Matching Items (11)
Filtering by
- All Subjects: psychology
- All Subjects: Industrial Engineering
- Member of: Theses and Dissertations
- Member of: Barrett, The Honors College Thesis/Creative Project Collection
Description
The gameplay experience can be understood as an interaction between player and game design characteristics. A greater understanding of these characteristics can be gained through empirical means. Subsequently, an enhanced knowledge of these characteristics should enable the creation of games that effectively generate desirable experiences for players. The purpose of this study was to investigate the relationships between gameplay enjoyment and the individual characteristics of gaming goal orientations, game usage, and gender. A total of 301 participants were surveyed and the data were analyzed using Structural Equation Modeling (SEM). This led to an expanded Gameplay Enjoyment Model (GEM) with 41 game features, an overarching Enjoyment factor, and 9 specific components, including Challenge, Companionship, Discovery, Fantasy, Fidelity, Identity, Multiplayer, Recognition, and Strategy. Furthermore, the 3x2 educational goal orientation framework was successfully applied to a gaming context. The resulting 3x2 Gaming Goal Orientations (GGO) model consists of 18 statements that describe players' motivations for gaming, which are distributed across the six dimensions of Task-Approach, Task-Avoidance, Self-Approach, Self-Avoidance, Other-Approach, and Other-Avoidance. Lastly, players' individual characteristics were used to predict gameplay enjoyment, which resulted in the formation of the GEM-Individual Characteristics (GEM-IC) model. In GEM-IC, the six GGO dimensions were the strongest predictors. Meanwhile, game usage variables like multiplayer, genre, and platform preference, were minimal to moderate predictors. Although commonly appearing in games research, gender and game time commitment variables failed to predict enjoyment. The results of this study enable important work to be conducted involving game experiences and player characteristics. After several empirical iterations, GEM is considered suitable to employ as a research and design tool. In addition, GGO should be useful to researchers interested in how player motivations relate to gameplay experiences. Moreover, GEM-IC points to several variables that may prove useful in future research. Accordingly, it is posited that researchers will derive more meaningful insights on games and players by investigating detailed, context-specific characteristics as compared to general, demographic ones. Ultimately, it is believed that GEM, GGO, and GEM-IC will be useful tools for researchers and designers who seek to create effective gameplay experiences that meet the needs of players.
ContributorsQuick, John (Author) / Atkinson, Robert (Thesis advisor) / McNamara, Danielle (Committee member) / Nelson, Brian (Committee member) / Savenye, Wilhelmina (Committee member) / Arizona State University (Publisher)
Created2013
Description
Temporal data are increasingly prevalent and important in analytics. Time series (TS) data are chronological sequences of observations and an important class of temporal data. Fields such as medicine, finance, learning science and multimedia naturally generate TS data. Each series provide a high-dimensional data vector that challenges the learning of the relevant patterns This dissertation proposes TS representations and methods for supervised TS analysis. The approaches combine new representations that handle translations and dilations of patterns with bag-of-features strategies and tree-based ensemble learning. This provides flexibility in handling time-warped patterns in a computationally efficient way. The ensemble learners provide a classification framework that can handle high-dimensional feature spaces, multiple classes and interaction between features. The proposed representations are useful for classification and interpretation of the TS data of varying complexity. The first contribution handles the problem of time warping with a feature-based approach. An interval selection and local feature extraction strategy is proposed to learn a bag-of-features representation. This is distinctly different from common similarity-based time warping. This allows for additional features (such as pattern location) to be easily integrated into the models. The learners have the capability to account for the temporal information through the recursive partitioning method. The second contribution focuses on the comprehensibility of the models. A new representation is integrated with local feature importance measures from tree-based ensembles, to diagnose and interpret time intervals that are important to the model. Multivariate time series (MTS) are especially challenging because the input consists of a collection of TS and both features within TS and interactions between TS can be important to models. Another contribution uses a different representation to produce computationally efficient strategies that learn a symbolic representation for MTS. Relationships between the multiple TS, nominal and missing values are handled with tree-based learners. Applications such as speech recognition, medical diagnosis and gesture recognition are used to illustrate the methods. Experimental results show that the TS representations and methods provide better results than competitive methods on a comprehensive collection of benchmark datasets. Moreover, the proposed approaches naturally provide solutions to similarity analysis, predictive pattern discovery and feature selection.
ContributorsBaydogan, Mustafa Gokce (Author) / Runger, George C. (Thesis advisor) / Atkinson, Robert (Committee member) / Gel, Esma (Committee member) / Pan, Rong (Committee member) / Arizona State University (Publisher)
Created2012
Description
This study was designed to test a new method of instruction for Japanese language students' re-acquisition of the Japanese relative clause structure. 10 Japanese language students who had already been exposed to the Japanese relative clause in their previous semester were asked to take a pretest that assessed their (a) knowledge of basic grammar concepts such as a "subject" and "predicate," (b) their ability to apply those basic grammar concepts to the Japanese language, and (c) their grasp of the rules applying to the formation of the Japanese relative clause. Students were then placed into a control group containing 6 students and an experimental group containing four students. The experimental group received additional lessons consisting of explicit instruction of basic grammar in both Japanese and English, as well as basic noun relativization rules in each language. The study found that the explicit instruction helped student comprehension of the relative clause structure, although some difficulties remain in identifying the relative clause and in constructing it on their own.
ContributorsHinojosa, Katie Crystine (Author) / Tomoko, Shimomura (Thesis director) / Ghanem, Carla (Committee member) / Chambers, Anthony (Committee member) / Barrett, The Honors College (Contributor) / School of International Letters and Cultures (Contributor)
Created2013-05
Description
Millions of people every day log onto their computers to play competitive games with others around the world. Each of these players has their own unique personality and their own reasons for playing. To explore the relationship between player personalities and gameplay, this study asked participants to report their Myers-Briggs sixteen personality types and complete a survey that asked them questions about their behavior while games playing competitively online including their preferred in-game archetype and questions about how they interact with other players online. The survey also included the Grit Scale test, which which was intended to explore players' perseverance. Nearly 700 people participated in the study and all responses were analyzed based on their Myers-Briggs' personality type. While this study revealed that Myers-Briggs' personality type alone cannot determine a player's mindset while playing online, it was found to be an indicator of how they feel about socializing with others online. The implications of these results are discussed in this paper.
ContributorsKeyvani, Kurosh (Author) / Atkinson, Robert (Thesis director) / Chavez-Echeagaray, Maria Elena (Committee member) / Computer Science and Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2017-05
Description
Abstract Chess has been a common research topic for expert-novice studies and thus for learning science as a whole because of its limited framework and longevity as a game. One factor is that chess studies are good at measuring how expert chess players use their memory and skills to approach a new chessboard con�guration. Studies have shown that chess skill is based on memory, speci�cally, "chunks" of chess piece positions that have been previously encountered by players. However, debate exists concerning how these chunks are constructed in players' memory. These chunks could be constructed by proximity of pieces on the chessboard as well as their precise location or constructed through attack-defense relations. The primary objective of this study is to support which one is more in line with chess players' actual chess abilities based off their memory, proximity or attack/defense. This study replicates and extends an experiment conducted by McGregor and Howe (2002), which explored the argument that pieces are primed more by attack and defense relations than by proximity. Like their study, the present study examined novice and expert chess players' response times for correct and error responses by showing slides of game configurations. In addition to these metrics, the present study also incorporated an eye-tracker to measure visual attention and EEG to measure affective and cognitive states. They were added to allow the comparison of subtle and unconscious behaviors of both novices and expert chess players. Overall, most McGregor and Howe's (2002) results were replicated supporting their theory on chess expertise. This included statistically significance for skill in the error rates with the mean error rates on the piece recognition tests were 70.1% for novices and 87.9% for experts, as well as significance for the two-way interaction for relatedness and proximity with error rates of 22.4% for unrelated/far, 18.8% for related/far, 15.8% for unrelated
ear, and 29.3% for related
ear. Unfortunately, there were no statistically significance for any of the response time effects, which McGregor and Howe found for the interaction between skill and proximity. Despite eye-tracking and EEG data not either support nor confirm McGregor and Howe's theory on how chess players memorize chessboard configurations, these metrics did help build a secondary theory on how novices typically rely on proximity to approach chess and new visual problems in general. This was exemplified by the statistically significant results for short-term excitement for the two-way interaction of skill and proximity, where the largest short-term excitement score was between novices on near proximity slides. This may indicate that novices, because they may lean toward using proximity to try to recall these pieces, experience a short burst of excitement when the pieces are close to each other because they are more likely to recall these configurations.
ear, and 29.3% for related
ear. Unfortunately, there were no statistically significance for any of the response time effects, which McGregor and Howe found for the interaction between skill and proximity. Despite eye-tracking and EEG data not either support nor confirm McGregor and Howe's theory on how chess players memorize chessboard configurations, these metrics did help build a secondary theory on how novices typically rely on proximity to approach chess and new visual problems in general. This was exemplified by the statistically significant results for short-term excitement for the two-way interaction of skill and proximity, where the largest short-term excitement score was between novices on near proximity slides. This may indicate that novices, because they may lean toward using proximity to try to recall these pieces, experience a short burst of excitement when the pieces are close to each other because they are more likely to recall these configurations.
ContributorsSeto, Christian Paul (Author) / Atkinson, Robert (Thesis director) / Runger, George (Committee member) / Industrial, Systems (Contributor, Contributor) / Barrett, The Honors College (Contributor)
Created2017-05
Description
The first step in process improvement is to scope the problem, next is measure the current process, but if data is not readily available and cannot be manually collected, then a measurement system must be implemented. General Dynamics Mission Systems (GDMS) is a lean company that is always seeking to improve. One of their current bottlenecks is the incoming inspection department. This department is responsible for finding defects on parts purchased and is critical to the high reliability product produced by GDMS. To stay competitive and hold their market share, a decision was made to optimize incoming inspection. This proved difficult because no data is being collected. Early steps in many process improvement methodologies, such as Define, Measure, Analyze, Improve and Control (DMAIC), include data collection; however, no measurement system was in place, resulting in no available data for improvement. The solution to this problem was to design and implement a Management Information System (MIS) that will track a variety of data. This will provide the company with data that will be used for analysis and improvement. The first stage of the MIS was developed in Microsoft Excel with Visual Basic for Applications because of the low cost and overall effectiveness of the software. Excel allows update to be made quickly, and allows GDMS to collect data immediately. Stage two would be moving the MIS to a more practicable software, such as Access or MySQL. This thesis is only focuses on stage one of the MIS, and GDMS will proceed with stage two.
ContributorsDiaz, Angel (Author) / McCarville, Daniel R. (Thesis director) / Pavlic, Theodore (Committee member) / Industrial, Systems (Contributor) / Barrett, The Honors College (Contributor)
Created2017-05
Description
The overall energy consumption around the United States has not been reduced even with the advancement of technology over the past decades. Deficiencies exist between design and actual energy performances. Energy Infrastructure Systems (EIS) are impacted when the amount of energy production cannot be accurately and efficiently forecasted. Inaccurate engineering assumptions can result when there is a lack of understanding on how energy systems can operate in real-world applications. Energy systems are complex, which results in unknown system behaviors, due to an unknown structural system model. Currently, there exists a lack of data mining techniques in reverse engineering, which are needed to develop efficient structural system models. In this project, a new type of reverse engineering algorithm has been applied to a year's worth of energy data collected from an ASU research building called MacroTechnology Works, to identify the structural system model. Developing and understanding structural system models is the first step in creating accurate predictive analytics for energy production. The associative network of the building's data will be highlighted to accurately depict the structural model. This structural model will enhance energy infrastructure systems' energy efficiency, reduce energy waste, and narrow the gaps between energy infrastructure design, planning, operation and management (DPOM).
ContributorsCamarena, Raquel Jimenez (Author) / Chong, Oswald (Thesis director) / Ye, Nong (Committee member) / Industrial, Systems (Contributor) / Barrett, The Honors College (Contributor)
Created2016-12
Description
Workplace productivity is a result of many factors, and among them is the setup of the office and its resultant noise level. The conversations and interruptions that come along with converting an office to an open plan can foster innovation and creativity, or they can be distracting and harm the performance of employees. Through simulation, the impact of different types of office noise was studied along with other changing conditions such as number of people in the office. When productivity per person, defined in terms of mood and focus, was measured, it was found that the effect of noise was positive in some scenarios and negative in others. In simulations where employees were performing very similar tasks, noise (and its correlates, such as number of employees), was beneficial. On the other hand, when employees were engaged in a variety of different types of tasks, noise had a negative overall effect. This indicates that workplaces that group their employees by common job functions may be more productive than workplaces where the problems and products that employees are working on are varied throughout the workspace.
ContributorsHall, Mikaela Starrantino (Author) / Pavlic, Theodore P. (Thesis director) / Cooke, Nancy (Committee member) / Industrial, Systems (Contributor) / Barrett, The Honors College (Contributor)
Created2017-05
Description
Consumer automotive vehicles have been an essential part of daily life for many over several decades. Many people also find that the multimedia screens found in the center consoles of many modern vehicles are robust enough to complete a certain number of tasks, such as navigating to a destination, playing music, or taking a phone call. As a result, it is important for designers to look into their decisions and how they might affect the overall experience a person has while interacting with multimedia screen as they are driving a vehicle. This study aims to look into how existing design decisions present themselves in the multimedia screens of modern vehicles and which principles of design users favor when interacting with the systems. With 188 participants and three vehicles tested, including the 2019 Toyota Highlander equipped with native software, the 2019 Hyundai Sonata equipped with Android Auto, and the 2020 Hyundai Elantra equipped with Apple CarPlay, it was found that design principles found in Human Computer Interaction, such as Gestalt principles, are relevant in allowing for a more positive, enjoyable experience in completing tasks such as navigation, playing music, and taking a phone call.
ContributorsHwang, Jaesang (Author) / Atkinson, Robert (Thesis director) / Chavez Echeagaray, Maria Elena (Committee member) / Barrett, The Honors College (Contributor) / Computer Science and Engineering Program (Contributor)
Created2023-05
Description
This quantitative, quasi-experimental study examined the effectiveness of three types of online guided-practice activities designed to increase learning of visual art concepts, the color concepts of hue, tint, shade, value, and neutral colors in particular, among fifth grade students in a large school district in the southwestern United States. The study's results indicated that, when students were given a limited amount of time to engage in practice activities, there was no statistically significant difference among the three types of guided practice and the control group. What was effective, however, was the instructional component of this study's instruments.
ContributorsDelahunt, Michael (Author) / Erickson, Mary (Thesis advisor) / Atkinson, Robert (Committee member) / Young, Bernard (Committee member) / Arizona State University (Publisher)
Created2014