineering program involving take-home kits and self-identified engineering projects. Findings derived from descriptive qualitative methods and thematic analysis illustrated development of parent thinking around failure and frustration, both within themselves and their reactions to seeing such emotions in their children. Analysis further revealed how such emotions emerge within their children and impact their experiences. These findings shed light on ways child-parent engagement and the tactics employed by parents may influence a child’s perseverance and willingness to work through difficulty. This research represents an entry point for investigating how parents perceive and react to failures and challenges, and how these reactions shape their communication around failure with their children. Such parental reactions and communication may shape children’s mindset development, perspectives, and engagement. Implications for family engagement and influence on children’s learning through academic emotions in STEM and engineering are discussed. 

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3. Impact of Interaction Context on the Student Affect-Learning Relationship in Child-Robot Interaction

   https://doi.org/10.1145/3319502.3374822  

   Chen, Huili; Park, Hae Won; Zhang, Xiajie; Breazeal, Cynthia (March 2020, ACM/IEEE International Conference on Human-Robot Interaction)

   Prior work in affect-aware educational robots has often relied on a common belief that the relationship between student affect and learning is independent of agent behaviors (child’s/robot’s) or unidirectional (positive/negative but not both) throughout the entire student-robot interaction.We argue that the student affect-learning relationship should be interpreted in two contexts: (1) social learning paradigm and (2) sub-events within child-robot interaction. In our paper, we examine two different social learning paradigms where children interact with a robot that acts either as a tutor or a tutee. Sub-events within child-robot interaction are defined as task-related events occurring in specific phases of an interaction (e.g., when the child/robot gets a wrong answer). We examine subevents at a macro level (entire interaction) and a micro level (within specific sub-events). In this paper, we provide an in-depth correlation analysis of children’s facial affect and vocabulary learning. We found that children’s affective displays became more predictive of their vocabulary learning when children interacted with a tutee robot who did not scaffold their learning. Additionally, children’s affect displayed during micro-level events was more predictive of their learning than during macro-level events. Last, we found that the affect-learning relationship is not unidirectional, but rather is modulated by context, i.e., several affective states facilitated student learning when displayed in some sub-events but inhibited learning when displayed in others. These findings indicate that both social learning paradigm and sub-events within interaction modulate student affect-learning relationship. 

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4. Caregivers’ Multiple Roles in Supporting their Child through an Engineering Design Project

   Simpson, Amber; Yang, Jing; Knox, Peter N.; Maltese, Adam V. (January 2021, American Society for Engineering Education)

   null (Ed.)

   Caregivers are one of the most significant influences in their children’s engineering engagement at a young age; however, the roles caregivers can play in supporting their children is less understood. Employing an intrinsic case study on a five-month engineering program conducted in an out-of-school context, we illustrate the multiple and different roles that three caregivers enacted, and the contextual factors of the program that influenced and shaped their role enactment. We observed 12 dynamic, complex, and evolving roles that caregivers endorsed to support their child throughout the engineering design process. These roles were situated within preexisting rules and expectations as caregivers while also developing an understanding of the rules and expectations of an engineer through their social interactions with volunteer engineers and makers. This work contributes to our understanding of how to create environments to enable caregivers to best support their children’s STEM learning process. 

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5. Transitioning Toward Independence: Enhancing Collaborative Self-Management of Children with Type 1 Diabetes

   https://doi.org/10.1145/3491102.3502055  

   Cha, Yoon Jeong; Saxena, Arpita; Wou, Alice; Lee, Joyce; Newman, Mark W; Park, Sun Young (April 2023, the ACM/SIGCHI Conference on Human Factors in Computing Systems (CHI 2022))

   Although child participation is required for successful Type 1 Diabetes (T1D) management, it is challenging because the child’s young age and immaturity make it difficult to perform self-care. Thus, parental caregivers are expected to be heavily involved in their child’s everyday illness management. Our study aims to investigate how children and parents collaborate to manage T1D and examine how the children become more independent in their self-management through the support of their parents. Through semi-structured interviews with children with T1D and their parents (N=41), our study showed that children’s knowledge of illness management and motivation for self-care were crucial for their transition towards independence. Based on these two factors, we identified four types of children’s collaboration (i.e., dependent, resistant, eager, and independent) and parents’ strategies for supporting their children’s independence. We suggest design implications for technologies to support collaborative care by improving children’s transition to independent illness management. 

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