For this master's thesis, an open learner model is integrated with Quinn, a teachable robotic agent developed at Arizona State University. This system is represented as a feedback system, which aims to improve a student’s understanding of a subject. It also helps to understand the effect of the learner model when it is represented by performance of the teachable agent. The feedback system represents performance of the teachable agent, and not of a student. Data in the feedback system is thus updated according to a student's understanding of the subject. This provides students an opportunity to enhance their understanding of a subject by analyzing their performance. To test the effectiveness of the feedback system, student understanding in two different conditions is analyzed. In the first condition a feedback report is not provided to the students, while in the second condition the feedback report is provided in the form of the agent’s performance.

Online learning communities have changed the way users learn due to the technological affordances web 2.0 has offered. This shift has produced different kinds of learning communities like massive open online courses (MOOCs), learning management systems (LMS) and question and answer based learning communities. Question and answer based communities are an important part of social information seeking. Thousands of users participate in question and answer based communities on the web like Stack Overflow, Yahoo Answers and Wiki Answers. Research in user participation in different online communities identifies a universal phenomenon that a few users are responsible for answering a high percentage of questions and thus promoting the sustenance of a learning community. This principle implies two major categories of user participation, people who ask questions and those who answer questions. In this research, I try to look beyond this traditional view, identify multiple subtler user participation categories. Identification of multiple categories of users helps to provide specific support by treating each of these groups of users separately, in order to maintain the sustenance of the community.

In this thesis, participation behavior of users in an open and learning based question and answer community called OpenStudy has been analyzed. Initially, users were grouped into different categories based on the number of questions they have answered like non participators, sample participators, low, medium and high participators. In further steps, users were compared across several features which reflect temporal, content and question/thread specific dimensions of user participation including those suggestive of learning in OpenStudy.

The goal of this thesis is to analyze user participation in three steps:

a. Inter group participation analysis: compare pre assumed user groups across the participation features extracted from OpenStudy data.

b. Intra group participation analysis: Identify sub groups in each category and examine how participation differs within each group with help of unsupervised learning techniques.

c. With these grouping insights, suggest what interventions might support the categories of users for the benefit of users and community.

This thesis presents new insights into participation because of the broad range of

features extracted and their significance in understanding the behavior of users in this learning community.

Embedded assessment constantly updates a model of the student as the student works on instructional tasks. Accurate embedded assessment allows students, instructors and instructional systems to make informed decisions without requiring the student to stop instruction and take a test. This thesis describes the development and comparison of several student models for Dragoon, an intelligent tutoring system. All the models were instances of Bayesian Knowledge Tracing, a standard method. Several methods of parameterization and calibration were explored using two recently developed toolkits, FAST and BNT-SM that replaces constant-valued parameters with logistic regressions. The evaluation was done by calculating the fit of the models to data from human subjects and by assessing the accuracy of their assessment of simulated students. The student models created using node properties as subskills were superior to coarse-grained, skill-only models. Adding this extra level of representation to emission parameters was superior to adding it to transmission parameters. Adding difficulty parameters did not improve fit, contrary to standard practice in psychometrics.

Online programming communities are widely used by programmers for troubleshooting or various problem solving tasks. Large and ever increasing volume of posts on these communities demands more efforts to read and comprehend thus making it harder to find relevant information. In my thesis; I designed and studied an alternate approach by using interactive network visualization to represent relevant search results for online programming discussion forums.

I conducted user study to evaluate the effectiveness of this approach. Results show that users were able to identify relevant information more precisely via visual interface as compared to traditional list based approach. Network visualization demonstrated effective search-result navigation support to facilitate user’s tasks and improved query quality for successive queries. Subjective evaluation also showed that visualizing search results conveys more semantic information in efficient manner and makes searching more effective.

As people age, the desire to grow old independently and in place becomes larger and takes greater importance in their lives. Successful aging involves the physical, mental and social well-being of an individual. To enable successful aging of older adults, it is necessary for them to perform both activities of daily living (ADL) and instrumental activities of daily living (IADL). Embedded assessment has made it possible to assess an individual's functional ability in-place, however the success of any technology depends largely on the user than the technology itself. Previous researches in in-situ functional assessment systems have heavily focused on the technology rather than on the user. This dissertation takes a user-centric approach to this problem by trying to identify the design and technical challenges of deploying and using a functional assessment system in the real world.

To investigate this line of research, a case study was conducted with 4 older adults in their homes, interviews were conducted with 8 caregivers and a controlled lab experiment was conducted with 8 young healthy adults at ASU, to test the sensors. This methodology provides a significant opportunity to advance the scientific field by expanding the present focus on IADL task performance to an integrated assessment of ADL and IADL task performance. Doing so would not only be more effective in identifying functional decline but could also provide a more comprehensive assessment of individuals' functional abilities with independence and also providing the caregivers with much needed respite.

The controlled lab study tested the sensors embedded into daily objects and found them to be reliable, and efficient. Short term exploratory case studies with healthy older adults revealed the challenges associated with design and technical aspects of the current system, while inductive analysis performed on interviews with caregivers helped to generate central themes on which future functional assessment systems need to be designed and built. The key central themes were a) focus on design / user experience, b) consider user's characteristics, personality, behavior and functional ability, c) provide support for independence, and d) adapt to individual user's needs.

On Android, existing security procedures require apps to request permissions for access to sensitive resources.

Only when the user approves the requested permissions will the app be installed.

However, permissions are an incomplete security mechanism.

In addition to a user's limited understanding of permissions, the mechanism does not account for the possibility that different permissions used together have the ability to be more dangerous than any single permission alone.

Even if users did understand the nature of an app's requested permissions, this mechanism is still not enough to guarantee that a user's information is protected.

Applications can potentially send or receive sensitive information from other applications without the required permissions by using intents.

In other words, applications can potentially collaborate in ways unforeseen by the user, even if the user understands the permissions of each app independently.

In this thesis, we present several graph-based approaches to address these issues.

We determine the permissions of an app and generate scores based on our assigned value of certain resources.

We analyze these scores overall, as well as in the context of the app's category as determined by Google Play.

We show that these scores can be used to identify overzealous apps, as well as apps that do not properly fit within their category.

We analyze potential interactions between different applications using intents, and identify several promiscuous apps with low permission scores, showing that permissions alone are not sufficient to evaluate the security risks of an app.

Our analyses can form the basis of a system to assist users in identifying apps that can potentially compromise user privacy.

Internet browsers are today capable of warning internet users of a potential phishing attack. Browsers identify these websites by referring to blacklists of reported phishing websites maintained by trusted organizations like Google, Phishtank etc. On identifying a Unified Resource Locator (URL) requested by a user as a reported phishing URL, browsers like Mozilla Firefox and Google Chrome display an 'active' warning message in an attempt to stop the user from making a potentially dangerous decision of visiting the website and sharing confidential information like username-password, credit card information, social security number etc.

However, these warnings are not always successful at safeguarding the user from a phishing attack. On several occasions, users ignore these warnings and 'click through' them, eventually landing at the potentially dangerous website and giving away confidential information. Failure to understand the warning, failure to differentiate different types of browser warnings, diminishing trust on browser warnings due to repeated encounter are some of the reasons that make users ignore these warnings. It is important to address these factors in order to eventually improve a user’s reaction to these warnings.

In this thesis, I propose a novel design to improve the effectiveness and reliability of phishing warning messages. This design utilizes the name of the target website that a fake website is mimicking, to display a simple, easy to understand and interactive warning message with the primary objective of keeping the user away from a potentially spoof website.

Study in user experience design states that there is a considerable gap between users and designers. Collaborative design and empathetic design methods attempt to make a strong relationship between these two. In participatory design activities, projective `make tools' are required for users to show their thoughts. This research is designed to apply an empathetic way of using `make tools' in user experience design for websites clients, users, and designers.

A magnetic wireframe tool has been used as a `make tool', and a sample project has been defined in order to see how the tool can create empathy among stakeholders. In this study fourth year graphic design students at Arizona State University (ASU), USA, are participating as users, faculty members have the role of clients, and Forty, Inc., a design firm in the Phoenix area, is the design team for the study. All of these three groups are cooperating on re-designing the homepage of the Design School in Herberger Institute for Design and Art (HIDA) at ASU.

A method for applying the magnetic tool was designed and used for each group. Results of users and clients' activities were shared with the design team, and they designed a final prototype for the wireframe of the sample project. Observation and interviews were done to see how participants work with the tool. Also, follow up questionnaires were used in order to evaluate all groups' experiences with the magnetic wireframe. Lastly, as a part of questionnaires, a sentence completion method has been used in order to collect the participants' exact thoughts about the magnetic tool.

Observations and results of data analysis in this research show that the tool was a helpful `make tool' for users and clients. They could talk about their ideas and also designers could learn more about people. The entire series of activities caused an empathetic relationship among stakeholders of the sample project. This method of using `make tools' in user experience design for web sites can be useful for collaborative UX design activities and further research in user experience design with empathy.

Research in the learning sciences suggests that students learn better by collaborating with their peers than learning individually. Students working together as a group tend to generate new ideas more frequently and exhibit a higher level of reasoning. In this internet age with the advent of massive open online courses (MOOCs), students across the world are able to access and learn material remotely. This creates a need for tools that support distant or remote collaboration. In order to build such tools we need to understand the basic elements of remote collaboration and how it differs from traditional face-to-face collaboration.

The main goal of this thesis is to explore how spoken dialogue varies in face-to-face and remote collaborative learning settings. Speech data is collected from student participants solving mathematical problems collaboratively on a tablet. Spoken dialogue is analyzed based on conversational and acoustic features in both the settings. Looking for collaborative differences of transactivity and dialogue initiative, both settings are compared in detail using machine learning classification techniques based on acoustic and prosodic features of speech. Transactivity is defined as a joint construction of knowledge by peers. The main contributions of this thesis are: a speech corpus to analyze spoken dialogue in face-to-face and remote settings and an empirical analysis of conversation, collaboration, and speech prosody in both the settings. The results from the experiments show that amount of overlap is lower in remote dialogue than in the face-to-face setting. There is a significant difference in transactivity among strangers. My research benefits the computer-supported collaborative learning community by providing an analysis that can be used to build more efficient tools for supporting remote collaborative learning.

This thesis is an initial test of the hypothesis that superficial measures suffice for measuring collaboration among pairs of students solving complex math problems, where the degree of collaboration is categorized at a high level. Data were collected

in the form of logs from students' tablets and the vocal interaction between pairs of students. Thousands of different features were defined, and then extracted computationally from the audio and log data. Human coders used richer data (several video streams) and a thorough understand of the tasks to code episodes as

collaborative, cooperative or asymmetric contribution. Machine learning was used to induce a detector, based on random forests, that outputs one of these three codes for an episode given only a characterization of the episode in terms of superficial features. An overall accuracy of 92.00% (kappa = 0.82) was obtained when

comparing the detector's codes to the humans' codes. However, due irregularities in running the study (e.g., the tablet software kept crashing), these results should be viewed as preliminary.