Instructional discourse around physically-based material typically involves the use of gestures to focus attention in the joint visual space of the instructor and the student. Such gestures are performed in conjunction with speech, thus situating the discourse in the space of what the student is learning or doing. This kind of situated discourse is missing in distance learning. The lack of situatedness between conversants contributes to communication inefficiencies, as was recently highlighted when classes began using Zoom during COVID. For instruction that involves hands-on learning, the inability for instructors to index the student's space and vice versa is a more significant problem. The use of telepresence technologies allows this spatial gap to be bridged but can often leave a communication gap. Little evaluation has been made into how these mechanisms alter gesture and speech. This paper explores how giving the instructor situated gestural access to the student's workspace in distant learning settings alters gesture and speech, with the goal of guiding design of future systems that are mindful of how these systems work, not simply whether they work. Our research is based on a system that supports situated instructional discourse through augmented reality gesturing. The instructor is able to see the student's workspace and perform gestures that are superposed into the student's workspace. This shared gestural space is used in tandem with a traditional video chat. Two spatially-focused tasks were examined: a two-dimension problem that required varying levels of assistance, and a three-dimension problem with complete guidance by the instructor needed. We evaluated how gestures were used to augment verbal communication and the types of gestures and verbal utterances used. We found that static pointing G-hand deictic gestures were more common in the augmented reality mode than in the co-situated mode and complex spatial structuring gestures (e.g. orientation gestures) occurred more often in the co-situated mode than the augmented reality mode. Additionally, students provided a range of body postures and working orientations to encourage or discourage invasion of their personal workspace in co-situated settings, and several subjects indicated that the augmented reality made them feel more comfortable with sharing their gestural space. These results provide a framework to guide the design of future improvements of telepresence systems. Subjective statements of comfort by subjects also suggest this may be a profitable avenue for exploration with students who are reluctant to engage physically with instructors.

Writing and storytelling require both ideas of what is in the story, and how to use language skills to express the story. This paper addresses the problem of bringing story play into the orbit of formal writing by implementing digitally-augmented enactment to bridge the gap between concrete story ideas and the more abstract organizations for expression. Our approach provides children with an interactive experience and enables them to capture their imaginative play to support story expression (retelling or writing). We designed and developed a mixed-reality storytelling system that combines physical play and digital augmentation within a tabletop environment. The system tracks the physical figurines on the tabletop and produces an animated (cartoon) version of their story in real-time, which the children can then view and use as a memory aid and dynamic outline for writing. In addition, to support a viewpoint-dependent narrative thinking, we developed the concept of implementing a virtual camera that allows children to control the narrative perspective by physically manipulating how their story world is viewed. In this paper, we will present the design and development of our system and a pilot study we conducted with 6 children aged 7-12 where two children worked together on creating stories with and without using our system. We will present the preliminary results we obtained from the pilot study by analyzing the collected data including questionnaires, interviews, and the story videos and writings. The results show that children using our system exhibited greater motivation in creating stories and higher levels of collaboration and writing performance compared to traditional tabletop play.

This paper aims to analyze the practices in recruiting mentors for our STEM + Computing (STEM+C) MentorCorps project. We used quantitative research methods in this study. A total of 69 student mentors were entered into study analysis. We utilized descriptive statistics and conducted t-tests and ANOVA to compare mentors' prior teaching, experience with kids, computing experience, Arduino knowledge, and Making experience regarding gender and ethnicity. The results indicated that female mentors had more prior experience working with kids, while male mentors had more computing skills than their female counterparts. There are no significant differences between genders regarding previous teaching, Arduino knowledge, and Making. Further, no differences were found between Caucasian, Hispanic, Asian, and Black/ African mentors regarding these five variables.

From an early age, children begin developing critical motor skills, such as fine motor control, that contribute significantly to reading, writing, drawing, and more, all of which are important for communication and school readiness. Pediatricians can evaluate a child’s motor skills using activities and questionnaires. Sometimes these involve adults drawing with their child, but it can be difficult to fully evaluate a child’s drawings through a handful of sketches from limited direct assessments. We propose creating a sketching system that will collect free-form drawing data from parents and children that can then automatically differentiate a child’s sketch from an adult’s using only the pen strokes of their drawing. In this paper, we describe our study that collected sketches from 14 children aged 2 to 5 and 25 adults over 18. We contribute a machine learning classifier based on sketch recognition features from free-hand drawings capable of distinguishing children’s sketches from those made by adults with an F-measure of 0.906. These results indicate the potential of creating sketch-based applications for assessing children’s fine motor development.

POWER5 offers significantly increased performance over previous POWER designs by incorporating simultaneous multithreading, an enhanced memory subsystem, and extensive RAS and power management support. The 276M transistor processor is implemented in 130nm silicon-on-insulator technology with 8-level of Cu metallization and operates at >1.5 GHz.