{"content":[{"text":"Socially Assistive Robotics for Personalized Education for Children","bbox":{"left":0.164299,"top":0.12009,"width":0.671412,"height":0.023566000000000004}},{"text":"Jillian Greczek, Elaine Short, Caitlyn E. Clabaugh, Katelyn Swift-Spong, and Maja Matari´c \n {greczek,elaine.g.short,clabaugh,swiftspo,mataric}@usc.edu \n Viterbi School of Engineering, University of Southern California, Los Angeles, CA 90089-0781","bbox":{"left":0.118162,"top":0.170904,"width":0.763678,"height":0.045881000000000005}},{"text":"Introduction \n Socially assistive robotics (SAR) has the potential to com- \n bine the massive replication and standardization of computer \n technology with the beneﬁts of learning in a social and tan- \n gible (hands-on) context. We are developing HRI methods \n for SAR systems designed to supplement the efforts of hu- \n man teachers to personalize education in the classroom. This \n abstract deﬁnes and proposes solutions to the computational \n challenges inherent in accomplishing differentiated and per- \n sonalized education utilizing SAR in real-world classrooms. \n We aim to design robotic systems that are compelling, as- \n sist children in achieving educational goals, and mitigate de- \n velopmental challenges in a classroom context. To do so, our \n approach must be deeply informed by the needs of our tar- \n get users, children, at all stages of development, and must \n adapt to a variety of special needs. In this abstract, we dis- \n cuss motivation and computational methods for personalized \n SAR systems for general, special needs, and mixed multi- \n child education contexts. We focus on the personalization \n and adaptation of curriculum, feedback, and robot character.","bbox":{"left":0.088235,"top":0.270816,"width":0.389714,"height":0.283758}},{"text":"Figure 1: Visualization of change in interaction between \n weeks one and two (black) and weeks two and three (red).","bbox":{"left":0.522059,"top":0.423631,"width":0.389713,"height":0.02899500000000005}},{"text":"second and third weeks in red. The direction and length \n of the visualized trajectories are proportional to the direc- \n tion and magnitude, respectively, of the participants’ change \n in interaction patterns from week to week. The trajectories \n of most participants are similar from week two to three. It \n can be hypothesized that participants whose movements are \n smaller than average from week two to three have minimal \n change in behavior; thus, a new or modiﬁed interaction strat- \n egy may be beneﬁcial for these participants, supporting the \n need for personalized education even within a general class- \n room context.","bbox":{"left":0.522059,"top":0.461752,"width":0.389714,"height":0.15352800000000005}},{"text":"General Education Classroom \n One process of developing systems for children with vary- \n ing needs and/or developmental challenges is to begin by re- \n cruiting participants from a more general population, and to \n develop systems and tools that will work for those children \n before extending to the target population. In one of our pre- \n liminary studies, children drawn from a general education \n classroom learned about nutrition in a one-on-one interac- \n tion using the DragonBot robot (Short et al. 2014). During \n each of six bi-weekly, ﬁve-to-ten minute long interactions, \n the child could select various artiﬁcial foods, present them \n to the robot, and receive feedback on those choices. Even \n within the general classroom population, we have found in- \n teresting variations based on age and sex that indicate the \n importance of differentiated education. \n We applied multidimensional scaling (MDS) to a select \n subset of features per interaction (mean response time, mean \n time to select food, and percentage of healthy food choices) \n for 16 participants over each of the three week interactions. \n We created a visualization of the participants’ trajectories \n over the three weeks. \n Figure 1 shows the change in participants’ interaction pat- \n terns between the ﬁrst and second weeks in black, and the","bbox":{"left":0.088235,"top":0.564628,"width":0.38971500000000003,"height":0.32526999999999995}},{"text":"Special Needs Education \n Through the pilot study in nutrition education for children \n described above, we found that children had a wide variety \n of reactions to the robot (especially in terms of their speech) \n and level of ability to perform the task (Short et al. 2014). As \n our work moves towards meeting the needs of children with \n developmental challenges, we expect to ﬁnd a signiﬁcant in- \n crease in variation within any individual developmental area, \n typically split into social, cognitive, gross-motor and ﬁne- \n motor development.","bbox":{"left":0.522059,"top":0.627269,"width":0.389714,"height":0.14733400000000008}},{"text":"Children with Autism Spectrum Disorders \n Children with autism spectrum disorders (ASDs) constitute \n a growing majority of special needs classrooms and have \n been one of our target beneﬁciary populations for over a \n decade (Feil-Seifer 2011). ASD symptoms can be nearly \n unique for each affected individual, and the recruitment of \n participants from such a sensitive population is often very","bbox":{"left":0.522059,"top":0.785344,"width":0.389714,"height":0.10455399999999992}}],"images_bbox":[{"left":0.531619,"top":0.264422,"width":0.370578,"height":0.144415}],"images_bbox_not_text_overlap":[{"left":0.531619,"top":0.264422,"width":0.370578,"height":0.144415}]}