An old proverb claims that “two heads are better than one”. Crowdsourcing research and practice have taken this to heart, attempting to show that thousands of heads can be even better. This is not limited to leveraging a crowd’s knowledge, but also their creativity—the ability to generate something not only useful, but also novel. In practice, there are initiatives such as Free and Open Source Software communities developing innovative software. In research, the field of crowdsourced creativity, which attempts to design scalable support mechanisms, is blooming. However, both contexts still present many opportunities for advancement.

In this dissertation, I seek to advance both the knowledge of limitations in current technologies used in practice as well as the mechanisms that can be used for large-scale support. The overall research question I explore is: “How can we support large-scale creative collaboration in distributed online communities?” I first advance existing support techniques by evaluating the impact of active support in brainstorming performance. Furthermore, I leverage existing theoretical models of individual idea generation as well as recommender system techniques to design CrowdMuse, a novel adaptive large-scale idea generation system. CrowdMuse models users in order to adapt itself to each individual. I evaluate the system’s efficacy through two large-scale studies. I also advance knowledge of current large-scale practices by examining common communication channels under the lens of Creativity Support Tools, yielding a list of creativity bottlenecks brought about by the affordances of these channels. Finally, I connect both ends of this dissertation by deploying CrowdMuse in an Open Source online community for two weeks. I evaluate their usage of the system as well as its perceived benefits and issues compared to traditional communication tools.

This dissertation makes the following contributions to the field of large-scale creativity: 1) the design and evaluation of a first-of-its-kind adaptive brainstorming system; 2) the evaluation of the effects of active inspirations compared to simple idea exposure; 3) the development and application of a set of creativity support design heuristics to uncover creativity bottlenecks; and 4) an exploration of large-scale brainstorming systems’ usefulness to online communities.

Advances in technology are fueling a movement toward ubiquity for beyond-the-desktop systems. Novel interaction modalities, such as free space or full body gestures are becoming more common, as demonstrated by the rise of systems such as the Microsoft Kinect. However, much of the interaction design research for such systems is still focused on desktop and touch interactions. Current thinking in free-space gestures are limited in capability and imagination and most gesture studies have not attempted to identify gestures appropriate for public walk-up-and-use applications. A walk-up-and-use display must be discoverable, such that first-time users can use the system without any training, flexible, and not fatiguing, especially in the case of longer-term interactions. One mechanism for defining gesture sets for walk-up-and-use interactions is a participatory design method called gesture elicitation. This method has been used to identify several user-generated gesture sets and shown that user-generated sets are preferred by users over those defined by system designers. However, for these studies to be successfully implemented in walk-up-and-use applications, there is a need to understand which components of these gestures are semantically meaningful (i.e. do users distinguish been using their left and right hand, or are those semantically the same thing?). Thus, defining a standardized gesture vocabulary for coding, characterizing, and evaluating gestures is critical. This dissertation presents three gesture elicitation studies for walk-up-and-use displays that employ a novel gesture elicitation methodology, alongside a novel coding scheme for gesture elicitation data that focuses on features most important to users’ mental models. Generalizable design principles, based on the three studies, are then derived and presented (e.g. changes in speed are meaningful for scroll actions in walk up and use displays but not for paging or selection). The major contributions of this work are: (1) an elicitation methodology that aids users in overcoming biases from existing interaction modalities; (2) a better understanding of the gestural features that matter, e.g. that capture the intent of the gestures; and (3) generalizable design principles for walk-up-and-use public displays.

Research has shown that the learning processes can be enriched and enhanced with the presence of affective interventions. The goal of this dissertation was to design, implement, and evaluate an affective agent that provides affective support in real-time in order to enrich the student’s learning experience and performance by inducing and/or maintaining a productive learning path. This work combined research and best practices from affective computing, intelligent tutoring systems, and educational technology to address the design and implementation of an affective agent and corresponding pedagogical interventions. It included the incorporation of the affective agent into an Exploratory Learning Environment (ELE) adapted for this research.

A gendered, three-dimensional, animated, human-like character accompanied by text- and speech-based dialogue visually represented the proposed affective agent. The agent’s pedagogical interventions considered inputs from the ELE (interface, model building, and performance events) and from the user (emotional and cognitive events). The user’s emotional events captured by biometric sensors and processed by a decision-level fusion algorithm for a multimodal system in combination with the events from the ELE informed the production-rule-based behavior engine to define and trigger pedagogical interventions. The pedagogical interventions were focused on affective dimensions and occurred in the form of affective dialogue prompts and animations.

An experiment was conducted to assess the impact of the affective agent, Hope, on the student’s learning experience and performance. In terms of the student’s learning experience, the effect of the agent was analyzed in four components: perception of the instructional material, perception of the usefulness of the agent, ELE usability, and the affective responses from the agent triggered by the student’s affective states.

Additionally, in terms of the student’s performance, the effect of the agent was analyzed in five components: tasks completed, time spent solving a task, planning time while solving a task, usage of the provided help, and attempts to successfully complete a task. The findings from the experiment did not provide the anticipated results related to the effect of the agent; however, the results provided insights to improve diverse components in the design of affective agents as well as for the design of the behavior engines and algorithms to detect, represent, and handle affective information.

The 21st century engineer will face a diverse set of challenges spread out along a broad spectrum of disciplines. Among others, the fields of energy, healthcare, cyberspace, virtual reality, and neuroscience require monumental efforts by the new generation of engineers to meet the demands of a growing society. However the most important, and likely the most under recognized, challenge lies in developing advanced personalized learning. It is the core foundation from which the rest of the challenges can be accomplished. Without an effective method of teaching engineering students how to realize these grand challenges, the knowledge pool from which to draw new innovations and discoveries will be greatly diminished. This paper introduces the Inventors Workshop (IW), a hands-on, passion-based approach to personalized learning. It is intended to serve as a manual that will inform the next generation of student leaders and inventioneers about the core concepts the Inventors Workshop was built upon, and how to continue improvement into the future. Due to the inherent complexities in the grand challenge of personalized learning, the IW has developed a multifaceted solution that is difficult to explain in a single phrase. To enable comprehension of the IW's full vision, the process undergone to date of establishing and expanding the IW is described. In addition, research has been conducted to determine a variety of paths the Inventors Workshop may utilize in future expansion. Each of these options is explored and related to the core foundations of the IW to assist future leaders and partners in effectively improving personalized learning at ASU and beyond.

My thesis is on the subject of mentoring. I researched the benefits and the styles of programs available and then used my research to create a survey to give to IDSA national members to see what they believe would make a good mentoring program. From there I tried to improve the current ASU IDSA mentoring program.

An investigation of the Caregiver Autism Residential E-health (CARE) system composed of low-cost, end-user deployable smart home technology and accompanying heuristics for rule-based models of human behavior has been evaluated for its potential as an empowering assistive technology with the capacity to enhance the well-being of people living with autism, their caregivers, and family members. It allows adults living with autism to create personalized smart home interventions that provide motivational support and is accompanied by guidelines for a safe and effective means of behavioral change. This investigation contributes a participatory co-design approach which addresses both the role of flexibility for the dynamic needs of the individual while offering strategies for dealing with the challenges of designing assistive smart home technologies for the needs of individuals across the wide range of autism spectrum disorders.

This honors thesis utilizes smart home components and concepts from Dr. Burleson's Game as Life, Life as Game (GaLLaG) systems. The thesis focuses on an automated lifestyle, where individuals utilize technology, such as door sensors, appliance and lamp modules, and system notifications, to assist in daily activities. The findings from our efforts to date indicate that after weeks of observations, there is no evidence that automated lifestyles create more productive and healthy lifestyles and lead to overall satisfaction in life; however, there are certain design principles that would assist future home automation applications.

Abstract Whether it is an abandoned New Year's Resolution or difficulty controlling procrastination, most can attest to failing to meet a goal. With ubiquitous computing, there is potential to support users' goals on a constant basis with pervasive technology elements such as integrated sensors and software. This study serves as a pilot for the behavior change component of a ubiquitous system, Game as Life, Life as Game (GALLAG), and how goal creation and motivation can be positively altered with the inclusion of a specific framework for users to follow. The study looked to find the efficacy of support tools (goal creation, reflection on past experience, and behavior change techniques and self-tracking) on creating a plan to reach a behavior goal, without the help of technology. Technology was ignored to focus on the effect of a framework for goal and plan generation. Over two weeks, there were 11 participants in the study; data collected was qualitative in the form of three video-recorded interview sessions, with quantitative data in the form of surveys. Participants were presented with support tools and tasked with picking a goal to work towards, as well as creating a plan to reach that goal. It was found that users struggled to create specific and detailed plans, even with the support tools provided, but this improved after the first meeting. Past experience was the most helpful support tool for creating better plans, however participants used this tool before being briefed on it. These results suggest a system should incorporate behavior change, self-tracking, and past experience earlier in the plan creation experience, allowing users a more concrete knowledge of these tools before beginning plan creation. By including these ideas in a framework, GALLAG can later implement that framework to better support users with a physical system. Keywords: behavior change, goal creation, motivation, self-efficacy, ubiquitous computing, pervasive game, human computer interaction

Affect signals what humans care about and is involved in rational decision-making and action selection. Many technologies may be improved by the capability to recognize human affect and to respond adaptively by appropriately modifying their operation. This capability, named affect-driven self-adaptation, benefits systems as diverse as learning environments, healthcare applications, and video games, and indeed has the potential to improve systems that interact intimately with users across all sectors of society. The main challenge is that existing approaches to advancing affect-driven self-adaptive systems typically limit their applicability by supporting the creation of one-of-a-kind systems with hard-wired affect recognition and self-adaptation capabilities, which are brittle, costly to change, and difficult to reuse. A solution to this limitation is to leverage the development of affect-driven self-adaptive systems with a manufacturing vision.

This dissertation demonstrates how using a software product line paradigm can jumpstart the development of affect-driven self-adaptive systems with that manufacturing vision. Applying a software product line approach to the affect-driven self-adaptive domain provides a comprehensive, flexible and reusable infrastructure of components with mechanisms to monitor a user’s affect and his/her contextual interaction with a system, to detect opportunities for improvements, to select a course of action, and to effect changes. It also provides a domain-specific architecture and well-documented process guidelines, which facilitate an understanding of the organization of affect-driven self-adaptive systems and their implementation by systematically customizing the infrastructure to effectively address the particular requirements of specific systems.

The software product line approach is evaluated by applying it in the development of learning environments and video games that demonstrate the significant potential of the solution, across diverse development scenarios and applications.

The key contributions of this work include extending self-adaptive system modeling, implementing a reusable infrastructure, and leveraging the use of patterns to exploit the commonalities between systems in the affect-driven self-adaptation domain.

Desirable outcomes such as health and wellbeing are tightly linked to people’s behaviors, thus inspiring research on technologies that support productively changing those behaviors. Many behavior change technologies are designed by Human-Computer Interaction experts, but this approach makes it difficult to personalize support to each user’s unique goals and needs. As an alternative to the provision of expert-developed pre-fabricated behavior change solutions, the present study aims to empower users’ self-experimentation for behavior change. To this end, two levels of supports were explored. First, the provision of interactive digital materials to support users’ creation of behavioral plans was developed. In the initial step, a tutorial for self-experimentation for behavior change that was fully scripted with images in succession was created. The tutorial focuses on facilitating users’ learning and applying behavior change techniques. Second, users were equipped with a tool to support their implementation of context-aware just-in-time interventions. This tool enables prototyping of sensor-based responsive systems for home environments, integrating simple sensors (two-state magnetic sensors, etc.) and media event components (wireless sound, etc.).

To evaluate the effectiveness of these two approaches, a between-subject trial comparing the approaches to a sleep education control was conducted with 27 participants over 7 weeks. Although results did not reveal significant difference in sleep quality improvement between the conditions, trends indicating greater effectiveness in the two treatment groups were observed. Analysis of the plans participants created and their revision performance also indicated that the two treatment groups developed more specific and personalized plans compared with the control group.