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- Genre: Masters Thesis
- Creators: Arizona State University
Description
The wide adoption and continued advancement of information and communications technologies (ICT) have made it easier than ever for individuals and groups to stay connected over long distances. These advances have greatly contributed in dramatically changing the dynamics of the modern day workplace to the point where it is now commonplace to see large, distributed multidisciplinary teams working together on a daily basis. However, in this environment, motivating, understanding, and valuing the diverse contributions of individual workers in collaborative enterprises becomes challenging. To address these issues, this thesis presents the goals, design, and implementation of Taskville, a distributed workplace game played by teams on large, public displays. Taskville uses a city building metaphor to represent the completion of individual and group tasks within an organization. Promising results from two usability studies and two longitudinal studies at a multidisciplinary school demonstrate that Taskville supports personal reflection and improves team awareness through an engaging workplace activity.
ContributorsNikkila, Shawn (Author) / Sundaram, Hari (Thesis advisor) / Byrne, Daragh (Committee member) / Davulcu, Hasan (Committee member) / Olson, Loren (Committee member) / Arizona State University (Publisher)
Created2013
Description
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.
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.
ContributorsEslamifar, Ali (Author) / Heywood, William (Thesis advisor) / Walker, Erin (Committee member) / Takamura, John (Committee member) / Arizona State University (Publisher)
Created2014
Description
Although there are many forms of organization on the Web, one of the most prominent ways to organize web content and websites are tags. Tags are keywords or terms that are assigned to a specific piece of content in order to help users understand the common relationships between pieces of content. Tags can either be assigned by an algorithm, the author, or the community. These tags can also be organized into tag clouds, which are visual representations of the structure and organization contained implicitly within these tags. Importantly, little is known on how we use these different tagging structures to understand the content and structure of a given site. This project examines 2 different characteristics of tagging structures: font size and spatial orientation. In order to examine how these different characteristics might interact with individual differences in attentional control, a measure of working memory capacity (WMC) was included. The results showed that spatial relationships affect how well users understand the structure of a website. WMC was not shown to have any significant effect; neither was varying the font size. These results should better inform how tags and tag clouds are used on the Web, and also provide an estimation of what properties to include when designing and implementing a tag cloud on a website.
ContributorsBanas, Steven (Author) / Sanchez, Christopher A (Thesis advisor) / Branaghan, Russell (Committee member) / Cooke, Nancy J. (Committee member) / Arizona State University (Publisher)
Created2011
Description
The Game As Life - Life As Game (GALLAG) project investigates how people might change their lives if they think of and/or experience their life as a game. The GALLAG system aims to help people reach their personal goals through the use of context-aware computing, and tailored games and applications. To accomplish this, the GALLAG system uses a combination of sensing technologies, remote audio/video feedback, mobile devices and an application programming interface (API) to empower users to create their own context-aware applications. However, the API requires programming through source code, a task that is too complicated and abstract for many users. This thesis presents GALLAG Strip, a novel approach to programming sensor-based context-aware applications that combines the Programming With Demonstration technique and a mobile device to enable users to experience their applications as they program them. GALLAG Strip lets users create sensor-based context-aware applications in an intuitive and appealing way without the need of computer programming skills; instead, they program their applications by physically demonstrating their envisioned interactions within a space using the same interface that they will later use to interact with the system, that is, using GALLAG-compatible sensors and mobile devices. GALLAG Strip was evaluated through a study with end users in a real world setting, measuring their ability to program simple and complex applications accurately and in a timely manner. The evaluation also comprises a benchmark with expert GALLAG system programmers in creating the same applications. Data and feedback collected from the study show that GALLAG Strip successfully allows users to create sensor-based context-aware applications easily and accurately without the need of prior programming skills currently required by the GALLAG system and enables them to create almost all of their envisioned applications.
ContributorsGarduno Massieu, Luis (Author) / Burleson, Winslow (Thesis advisor) / Hekler, Eric (Committee member) / Gupta, Sandeep (Committee member) / Arizona State University (Publisher)
Created2012
Design and development of an immersive virtual reality team trainer for advance cardiac life support
Description
Technology in the modern day has ensured that learning of skills and behavior may be both widely disseminated and cheaply available. An example of this is the concept of virtual reality (VR) training. Virtual Reality training ensures that learning can be provided often, in a safe simulated setting, and it may be delivered in a manner that makes it engaging while negating the need to purchase special equipment. This thesis presents a case study in the form of a time critical, team based medical scenario known as Advanced Cardiac Life Support (ACLS). A framework and methodology associated with the design of a VR trainer for ACLS is detailed. In addition, in order to potentially provide an engaging experience, the simulator was designed to incorporate immersive elements and a multimodal interface (haptic, visual, and auditory). A study was conducted to test two primary hypotheses namely: a meaningful transfer of skill is achieved from virtual reality training to real world mock codes and the presence of immersive components in virtual reality leads to an increase in the performance gained. The participant pool consisted of 54 clinicians divided into 9 teams of 6 members each. The teams were categorized into three treatment groups: immersive VR (3 teams), minimally immersive VR (3 teams), and control (3 teams). The study was conducted in 4 phases from a real world mock code pretest to assess baselines to a 30 minute VR training session culminating in a final mock code to assess the performance change from the baseline. The minimally immersive team was treated as control for the immersive components. The teams were graded, in both VR and mock code sessions, using the evaluation metric used in real world mock codes. The study revealed that the immersive VR groups saw greater performance gain from pretest to posttest than the minimally immersive and control groups in case of the VFib/VTach scenario (~20% to ~5%). Also the immersive VR groups had a greater performance gain than the minimally immersive groups from the first to the final session of VFib/VTach (29% to -13%) and PEA (27% to 15%).
ContributorsVankipuram, Akshay (Author) / Li, Baoxin (Thesis advisor) / Burleson, Winslow (Committee member) / Kahol, Kanav (Committee member) / Arizona State University (Publisher)
Created2012
Description
Brain Computer Interfaces are becoming the next generation controllers not only in the medical devices for disabled individuals but also in the gaming and entertainment industries. In order to build an effective Brain Computer Interface, which accurately translates the user thoughts into machine commands, it is important to have robust and fail proof signal processing and machine learning modules which operate on the raw EEG signals and estimate the current thought of the user.
In this thesis, several techniques used to perform EEG signal pre-processing, feature extraction and signal classification have been discussed, implemented, validated and verified; efficient supervised machine learning models, for the EEG motor imagery signal classification are identified. To further improve the performance of system unsupervised feature learning techniques have been investigated by pre-training the Deep Learning models. Use of pre-training stacked autoencoders have been proposed to solve the problems caused by random initialization of weights in neural networks.
Motor Imagery (imaginary hand and leg movements) signals are acquire using the Emotiv EEG headset. Different kinds of features like mean signal, band powers, RMS of the signal have been extracted and supplied to the machine learning (ML) stage, wherein, several ML techniques like LDA, KNN, SVM, Logistic regression and Neural Networks are applied and validated. During the validation phase the performances of various techniques are compared and some important observations are reported. Further, deep Learning techniques like autoencoding have been used to perform unsupervised feature learning. The reliability of the features is analyzed by performing classification by using the ML techniques mentioned earlier. The performance of the neural networks has been further improved by pre-training the network in an unsupervised fashion using stacked autoencoders and supplying the stacked autoencoders’ network parameters as initial parameters to the neural network. All the findings in this research, during each phase (pre-processing, feature extraction, classification) are directly relevant and can be used by the BCI research community for building motor imagery based BCI applications.
Additionally, this thesis attempts to develop, test, and compare the performance of an alternative method for classifying human driving behavior. This thesis proposes the use of driver affective states to know the driving behavior. The purpose of this part of the thesis was to classify the EEG data collected from several subjects while driving simulated vehicle and compare the classification results with those obtained by classifying the driving behavior using vehicle parameters collected simultaneously from all the subjects. The objective here is to see if the drivers’ mental state is reflected in his driving behavior.
In this thesis, several techniques used to perform EEG signal pre-processing, feature extraction and signal classification have been discussed, implemented, validated and verified; efficient supervised machine learning models, for the EEG motor imagery signal classification are identified. To further improve the performance of system unsupervised feature learning techniques have been investigated by pre-training the Deep Learning models. Use of pre-training stacked autoencoders have been proposed to solve the problems caused by random initialization of weights in neural networks.
Motor Imagery (imaginary hand and leg movements) signals are acquire using the Emotiv EEG headset. Different kinds of features like mean signal, band powers, RMS of the signal have been extracted and supplied to the machine learning (ML) stage, wherein, several ML techniques like LDA, KNN, SVM, Logistic regression and Neural Networks are applied and validated. During the validation phase the performances of various techniques are compared and some important observations are reported. Further, deep Learning techniques like autoencoding have been used to perform unsupervised feature learning. The reliability of the features is analyzed by performing classification by using the ML techniques mentioned earlier. The performance of the neural networks has been further improved by pre-training the network in an unsupervised fashion using stacked autoencoders and supplying the stacked autoencoders’ network parameters as initial parameters to the neural network. All the findings in this research, during each phase (pre-processing, feature extraction, classification) are directly relevant and can be used by the BCI research community for building motor imagery based BCI applications.
Additionally, this thesis attempts to develop, test, and compare the performance of an alternative method for classifying human driving behavior. This thesis proposes the use of driver affective states to know the driving behavior. The purpose of this part of the thesis was to classify the EEG data collected from several subjects while driving simulated vehicle and compare the classification results with those obtained by classifying the driving behavior using vehicle parameters collected simultaneously from all the subjects. The objective here is to see if the drivers’ mental state is reflected in his driving behavior.
ContributorsManchala, Vamsi Krishna (Author) / Redkar, Sangram (Thesis advisor) / Rogers, Bradley (Committee member) / Sugar, Thomas (Committee member) / Arizona State University (Publisher)
Created2015
Description
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.
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.
ContributorsMehta, Vishal Vimal (Author) / Hsiao, Ihan (Thesis advisor) / Walker, Erin (Committee member) / Sarwat, Mohamed (Committee member) / Arizona State University (Publisher)
Created2015
Description
The International Standards Organization (ISO) documentation utilizes Fitts’ law to determine the usability of traditional input devices like mouse and touchscreens for one- or two-dimensional operations. To test the hypothesis that Fitts’ Law can be applied to hand/air gesture based computing inputs, Fitts’ multi-directional target acquisition task is applied to three gesture based input devices that utilize different technologies and two baseline devices, mouse and touchscreen. Three target distances and three target sizes were tested six times in a randomized order with a randomized order of the five input technologies. A total of 81 participants’ data were collected for the within subjects design study. Participants were instructed to perform the task as quickly and accurately as possible according to traditional Fitts’ testing procedures. Movement time, error rate, and throughput for each input technology were calculated.
Additionally, no standards exist for equating user experience with Fitts’ measures such as movement time, throughput, and error count. To test the hypothesis that a user’s experience can be predicted using Fitts’ measures of movement time, throughput and error count, an ease of use rating using a 5-point scale for each input type was collected from each participant. The calculated Mean Opinion Scores (MOS) were regressed on Fitts’ measures of movement time, throughput, and error count to understand the extent to which they can predict a user’s subjective rating.
Additionally, no standards exist for equating user experience with Fitts’ measures such as movement time, throughput, and error count. To test the hypothesis that a user’s experience can be predicted using Fitts’ measures of movement time, throughput and error count, an ease of use rating using a 5-point scale for each input type was collected from each participant. The calculated Mean Opinion Scores (MOS) were regressed on Fitts’ measures of movement time, throughput, and error count to understand the extent to which they can predict a user’s subjective rating.
ContributorsBurno, Rachael A (Author) / Wu, Bing (Thesis advisor) / Cooke, Nancy J. (Committee member) / Branaghan, Russell (Committee member) / Arizona State University (Publisher)
Created2015
Description
Highly automated vehicles require drivers to remain aware enough to takeover
during critical events. Driver distraction is a key factor that prevents drivers from reacting
adequately, and thus there is need for an alert to help drivers regain situational awareness
and be able to act quickly and successfully should a critical event arise. This study
examines two aspects of alerts that could help facilitate driver takeover: mode (auditory
and tactile) and direction (towards and away). Auditory alerts appear to be somewhat
more effective than tactile alerts, though both modes produce significantly faster reaction
times than no alert. Alerts moving towards the driver also appear to be more effective
than alerts moving away from the driver. Future research should examine how
multimodal alerts differ from single mode, and see if higher fidelity alerts influence
takeover times.
during critical events. Driver distraction is a key factor that prevents drivers from reacting
adequately, and thus there is need for an alert to help drivers regain situational awareness
and be able to act quickly and successfully should a critical event arise. This study
examines two aspects of alerts that could help facilitate driver takeover: mode (auditory
and tactile) and direction (towards and away). Auditory alerts appear to be somewhat
more effective than tactile alerts, though both modes produce significantly faster reaction
times than no alert. Alerts moving towards the driver also appear to be more effective
than alerts moving away from the driver. Future research should examine how
multimodal alerts differ from single mode, and see if higher fidelity alerts influence
takeover times.
ContributorsBrogdon, Michael A (Author) / Gray, Robert (Thesis advisor) / Branaghan, Russell (Committee member) / Chiou, Erin (Committee member) / Arizona State University (Publisher)
Created2018
Description
Reading partners’ actions correctly is essential for successful coordination, but interpretation does not always reflect reality. Attribution biases, such as self-serving and correspondence biases, lead people to misinterpret their partners’ actions and falsely assign blame after an unexpected event. These biases thus further influence people’s trust in their partners, including machine partners. The increasing capabilities and complexity of machines allow them to work physically with humans. However, their improvements may interfere with the accuracy for people to calibrate trust in machines and their capabilities, which requires an understanding of attribution biases’ effect on human-machine coordination. Specifically, the current thesis explores how the development of trust in a partner is influenced by attribution biases and people’s assignment of blame for a negative outcome. This study can also suggest how a machine partner should be designed to react to environmental disturbances and report the appropriate level of information about external conditions.
ContributorsHsiung, Chi-Ping (M.S.) (Author) / Chiou, Erin (Thesis advisor) / Cooke, Nancy J. (Thesis advisor) / Zhang, Wenlong (Committee member) / Arizona State University (Publisher)
Created2019