Exploring Kid Space in the wild: a preliminary study of multimodal and immersive collaborative play-based learning experiences

Parents recognize the potential benefits of technology for their young children but are wary of too much screen time and its potential deficits in terms of social engagement and physical activity. To address these concerns, related literature suggests technology usages with a blend of digital and physical learning experiences. Towards this end, we developed Kid Space, incorporating immersive computing experiences designed to engage children more actively in physical movement and social collaboration during play-based learning. The technology features an animated peer learner, Oscar, who aims to understand and respond to children's actions and utterances using extensive multimodal sensing and sensemaking technologies. To investigate student engagement during Kid Space learning experiences, an exploratory case study was designed using a formative research method with eight first-grade students. Multimodal data (audio and video) along with observational, interview, and questionnaire data were collected and analyzed. The results show that the students demonstrated high levels of engagement, less attention focused on the screen (projected wall), and more physical activity. In addition to these promising results, the study also enabled us to understand actionable insights to improve Kid Space for future deployments (e.g., the need for real-time personalization). We plan to incorporate the lessons learned from this preliminary study and deploy Kid Space with real-time personalization features for longer periods with more students.

Automated indexing of Internet stories for health behavior change: weight loss attitude pilot study

Background

Automated health behavior change interventions show promise, but suffer from high attrition and disuse. The Internet abounds with thousands of personal narrative accounts of health behavior change that could not only provide useful information and motivation for others who are also trying to change, but an endless source of novel, entertaining stories that may keep participants more engaged than messages authored by interventionists.

Objective

Given a collection of relevant personal health behavior change stories gathered from the Internet, the aim of this study was to develop and evaluate an automated indexing algorithm that could select the best possible story to provide to a user to have the greatest possible impact on their attitudes toward changing a targeted health behavior, in this case weight loss.

Methods

An indexing algorithm was developed using features informed by theories from behavioral medicine together with text classification and machine learning techniques. The algorithm was trained using a crowdsourced dataset, then evaluated in a 2×2 between-subjects randomized pilot study. One factor compared the effects of participants reading 2 indexed stories vs 2 randomly selected stories, whereas the second factor compared the medium used to tell the stories: text or animated conversational agent. Outcome measures included changes in self-efficacy and decisional balance for weight loss before and after the stories were read.

Results

Participants were recruited from a crowdsourcing website (N=103; 53.4%, 55/103 female; mean age 35, SD 10.8 years; 65.0%, 67/103 precontemplation; 19.4%, 20/103 contemplation for weight loss). Participants who read indexed stories exhibited a significantly greater increase in self-efficacy for weight loss compared to the control group (F_1,107=5.5, P=.02). There were no significant effects of indexing on change in decisional balance (F_1,97=0.05, P=.83) and no significant effects of medium on change in self-efficacy (F_1,107=0.04, P=.84) or decisional balance (F_1,97=0.78, P=.38).

Conclusions

Personal stories of health behavior change can be harvested from the Internet and used directly and automatically in interventions to affect participant attitudes, such as self-efficacy for changing behavior. Such approaches have the potential to provide highly tailored interventions that maximize engagement and retention with minimal intervention development effort.