Supporting children’s writing activities is a primary concern in elementary-school grades. Using embodied means such as visual stimuli through pictures or motion pictures has been shown effective in supporting children’s writing. To guide children during the writing activity, contextual cues are usually provided in face-to-face classroom practice with interactive instruction from a teacher. This paper explores the design of an interactive online system for video-scaffolded writing with contextual cues. In our online study with 13 children, we explored two approaches to designing contextual cues: temporally situated and visually situated. Participants were divided into two groups, each using a web-based interface to watch an animated video and write the story guided by one of the cue designs. We analyzed the children’s writing process and outcome in both groups. Our results show that temporally situated cues support a stronger overall story structure, while visually situated cues support more descriptive and interpretive writing.

Many story authoring tools have been designed to support children’s creativity through story enactment. However, at school, children are encouraged to engage in formal modes of expression, such as writing. This paper aims to explore how we may design tools that support children in writing using body-based enactment as a scaffold. In our study, 17 children used an interactive story authoring system to enact and record stories as videos and then write the stories while viewing them. We compared the story enactment videos and the corresponding written story essays in terms of the narrative structure, content, and coherence. Our results show a significant difference in story structure in the two formats and a marginally significant difference in the coherence ratings. We present our findings in terms of learned lessons for designing children’s enactment-based authoring tools to encourage more substantial written outcomes.

The need for interpretable and accountable intelligent systems grows along with the prevalence of artificial intelligence ( AI ) applications used in everyday life. Explainable AI ( XAI ) systems are intended to self-explain the reasoning behind system decisions and predictions. Researchers from different disciplines work together to define, design, and evaluate explainable systems. However, scholars from different disciplines focus on different objectives and fairly independent topics of XAI research, which poses challenges for identifying appropriate design and evaluation methodology and consolidating knowledge across efforts. To this end, this article presents a survey and framework intended to share knowledge and experiences of XAI design and evaluation methods across multiple disciplines. Aiming to support diverse design goals and evaluation methods in XAI research, after a thorough review of XAI related papers in the fields of machine learning, visualization, and human-computer interaction, we present a categorization of XAI design goals and evaluation methods. Our categorization presents the mapping between design goals for different XAI user groups and their evaluation methods. From our findings, we develop a framework with step-by-step design guidelines paired with evaluation methods to close the iterative design and evaluation cycles in multidisciplinary XAI teams. Further, we provide summarized ready-to-use tables of evaluation methods and recommendations for different goals in XAI research.

Storytelling is a critical step in the cognitive development of children. Particularly, this requires children to mentally project into the story context and to identify with the thoughts of the characters in their stories. We propose to support free imagination in creative storytelling through an enactment-based approach that allows children to embody an avatar and perform as the story character. We designed our story creation interface with two modes of avatar: the story-relevant avatar and the self-avatar, to investigate the effects of avatar design on the quality of children's creative products. In our study with 20 child participants, the results indicate that self-avatars can create a stronger sense of identification and embodied presence, while story-relevant avatars can provide a scaffold for mental projection.

Touch is a contextual medium for affect conveyance. It is difficult to assign specific meanings to each instance of touch in isolation. However, when engaged within context, touch becomes a powerful and indispensable means for humans to convey feelings of love, concern, and sympathy. This paper explores how remote touch may influence one's affective experience of music, and how this experience is dependent on the relationship between the originator and recipient of the touch. For our study, we designed: (1) A mediated touch system that provides kinesthetic touches through an armband device that is controlled by changes in force applied to an input device, and (2) A touch-media system by which one can record and playback a co-temporal stream of touches with a track of music. We examined: (1) How remote touch may influence a touch recipient's experience of the music, and (2) how the recipients' affective experiences may depend on whether the touch is believed to be generated by intimates (familiar touch) versus by a music performer as part of a performance (unfamiliar touch)? Our study's results revealed that there is a significant correlation between the conditions (familiar, unfamiliar, and no touches) and song types (happy, sad) in both positive and negative affective experiences.

This paper extends the concept of digital literacy and applies it to Making. Through case descriptions, we contribute an understanding of how children can become or fail to become individuals literate in Making within a formal learning context. Our analysis draws from video recordings and other data sources of two 4th grade classrooms in which the students, who had already participated in 1.5 years of more structured 'makified activities', engaged in an open-ended, exploration-based, and playful task that was more in line with the spirit of Making. Student teams were classified as 'high in Making literacy' and 'low in Making literacy', revealing how Making literacy was expressed at the level of skills, mental models, and practices in various ways for different students. Our qualitative analysis demonstrates what burgeoning Making literacy may mean in a public elementary school classroom, paving the way for a vision of a time when Making becomes generalized practice.

We present Making activities designed and observed within the formal environment of elementary school classrooms. Using a collaborative curriculum-matching design process with teachers, 8 Maker activities and lesson plans were developed, and implemented in Science and Language Arts classrooms of a school with a large percentage of students from underrepresented populations, over the course of 18 weeks during one semester. Coded videos revealed three categories of Maker activities: those that enabled learning, demonstrated learning, and provided learning of the concept itself. Experiences of teachers and students also revealed eagerness to participate, engagement, and exploration in the activities, as observed in a series of analyses. Other themes include the importance of multi-sensory exploration and ownership of self-constructed apparatuses with electronics. The resulting Maker activities and lesson plans offer strategies for familiarizing students with electronic tools and fostering tinkering while remaining true to the learning standards of the classroom.

Automatic analysis of human facial emotions is one of the challenging problems in intelligent systems and social signal processing. It has many applications in human-computer interactions, social robots, interactive multimedia and behavior monitoring. In this paper, our specific aim is to develop a method for facial movement recognition based on verifying movable facial elements and estimate the movements after any facial expressions. The algorithm plots a face model graph based on facial expression muscles in each frame and extracts features by measuring facial graph edges' size and angle variations. Seven facial expressions, including neutral pose are being classified in this study using support vector machine and other classifiers on MMI databases. The approach does not rely on action unit system, and therefore eliminates errors which are otherwise propagated to the final result due to incorrect initial identification of action units. Experimental results show that analyzing facial movements gives accurate and efficient information in order to identify different facial expressions.