The purpose of this thesis is to understand peer-to-peer study habits at Arizona State University, and provide recommendations for improving these habits through online integration. This was done by researching current peer-to-peer collaboration literature, and analyzing online integration efforts. Interviews of Arizona State University students were carried out in order to discover specific insights on study patterns at this university. The scope of this research study was further limited to freshman and sophomore engineering, mathematics, and science majors in order to mitigate the impacts of external factors. The background research and study illuminated various flaws in existing peer-to-peer collaboration tools and methods. These weaknesses were then used to design two online tools that would be incorporated into a student resource dashboard. The first tool, called "Ask a Peer", provides a question and answer forum for students. This tool differs from existing products because it provides a mobile platform for students to receive reputable and immediate responses from their classmates. The second tool, "Study Buddy Finder", can be used by students to form study partnerships. This tool is beneficial because it displays information that is essential to students deciding to work together. The thesis provides detailed designs for both modules, and provides the foundation for implementation.

This project explores the development of a Ruby on Rails web application to help supervisors manage the tutoring centers at Arizona State University. Each phase of the project's development is examined in detail, including the requirements analysis, system design, system implementation, test strategy, and deployment strategy. The application also includes a REST API for providing the tutoring center data to external applications. These external applications can then present the data, along with their own unique features, to students. This should allow students to have an easier time locating the academic resources they need.

Academic resources at Arizona State University are vast and allow a student to maintain success through his/her university attendance. The distribution and management of these systems is arduous and manually done. A software solution for the distribution of academic resource information is a Dashboard system that utilizes information from the university, and is expandable. A solution for the management of academic centers utilizes a scheduling algorithm that allows quick scheduling of resources. Overall these solutions provide easier workflows than the current systems allow.

Digital technologies are quickly being combined with and replacing teacher curriculums and student resource tools. This is particularly true with advances in digital textbooks as it provides a medium for opportunity and growth in the nature of the textbook as it pertains to students in the classroom. Although great strides have been taken in intelligent tutoring systems personalized toward a student's needs there seems to be an overall disconnect between student needs in the classroom in not utilizing or adopting these technologies. In this paper I provide both conflicting and comparable needs of teachers and students surrounding the textbook to reveal the costs and benefits associated with technology adoption. Through 4 teacher interviews and 4 participatory prototyping sessions I found that students and teachers desire the following elements in technology: 1) Collaboration 2) Synchronicity 3) Adaptive 4) Automation. I discuss the implications of implementing such features and how they could be applied in integrated Q&A system to encourage collaborative learning.

Technological advances in the past decade alone are calling for modifications to the usability of various devices. Physical human interaction is becoming a popular method to communicate with user interfaces. This ranges from touch-based devices such as an iPad or tablet to free space gesture systems such as the Microsoft Kinect. With the rise in popularity of these types of devices comes the increased amount of them in public areas. Public areas frequently use walk-up-and-use displays, which give many people the opportunity to interact with them. Walk-up-and-use displays are intended to be simple enough that any individual, regardless of experience using similar technology, will be able to successfully maneuver the system. While this should be easy enough for the people using it, it is a more complicated task for the designers who are in charge of creating an interface simple enough to use while also accomplishing the tasks it was built to complete. A serious issue that I'll be addressing in this thesis is how a system designer knows what gestures to program the interface to successfully respond to. Gesture elicitation is one widely used method to discover common, intuitive, gestures that can be used with public walk-up-and-use interactive displays. In this paper, I present a study to extract common intuitive gestures for various tasks, an analysis of the responses, and suggestions for future designs of interactive, public, walk-up-and use interactions.

Along with the number of technologies that have been introduced over a few years ago, gesture-based human-computer interactions are becoming the new phase in encompassing the creativity and abilities for users to communicate and interact with devices. Because of how the nature of defining free-space gestures influence user's preference and the length of usability of gesture-driven devices, defined low-stress and intuitive gestures for users to interact with gesture recognition systems are necessary to consider. To measure stress, a Galvanic Skin Response instrument was used as a primary indicator, which provided evidence of the relationship between stress and intuitive gestures, as well as user preferences towards certain tasks and gestures during performance. Fifteen participants engaged in creating and performing their own gestures for specified tasks that would be required during the use of free-space gesture-driven devices. The tasks include "activation of the display," scroll, page, selection, undo, and "return to main menu." They were also asked to repeat their gestures for around ten seconds each, which would give them time and further insight of how their gestures would be appropriate or not for them and any given task. Surveys were given at different time to the users: one after they had defined their gestures and another after they had repeated their gestures. In the surveys, they ranked their gestures based on comfort, intuition, and the ease of communication. Out of those user-ranked gestures, health-efficient gestures, given that the participants' rankings were based on comfort and intuition, were chosen in regards to the highest ranked gestures.

A study was undertaken to examine and test the effectiveness of a self-experimentation model, guided by a mobile app called PACO, in helping college students improve behaviors associated with sleep. Thirteen participants were enrolled in this study and their nightly sleep quality and sleep duration were measured via PACO as they underwent three conditions: a baseline non-intervention phase, an expert-developed intervention phase, in which pre-made intervention examples were provided and used in PACO, and a self-experimentation phase, during which users were invited to develop their own sleep-behavior interventions using PACO. The participants were randomly placed into three groups, and the points of transition between phases was staggered across five weeks according to a multiple baseline design. The goal and hypothesis was to determine if sleep duration and sleep quality (sleep satisfaction) were improved in the final self-experimentation phase compared to the expert-developed experimentation phase and baseline phase, as well as in the expert-developed experimentation phase compared to the baseline phase. The results show little change, and nearly no improvement in the outcome measures between phases, leaving us unable to support the hypothesis. However, the existence of several limitations considered in retrospect, such as the small sample size, the short study time period, and technical difficulties with the PACO application means that no concrete conclusions should be made regarding the effectiveness of the self-experimentation model, nor the usability of PACO. Additional research should be made toward user motivation and modes of teaching the underlying behavioral science principles to casual users to increase effectiveness.

Electronic books or eBooks have the potential to revolutionize the way humans read and learn. eBooks offer many advantages such as simplicity, ease of use, eco-friendliness, and portability. The advancement of technology has introduced many forms of multimedia objects into eBooks, which may help people learn from them. To help the readers understand and comprehend a concept that is put forward by the author of an eBook, there is ongoing research involving the use of augmented reality (AR) in education. This study explores how AR and three-dimensional interactive models are integrated into eBooks to help the readers comprehend the content quickly and swiftly. It compares the reading activities of people when they experience these two visual representations within an eBook.

This study required participants to interact with some instructional material presented on an eBook and complete a learning measure. While interacting with the eBook, participants were equipped with a set of physiological devices, namely an ABM EEG headset and eye tracker during the experiment to collect biometric data that could be used to objectively measure their user experience. Fifty college students participated in this study. The data collected from each of the participants was used to analyze the reading activities of people by performing an Independent Samples t-test.

There is a demanding need to empower students from kindergarten through high school to learn computer science and be equipped with the computational thinking skills that they need in today's technology driven world. However, introducing computer programming to students can be challenging, especially for those who aren't familiar with the nuances of code. Several popular tools are used in curriculum for K-12 students which utilize interactive and visualization approaches to engage young kids in learning computational concepts. Possibilities of using Augmented Reality (AR) in teaching programming to novices are explored in this work.

In this thesis Ogmented, an AR application is designed which includes interactive learning material that covers a range of fundamental Object-Oriented Programming (OOP) concepts. This work aims to exploit the idea to learn abstract concepts via AR by capitalizing the strength of visual-aided and interactive elements. A user study with a group of elementary school students is conducted. It explored how students operated the AR application with the interactive elements and how they wrote codes to solve programming problems. It was observed that students who followed instructions while taking tutorials were successfully able to write fragments of codes in exercise modules. Irrespective of their knowledge about programming, majority of students were able to write executable code snippets for concepts they were taught with use of Ogmented. This shares an initial insight on using AR in classroom to teach abstract programming concepts.

The increasing volume and complexity of software systems and the growing demand of programming skills calls for efficient information retrieval techniques from source code documents. Programming related information seeking is often challenging for users facing constraints in knowledge and experience. Source code documents contain multi-faceted semi-structured text, having different levels of semantic information like syntax, blueprints, interfaces, flow graphs, dependencies and design patterns. Matching user queries optimally across these levels is a major challenge for information retrieval systems. Code recommendations can help information seeking and retrieval by pro-actively sampling similar examples based on the users context. These recommendations can be beneficial in improving learning via examples or improving code quality by sampling best practices or alternative implementations.

In this thesis, an attempt is made to help programming related information seeking processes via pro-active code recommendations, and information retrieval processes by extracting structural-semantic information from source code. I present CodeReco, a system that recommends semantically similar Java method samples. Conventional code recommendations found in integrated development environments are primarily driven by syntactical compliance and auto-completion, whereas CodeReco is driven by similarities in use of language and structure-semantics. Methods are transformed to a vector space model and a novel metric of similarity is designed. Features in this vector space are categorized as belonging to types signature, structure, concept and language for user personalization.

Offline tests show that CodeReco recommendations cover broader programming concepts and have higher conceptual similarity with their samples. A user study was conducted where users rated Java method recommendations that helped them icomplete two programming problems. 61.5% users were positive that real time method recommendations are helpful, and 50% reported this would reduce time spent in web searches. The empirical utility of CodeReco’s similarity metric on those problems was compared with a purely language based similarity metric (baseline). Baseline received higher ratings from novices, arguably due to lack of structure-semantics in their samples while seeking recommendations.