Learning IoT without the "I" - Educational Internet of Things in a Developing Context

To provide better education to children from different socio-economic backgrounds, the Thai Government launched the "One Tablet PC Per Child" (OTPC) policy and distributed 800,000 tablet computers to first grade students across the country in 2012. This initiative is an opportunity to study how mobile learning and Internet of Things (IoT) technology can be designed for students in underprivileged areas of northern Thailand. In this position paper, we present a prototype, called OBSY (Observation Learning System) which targets primary science education. OBSY consists of i) a sensor device, developed with low-cost open source singled-board computer Raspberry Pi, housed in a 3D printed case, ii) a mobile device friendly graphical interface displaying visualisations of the sensor data, iii) a self-contained DIY Wi-Fi network which allows the system to operate in an environment with inadequate ICT infrastructure

The Social Interaction Experiences of Older People in a 3D Virtual Environment

Virtual worlds offer much potential in supporting social interaction for older adults, particularly as a platform which can provide an interactive and immersive social experience. Yet, there has not been much work carried out to study the use, interaction and behavior of older people in 3D virtual world systems, especially studies which investigate their interactions in a fully functional virtual world. Most focus on issues related to usability such as cognitive difficulties when navigation in a 3D space and we know little about their perceptions and preferences when socializing in a virtual space. In this chapter, we report an experimental study examining the various factors which affected the social experience of older users in virtual worlds. The study involved 38 older participants engaging with a 3D and non-3D virtual grocery store. A mixed method of questionnaire and contextual interview was used for data collection and analysis. Overall, we found that physical presence was a significant predictor of many measures defining the quality of social interaction, yet participants often reported a sense of artificiality in their virtual experience. Interestingly, avatars were not considered directly important for social interaction and instead were only seen as a “place holder” to complete the tasks. Two factors contributed to this, the lack of non-verbal communication and the perceived difficulty in embodying physical people with virtual avatars

Cue Now, Reflect Later: A Study of Delayed Reflection of Diary Events

Diary studies require participants to record entries at the moment of events, but the process often distracts the participants and disrupts the flow of the events. In this work, we explore the notion of delayed reflection for diary studies. Users quickly denote cues of diary events and only reflect on the cues later when they are not busy. To minimize disruptions, we employed a squeeze gesture that is swift and discreet for denoting cues. We investigated the feasibility of delayed reflection and compared it against a conventional digital diary that requires users to reflect immediately at the time of entry. In a weeklong field study, we asked participants to record their daily experiences with both types of diaries. Our results show that users’ preference is context-dependent. Delayed reflection is favored for use in contexts when interruptions are deemed inappropriate (e.g. in meetings or lectures) or when the users are mobile (e.g. walking). In contrast, the users prefer immediate reflection when they are alone, such as during leisure and downtime

weSport: Utilising Wrist-Band Sensing to Detect Player Activities in Basketball Games

Wristbands have been traditionally designed to track the activities of a single person. However there is an opportunity to utilize the sensing capabilities of wristbands to offer activity tracking services within the domain of team-based sports games. In this paper we demonstrate the design of an activity tracking system capable of detecting the players’ activities within a one-to-one basketball game. Relying on the inertial sensors of wristbands and smartphones, the system can capture the shooting attempts of each player and provide statistics about their performance. The system is based on a two- level classification architecture, combining data from both players in the game. We employ a technique for semi-automatic labelling of the ground truth that requires minimum manual input during a training game. Using a single game as a training dataset, and applying the classifier on future games we demonstrate that the system can achieve a good level of accuracy detecting the shooting attempts of both players in the game (precision 91.34%, recall 94.31%)

Generalizability of Machine Learning to Categorize Various Mental Illness Using Social Media Activity Patterns

Mental illness has recently become a global health issue, causing significant suffering in people’s lives and having a negative impact on productivity. In this study, we analyzed the generalization capacity of machine learning to classify various mental illnesses across multiple social media platforms (Twitter and Reddit). Language samples were gathered from Reddit and Twitter postings in discussion forums devoted to various forms of mental illness (anxiety, autism, schizophrenia, depression, bipolar disorder, and BPD). Following this process, information from 606,208 posts (Reddit) created by a total of 248,537 people and from 23,102,773 tweets was used for the analysis. We initially trained and tested machine learning models (CNN and Word2vec) using labeled Twitter datasets, and then we utilized the dataset from Reddit to assess the effectiveness of our trained models and vice versa. According to the experimental findings, the suggested method successfully classified mental illness in social media texts even when training datasets did not include keywords or when unrelated datasets were utilized for testing

Social and Linguistic Behavior and its Correlation to Trait Empathy

A growing body of research exploits social media behaviors to gauge psychological character- istics, though trait empathy has received little attention. Because of its intimate link to the abil- ity to relate to others, our research aims to predict participants’ levels of empathy, given their textual and friending behaviors on Facebook. Using Poisson regression, we compared the vari- ance explained in Davis’ Interpersonal Reactivity Index (IRI) scores on four constructs (em- pathic concern, personal distress, fantasy, perspective taking), by two classes of variables: 1) post content and 2) linguistic style. Our study lays the groundwork for a greater understanding of empathy’s role in facilitating interactions on social media

Show Me You Care: Trait Empathy, Linguistic Style and Mimicry on Facebook

Linguistic mimicry, the adoption of another’s language patterns, is a subconscious behavior with pro-social benefits. However, some professions advocate its conscious use in empathic communication. This involves mutual mimicry; effective communicators mimic their interlocutors, who also mimic them back. Since mimicry has often been studied in face-to-face contexts, we ask whether individuals with empathic dis- positions have unique communication styles and/or elicit mimicry in mediated communication on Facebook. Participants completed Davis’ Interpersonal Reactivity Index and provided access to Facebook activity. We confirm that dispositional empathy is correlated to the use of particular stylistic features. In addition, we identify four empathy profiles and find correlations to writing style. When a linguistic feature is used, this often “triggers” use by friends. However, the presence of particular features, rather than participant dispo- sition, best predicts mimicry. This suggests that machine-human communications could be enhanced based on recently used features, without extensive user profiling