{"content":[{"text":"challenging. Additionally, when working with special needs \n populations, it is often necessary to bring the experimental \n setup to a context they are familiar with (school, home, ther- \n apy room) rather than making accommodations to bring par- \n ticipants into the lab. Therefore, the sensing used in these \n studies must be robust to real-world noise but also portable \n and easy to set up. Computational challenges are found in \n expanding human-robot interactions to span longer, multi- \n ple sessions to allow for generalized behavior change and \n skill improvement to take place.","bbox":{"left":0.088235,"top":0.069138,"width":0.389714,"height":0.139691}},{"text":"tally appropriate tasks that are optimal for a triadic and so- \n cially mediated interaction between two children with vary- \n ing needs and a robot. \n Each child in US special education receives an Individual \n Education Plan (IEP), detailing the individual child’s chal- \n lenges and educational goals and the speciﬁc steps that will \n be taken to meet the child’s needs. We aim to leverage the \n information in IEPs to build computational models of edu- \n cational tasks that allow a single task to be adapted to help \n address any combination of developmental challenges. An \n ideal SAR system will exist in an environment populated \n by both typically-developing children and children with de- \n velopmental challenges. Towards this end, we are exploring \n computational methods to facilitate social interaction and \n educational goals by optimizing robotic instruction for any \n pair of children.","bbox":{"left":0.522059,"top":0.069138,"width":0.389714,"height":0.222712}},{"text":"Robot-Mediated Social Skills Practice \n A recent study we performed addressed SAR-mediated skill \n practice with school children with ASDs. We examined the \n beneﬁts of autonomy-promoting graded feedback during an \n imitation game between a robot and a child with an ASD \n (Greczek et al. 2014). Graded cueing is an occupational \n therapy technique of giving the minimum required feedback \n when learning or practicing a task in order to promote pa- \n tient autonomy and learning generalized skills. The imple- \n mentation of the model was used to give feedback on imi- \n tation accuracy in a “copy-cat” game played between a Nao \n robot and a child (aged 7-10). \n We sought to address the low-N, high-variance nature \n of this population with a Bayesian model of ﬁrst prompt \n choice. Within four possible user prompts of increasing \n speciﬁcity, the graded cueing model selects the least speciﬁc \n feedback appropriate to the user’s skill level based on previ- \n ous observed user behavior. In this way, the model can reach \n a minimal level of feedback personalized to each user that \n changes along with the user’s ability over time. Although \n the model was not fully exercised in the study, graded cueing \n style feedback was found to be at least as good as maximally \n speciﬁc feedback after every user mistake. These results en- \n courage future work focusing on increasing user autonomy \n at a task without impacting performance.","bbox":{"left":0.088235,"top":0.220736,"width":0.389714,"height":0.355389}},{"text":"Future Work \n Our future work in developing personalization algorithms \n for SAR systems centers on creating a method for selecting \n optimal educational tasks in multi-child robot interactions, \n expanding the graded cueing framework, and creating meth- \n ods for customizing the robot’s backstory. \n In our preliminary nutrition education study using the \n DragonBot, we designed a backstory for the robot that con- \n nected the robot’s character to its physical embodiment and \n the task (Short et al. 2014). Moving forward we are explor- \n ing the theme of developing aligned and consistent robot \n characters where the robots backstory, embodiment, and per- \n sonality all form a cohesive task-relevant and interaction \n partner. Personality consistency between verbal and non- \n verbal modalities of a computer agent has been shown to \n increase liking (Isbister and Nass 2000). We plan to con- \n vey personality through these modalities as well as develop \n a method for creating a robot backstory that is consistent \n with the robot’s personality. \n Our future work in the context of graded cueing will ex- \n pand the framework to include tasks with multiple steps \n and goals over many sessions with the robot. The key in \n this expansion will be the representation of each child’s \n knowledge and abilities as part of a larger ontological struc- \n ture of domain-speciﬁc skills. The larger goal is to create \n a personalized health education framework with autonomy- \n encouraging feedback from the SAR system. \n Due to an increasing level of primary education standard- \n ization in the US, it is possible to ﬁnd itemized curricula \n designed to address the wide but ﬁnite variety of develop- \n mental challenges that children experience. In the context of \n special education, we plan to leverage existing curricula to \n develop a method for selecting an optimal educational task \n in multi-child interactions with a SAR system. We plan to \n develop methods that will enable SAR systems to cater to \n multiple childrens’ educational and developmental needs in \n a real-world classroom. \n Acknowledgements \n This work is supported by the NSF Graduate Research \n Fellowship Program and NSF grants IIS-1139148, IIS- \n 0803565, IIS-0713697, and CNS-0709296.","bbox":{"left":0.522059,"top":0.301693,"width":0.389714,"height":0.588205}},{"text":"Multi-Child Interaction \n Computational research in adaptive user models for edu- \n cation is often limited to optimizing for individual users \n (Leyzberg, Spaulding, and Scassellati 2014; Han and Kim \n 2009; Schiafﬁno, Garcia, and Amandi 2008). Such research \n is motivated by the “two-sigma” phenomenon which shows \n that one-on-one tutoring leads to student performance that is \n two standard deviations above average (Corbett 2001). How- \n ever, in many educational contexts, dyadic instruction be- \n tween one robot and one user may not optimally accomplish \n educational goals. One intuitive instance is the development \n of social communication skills in children with ASDs as in \n the graded cueing study (Greczek et al. 2014). According \n to socio-constructivist theory, knowledge construction is so- \n cially mediated, and thus, it is important that both the teacher \n and the entire learning environment be social (Hickey 1997). \n The legal principle of Least Restrictive Environment (LRE) \n supports this approach by requiring that children with spe- \n cial needs are educated in as general a classroom as possi- \n ble (Hasazi et al. 1994). In keeping with these ideas, we are \n inspired to develop a computational model of developmen-","bbox":{"left":0.088235,"top":0.589281,"width":0.389714,"height":0.3006169999999999}}],"images_bbox":[],"images_bbox_not_text_overlap":[]}