EnviroScape: Coping With Stress Using Implicit Biofeedback Application

Stress has been identified by the Word Health Organization as an epidemic that has negative impacts on work productivity. It costs the American industry approximately $300 billion/year and is also the leading contributor to obesity and cardiovascular diseases. Current stress remediation tools incorporate techniques such as deep breathing, meditation and biofeedback responses. These type of exercises require a substantial amount of time and resources along with adhering to their strict system in order to see results. Most biofeedback mechanisms are repetitive and mundane and also require complex equipment to participate, in order to receive proper evaluation on stress levels. The purpose of this study is to develop an engaging relaxation technique and analyze the effects of the biofeedback mechanism on the stress levels of a user. An interactive application is developed such that the user receives subtle cues when they are in a “stressed” state, which is determined through the physiological indicator of the user’s breathing rate (BR) signal. Unlike previous research, this biofeedback game focuses on providing a soothing natural environment with no specific objectives in order to distract them from their current stressful state. This will help analyze and discuss the effects of a non-competitive video game on a user’s stress levels, their awareness to recognize signs of stress and their ability to reduce them

Scalable Teaching and Learning via Intelligent User Interfaces

The increasing demand for higher education and the educational budget cuts lead to large class sizes. Learning at scale is also the norm in Massive Open Online Courses (MOOCs). While it seems cost-effective, the massive scale of class challenges the adoption of proven pedagogical approaches and practices that work well in small classes, especially those that emphasize interactivity, active learning, and personalized learning. As a result, the standard teaching approach in today’s large classes is still lectured-based and teacher-centric, with limited active learning activities, and with relatively low teaching and learning effectiveness. This dissertation explores the usage of Intelligent User Interfaces (IUIs) to facilitate the efficient and effective adoption of the tried-and-true pedagogies at scale. The first system is MindMiner, an instructor-side data exploration and visualization system for peer review understanding. MindMiner helps instructors externalize and quantify their subjective domain knowledge, interactively make sense of student peer review data, and improve data exploration efficiency via distance metric learning. MindMiner also helps instructors generate customized feedback to students at scale. We then present BayesHeart, a probabilistic approach for implicit heart rate monitoring on smartphones. When integrated with MOOC mobile clients, BayesHeart can capture learners’ heart rates implicitly when they watch videos. Such information is the foundation of learner attention/affect modeling, which enables a ‘sensorless’ and scalable feedback channel from students to instructors. We then present CourseMIRROR, an intelligent mobile system integrated with Natural Language Processing (NLP) techniques that enables scalable reflection prompts in large classrooms. CourseMIRROR 1) automatically reminds and collects students’ in-situ written reflections after each lecture; 2) continuously monitors the quality of a student’s reflection at composition time and generates helpful feedback to scaffold reflection writing; 3) summarizes the reflections and presents the most significant ones to both instructors and students. Last, we present ToneWars, an educational game connecting Chinese as a Second Language (CSL) learners with native speakers via collaborative mobile gameplay. We present a scalable approach to enable authentic competition and skill comparison with native speakers by modeling their interaction patterns and language skills asynchronously. We also prove the effectiveness of such modeling in a longitudinal study

A Design Framework for Engaging Collective Interaction Applications for Mobile Devices

The main objective of this research is to define the conceptual and technological key factors of engaging collective interaction applications for mobile devices. To answer the problem, a throwaway prototyping software development method is utilized to study design issues. Furthermore, a conceptual framework is constructed in accordance with design science activities. This fundamentally exploratory research is a combination of literature review, design and implementation of mobile device based prototypes, as well as empirical humancomputer interaction studies, which were conducted during the period 2008 - 2012. All the applications described in this thesis were developed mainly for research purposes in order to ensure that attention could be focused on the problem statement. The thesis presents the design process of the novel Engaging Collective Interaction (ECI) framework that can be used to design engaging collective interaction applications for mobile devices e.g. for public events and co-creational spaces such as sport events, schools or exhibitions. The building and evaluating phases of design science combine the existing knowledge and the results of the throwaway prototyping approach. Thus, the framework was constructed from the key factors identified of six developed and piloted prototypes. Finally, the framework was used to design and implement a collective sound sensing application in a classroom setting. The evaluation results indicated that the framework offered knowledge to develop a purposeful application. Furthermore, the evolutionary and iterative framework building process combined together with the throwaway prototyping process can be presented as an unseen Dual Process Prototyping (DPP) model. Therefore it is claimed that: 1) ECI can be used to design engaging collective interaction applications for mobile devices. 2) DPP is an appropriate method to build a framework or a model. This research indicates that the key factors of the presented framework are: collaborative control, gamification, playfulness, active spectatorship, continuous sensing, and collective experience. Further, the results supported the assumption that when the focus is more on activity rather than technology, it has a positive impact on the engagement. As a conclusion, this research has shown that a framework for engaging collective interaction applications for mobile devices can be designed (ECI) and it can be utilized to build an appropriate application. In addition, the framework design process can be presented as a novel model (DPP). The framework does not provide a step-by-step guide for designing applications, but it helps to refine the design of successful ones. The overall benefit of the framework is that developers can pay attention to the factors of engaging application at an early stage of design

Digital Food Marketing to Children and Adolescents: Problematic Practices and Policy Interventions

Examines trends in digital marketing to youth that uses "immersive" techniques, social media, behavioral profiling, location targeting and mobile marketing, and neuroscience methods. Recommends principles for regulating inappropriate advertising to youth

A Design Exploration of Affective Gaming

Physiological sensing has been a prominent fixture in games user research (GUR) since the late 1990s, when researchers began to explore its potential to enhance and understand experience within digital game play. Since these early days, it has been widely argued that “affective gaming”—in which gameplay is influenced by a player’s emotional state—can enhance player experience by integrating physiological sensors into play. In this thesis, I conduct a design exploration of the field of affective gaming by first, systematically exploring the field and creating a framework (the affective game loop) to classify existing literature; and second by presenting two design probes, to probe and explore the design space of affective games contextualized within the affective game loop: In the Same Boat and Commons Sense. The systematic review explored this unique design space of affective gaming, opening up future avenues for exploration. The affective game loop was created as a way to classify the physiological signals and sensors most commonly used in prior literature within the context of how they are mapped into the gameplay itself. Findings suggest that the physiological input mappings can be more action-based (e.g., affecting mechanics in the game such as the movement of the character) or more context-based (e.g., affecting things like environmental or difficulty variables in the game). Findings also suggested that while the field has been around for decades, there is still yet to be any commercial successes, so does physiological interaction really heighten player experience? This question instigated the design of the two probes, exploring ways to implement these mappings and effectively heighten player experience. In the Same Boat (Design Probe One) is an embodied mirroring game designed to promote an intimate interaction, using players’ breathing rate and facial expressions to control movement of a canoe down a river. Findings suggest that playing In the Same Boat fostered the development of affiliation between the players, and that while embodied controls were less intuitive, people enjoyed them more, indicating the potential of embodied controls to foster social closeness in synchronized play over a distance. Commons Sense (Design Probe Two) is a communication modality intended to heighten audience engagement and effectively capture and communicate the audience experience, using a webcam-based heart rate detection software that takes an average of each spectator’s heart rate as input to affect in-game variables such as lighting and sound design, and game difficulty. Findings suggest that Commons Sense successfully facilitated the communication of audience response in an online entertainment context—where these social cues and signals are inherently diminished. In addition, Commons Sense is a communication modality that can both enhance a play experience while offering a novel way to communicate. Overall, findings from this design exploration shows that affective games offer a novel way to deliver a rich gameplay experience for the player

Learning Opportunities and Challenges of Sensor-enabled Intelligent Tutoring Systems on Mobile Platforms: Benchmarking the Reliability of Mobile Sensors to Track Human Physiological Signals and Behaviors to Enhance Tablet-Based Intelligent Tutoring Systems

Desktop-based intelligent tutoring systems have existed for many decades, but the advancement of mobile computing technologies has sparked interest in developing mobile intelligent tutoring systems (mITS). Personalized mITS are applicable to not only stand-alone and client-server systems but also cloud systems possibly leveraging big data. Device-based sensors enable even greater personalization through capture of physiological signals during periods of student study. However, personalizing mITS to individual students faces challenges. The Achilles heel of personalization is the feasibility and reliability of these sensors to accurately capture physiological signals and behavior measures. This research reviews feasibility and benchmarks reliability of basic mobile platform sensors in various student postures. The research software and methodology are generalizable to a range of platforms and sensors. Incorporating the tile-based puzzle game 2048 as a substitute for a knowledge domain also enables a broad spectrum of test populations. Baseline sensors include the on-board camera to detect eyes/faces and the Bluetooth Empatica E4 wristband to capture heart rate, electrodermal activity (EDA), and skin temperature. The test population involved 100 collegiate students randomly assigned to one of three different ergonomic positions in a classroom: sitting at a table, standing at a counter, or reclining on a sofa. Well received by the students, EDA proved to be more reliable than heart rate or face detection in the three different ergonomic positions. Additional insights are provided on advancing learning personalization through future sensor feasibility and reliability studies