Matching Items (4)
Filtering by
- All Subjects: Computer Science
- Genre: Doctoral Dissertation
- Resource Type: Text
Description
Statistics is taught at every level of education, yet teachers often have to assume their students have no knowledge of statistics and start from scratch each time they set out to teach statistics. The motivation for this experimental study comes from interest in exploring educational applications of augmented reality (AR) delivered via mobile technology that could potentially provide rich, contextualized learning for understanding concepts related to statistics education. This study examined the effects of AR experiences for learning basic statistical concepts. Using a 3 x 2 research design, this study compared learning gains of 252 undergraduate and graduate students from a pre- and posttest given before and after interacting with one of three types of augmented reality experiences, a high AR experience (interacting with three dimensional images coupled with movement through a physical space), a low AR experience (interacting with three dimensional images without movement), or no AR experience (two dimensional images without movement). Two levels of collaboration (pairs and no pairs) were also included. Additionally, student perceptions toward collaboration opportunities and engagement were compared across the six treatment conditions. Other demographic information collected included the students' previous statistics experience, as well as their comfort level in using mobile devices. The moderating variables included prior knowledge (high, average, and low) as measured by the student's pretest score. Taking into account prior knowledge, students with low prior knowledge assigned to either high or low AR experience had statistically significant higher learning gains than those assigned to a no AR experience. On the other hand, the results showed no statistical significance between students assigned to work individually versus in pairs. Students assigned to both high and low AR experience perceived a statistically significant higher level of engagement than their no AR counterparts. Students with low prior knowledge benefited the most from the high AR condition in learning gains. Overall, the AR application did well for providing a hands-on experience working with statistical data. Further research on AR and its relationship to spatial cognition, situated learning, high order skill development, performance support, and other classroom applications for learning is still needed.
ContributorsConley, Quincy (Author) / Atkinson, Robert K (Thesis advisor) / Nguyen, Frank (Committee member) / Nelson, Brian C (Committee member) / Arizona State University (Publisher)
Created2013
Description
The video game graphics pipeline has traditionally rendered the scene using a polygonal approach. Advances in modern graphics hardware now allow the rendering of parametric methods. This thesis explores various smooth surface rendering methods that can be integrated into the video game graphics engine. Moving over to parametric or smooth surfaces from the polygonal domain has its share of issues and there is an inherent need to address various rendering bottlenecks that could hamper such a move. The game engine needs to choose an appropriate method based on in-game characteristics of the objects; character and animated objects need more sophisticated methods whereas static objects could use simpler techniques. Scaling the polygon count over various hardware platforms becomes an important factor. Much control is needed over the tessellation levels, either imposed by the hardware limitations or by the application, to be able to adaptively render the mesh without significant loss in performance. This thesis explores several methods that would help game engine developers in making correct design choices by optimally balancing the trade-offs while rendering the scene using smooth surfaces. It proposes a novel technique for adaptive tessellation of triangular meshes that vastly improves speed and tessellation count. It develops an approximate method for rendering Loop subdivision surfaces on tessellation enabled hardware. A taxonomy and evaluation of the methods is provided and a unified rendering system that provides automatic level of detail by switching between the methods is proposed.
ContributorsAmresh, Ashish (Author) / Farin, Gerlad (Thesis advisor) / Razdan, Anshuman (Thesis advisor) / Wonka, Peter (Committee member) / Hansford, Dianne (Committee member) / Arizona State University (Publisher)
Created2011
Description
In motor learning, real-time multi-modal feedback is a critical element in guided training. Serious games have been introduced as a platform for at-home motor training due to their highly interactive and multi-modal nature. This dissertation explores the design of a multimodal environment for at-home training in which an autonomous system observes and guides the user in the place of a live trainer, providing real-time assessment, feedback and difficulty adaptation as the subject masters a motor skill. After an in-depth review of the latest solutions in this field, this dissertation proposes a person-centric approach to the design of this environment, in contrast to the standard techniques implemented in related work, to address many of the limitations of these approaches. The unique advantages and restrictions of this approach are presented in the form of a case study in which a system entitled the "Autonomous Training Assistant" consisting of both hardware and software for guided at-home motor learning is designed and adapted for a specific individual and trainer.
In this work, the design of an autonomous motor learning environment is approached from three areas: motor assessment, multimodal feedback, and serious game design. For motor assessment, a 3-dimensional assessment framework is proposed which comprises of 2 spatial (posture, progression) and 1 temporal (pacing) domains of real-time motor assessment. For multimodal feedback, a rod-shaped device called the "Intelligent Stick" is combined with an audio-visual interface to provide feedback to the subject in three domains (audio, visual, haptic). Feedback domains are mapped to modalities and feedback is provided whenever the user's performance deviates from the ideal performance level by an adaptive threshold. Approaches for multi-modal integration and feedback fading are discussed. Finally, a novel approach for stealth adaptation in serious game design is presented. This approach allows serious games to incorporate motor tasks in a more natural way, facilitating self-assessment by the subject. An evaluation of three different stealth adaptation approaches are presented and evaluated using the flow-state ratio metric. The dissertation concludes with directions for future work in the integration of stealth adaptation techniques across the field of exergames.
In this work, the design of an autonomous motor learning environment is approached from three areas: motor assessment, multimodal feedback, and serious game design. For motor assessment, a 3-dimensional assessment framework is proposed which comprises of 2 spatial (posture, progression) and 1 temporal (pacing) domains of real-time motor assessment. For multimodal feedback, a rod-shaped device called the "Intelligent Stick" is combined with an audio-visual interface to provide feedback to the subject in three domains (audio, visual, haptic). Feedback domains are mapped to modalities and feedback is provided whenever the user's performance deviates from the ideal performance level by an adaptive threshold. Approaches for multi-modal integration and feedback fading are discussed. Finally, a novel approach for stealth adaptation in serious game design is presented. This approach allows serious games to incorporate motor tasks in a more natural way, facilitating self-assessment by the subject. An evaluation of three different stealth adaptation approaches are presented and evaluated using the flow-state ratio metric. The dissertation concludes with directions for future work in the integration of stealth adaptation techniques across the field of exergames.
ContributorsTadayon, Ramin (Author) / Panchanathan, Sethuraman (Thesis advisor) / McDaniel, Troy (Committee member) / Amresh, Ashish (Committee member) / Glenberg, Arthur (Committee member) / Li, Baoxin (Committee member) / Arizona State University (Publisher)
Created2017
Description
Currently, educational games are designed with the educational content as the primary factor driving the design of the game. While this may seem to be the optimal approach, this design paradigm causes multiple issues. For one, the games themselves are often not engaging as game design principles were put aside in favor of increasing the educational value of the game. The other issue is that the code base of the game is mostly or completely unusable for any other games as the game mechanics are too strongly connected to the educational content being taught. This means that the mechanics are impossible to reuse in future projects without major revisions, and starting over is often more time and cost efficient.
This thesis presents the Content Agnostic Game Engineering (CAGE) model for designing educational games. CAGE is a way to separate the educational content from the game mechanics without compromising the educational value of the game. This is done by designing mechanics that can have multiple educational contents layered on top of them which can be switched out at any time. CAGE allows games to be designed with a game design first approach which allows them to maintain higher engagement levels. In addition, since the mechanics are not tied to the educational content several different educational topics can reuse the same set of mechanics without requiring major revisions to the existing code.
Results show that CAGE greatly reduces the amount of code needed to make additional versions of educational games, and speeds up the development process. The CAGE model is also shown to not induce high levels of cognitive load, allowing for more in depth topic work than was attempted in this thesis. However, engagement was low and switching the active content does interrupt the game flow considerably. Altering the difficulty of the game in real time in response to the affective state of the player was not shown to increase engagement. Potential causes of the issues with CAGE games and potential fixes are discussed.
This thesis presents the Content Agnostic Game Engineering (CAGE) model for designing educational games. CAGE is a way to separate the educational content from the game mechanics without compromising the educational value of the game. This is done by designing mechanics that can have multiple educational contents layered on top of them which can be switched out at any time. CAGE allows games to be designed with a game design first approach which allows them to maintain higher engagement levels. In addition, since the mechanics are not tied to the educational content several different educational topics can reuse the same set of mechanics without requiring major revisions to the existing code.
Results show that CAGE greatly reduces the amount of code needed to make additional versions of educational games, and speeds up the development process. The CAGE model is also shown to not induce high levels of cognitive load, allowing for more in depth topic work than was attempted in this thesis. However, engagement was low and switching the active content does interrupt the game flow considerably. Altering the difficulty of the game in real time in response to the affective state of the player was not shown to increase engagement. Potential causes of the issues with CAGE games and potential fixes are discussed.
ContributorsBaron, Tyler John (Author) / Amresh, Ashish (Thesis advisor) / Nelson, Brian C (Committee member) / Niemczyk, Mary (Committee member) / Arizona State University (Publisher)
Created2017