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- Genre: Masters Thesis
- Creators: Arizona State University
- Member of: ASU Electronic Theses and Dissertations
Description
The field of education has been immensely benefited by major breakthroughs in technology. The arrival of computers and the internet made student-teacher interaction from different parts of the world viable, increasing the reach of the educator to hitherto remote corners of the world. The arrival of mobile phones in the recent past has the potential to provide the next paradigm shift in the way education is conducted. It combines the universal reach and powerful visualization capabilities of the computer with intimacy and portability. Engineering education is a field which can exploit the benefits of mobile devices to enhance learning and spread essential technical know-how to different parts of the world. In this thesis, I present AJDSP, an Android application evolved from JDSP, providing an intuitive and a easy to use environment for signal processing education. AJDSP is a graphical programming laboratory for digital signal processing developed for the Android platform. It is designed to provide utility; both as a supplement to traditional classroom learning and as a tool for self-learning. The architecture of AJDSP is based on the Model-View-Controller paradigm optimized for the Android platform. The extensive set of function modules cover a wide range of basic signal processing areas such as convolution, fast Fourier transform, z transform and filter design. The simple and intuitive user interface inspired from iJDSP is designed to facilitate ease of navigation and to provide the user with an intimate learning environment. Rich visualizations necessary to understand mathematically intensive signal processing algorithms have been incorporated into the software. Interactive demonstrations boosting student understanding of concepts like convolution and the relation between different signal domains have also been developed. A set of detailed assessments to evaluate the application has been conducted for graduate and senior-level undergraduate students.
ContributorsRanganath, Suhas (Author) / Spanias, Andreas (Thesis advisor) / Tepedelenlioğlu, Cihan (Committee member) / Tsakalis, Konstantinos (Committee member) / Arizona State University (Publisher)
Created2013
Description
Believe It! is an animated interactive computer program that delivers cognitive restructuring to adolescent females' irrational career beliefs. It challenges the irrational belief and offers more reasonable alternatives. The current study investigated the potentially differential effects of Asian versus Caucasian animated agents in delivering the treatment to young Chinese American women. The results suggested that the Asian animated agent was not significantly superior to the Caucasian animated agent. Nor was there a significant interaction between level of acculturation and the effects of the animated agents. Ways to modify the Believe It! program for Chinese American users were recommended.
ContributorsZhang, Xue (Author) / Horan, John J (Thesis advisor) / Homer, Judith (Committee member) / Atkinson, Robert (Committee member) / Arizona State University (Publisher)
Created2013
Description
Efficiency of components is an ever increasing area of importance to portable applications, where a finite battery means finite operating time. Higher efficiency devices need to be designed that don't compromise on the performance that the consumer has come to expect. Class D amplifiers deliver on the goal of increased efficiency, but at the cost of distortion. Class AB amplifiers have low efficiency, but high linearity. By modulating the supply voltage of a Class AB amplifier to make a Class H amplifier, the efficiency can increase while still maintaining the Class AB level of linearity. A 92dB Power Supply Rejection Ratio (PSRR) Class AB amplifier and a Class H amplifier were designed in a 0.24um process for portable audio applications. Using a multiphase buck converter increased the efficiency of the Class H amplifier while still maintaining a fast response time to respond to audio frequencies. The Class H amplifier had an efficiency above the Class AB amplifier by 5-7% from 5-30mW of output power without affecting the total harmonic distortion (THD) at the design specifications. The Class H amplifier design met all design specifications and showed performance comparable to the designed Class AB amplifier across 1kHz-20kHz and 0.01mW-30mW. The Class H design was able to output 30mW into 16Ohms without any increase in THD. This design shows that Class H amplifiers merit more research into their potential for increasing efficiency of audio amplifiers and that even simple designs can give significant increases in efficiency without compromising linearity.
ContributorsPeterson, Cory (Author) / Bakkaloglu, Bertan (Thesis advisor) / Barnaby, Hugh (Committee member) / Kiaei, Sayfe (Committee member) / Arizona State University (Publisher)
Created2013
Description
In an effort to stress the benefits of the application of renewable energy to the next generation of science, technology, engineering, arts, and mathematics (STEAM) professionals, instructional modules on energy and biogas were integrated into a summer camp curriculum that challenged students to apply STEAM concepts in the design and development of chain reaction machines. Each module comprised an interactive presentations and a hands-on component where students operated a manipulative relevant to the content. During summer 2013, this camp was implemented at two high schools in Arizona and one in Trinidad and Tobago. Assessments showed that the overall modules were effective in helping students learn and retain the information presented on energy and biogas production. To improve future implementations of these modules, specifically the module on biogas production, the anaerobic digester was redesigned. In addition, a designed experiment was conducted to determine how to optimize the influent and operational environment that is available in an average high school classroom to generate maximum biogas yield. Eight plug-flow anaerobic digesters made of PVC piping and fixtures were used in a 2x3 factorial design assessing: co-digestion (20mL or 50mL) used cooking oil, temperature (25°C or 40°C), and addition of inoculum (0mL or 200mL). Biogas production was captured at two intervals over a 30-day period, and the experiments were replicated three times. Results showed that temperature at 40°C significantly increased biogas production and should be used over 25°C when using anaerobic digesters. Other factors that may potentially increase biogas production are combination of temperature at 40°C and 50mL of used cooking oil. In the future, the improvements made in the design of the anaerobic digester, and the applications of the finding from the experimental design, are expected to lead to an improved manipulative for teaching students about biogas production.
ContributorsMcCall, Shakira Renee (Author) / Dalrymple, Odesma O (Thesis advisor) / Bradley, Rogers (Committee member) / Hristovski, Kiril (Committee member) / Arizona State University (Publisher)
Created2014
Description
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.
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.
ContributorsViswanathan, Sree Aurovindh (Author) / VanLehn, Kurt (Thesis advisor) / T.H CHI, Michelene (Committee member) / Walker, Erin (Committee member) / Arizona State University (Publisher)
Created2014
Description
For this master's thesis, a unique set of cognitive prompts, designed to be delivered through a teachable robotic agent, were developed for students using Tangible Activities for Geometry (TAG), a tangible learning environment developed at Arizona State University. The purpose of these prompts is to enhance the affordances of the tangible learning environment and help researchers to better understand how we can design tangible learning environments to best support student learning. Specifically, the prompts explicitly encourage users to make use of their physical environment by asking students to perform a number of gestures and behaviors while prompting students about domain-specific knowledge. To test the effectiveness of these prompts that combine elements of cognition and physical movements, the performance and behavior of students who encounter these prompts while using TAG will be compared against the performance and behavior of students who encounter a more traditional set of cognitive prompts that would typically be used within a virtual learning environment. Following this study, data was analyzed using a novel modeling and analysis tool that combines enhanced log annotation using video and user model generation functionalities to highlight trends amongst students.
ContributorsThomas, Elissa (Author) / Burleson, Winslow (Thesis advisor) / Muldner, Katarzyna (Committee member) / Walker, Erin (Committee member) / Glenberg, Arthur (Committee member) / Arizona State University (Publisher)
Created2014
Description
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.
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.
ContributorsNelakurthi, Arun Reddy (Author) / Pon-Barry, Heather (Thesis advisor) / VanLehn, Kurt (Committee member) / Walker, Erin (Committee member) / Arizona State University (Publisher)
Created2014
Description
In this action research, the need for high schools to embrace a pedagogical shift to teaching 21st century computer and online literacy skills is investigated. This study explored areas of secondary and higher education, technology usage, and online pedagogies, 21st century skill frameworks, and brain function as they pertain to learning and decision-making, with the aim of comprehending the differing high school levels of preparedness for college in regards to 21st century skills. Through literature reviews, a research was designed to further explore the specific areas of a discovered gap in high school students' 21st century skills for college. Pre- and post-unit surveys, in combination with student assignment scores, were complied and examined to reveal a weakness in academic habits and computer literacy skills associated with 21st century learning. The study results support literature review findings of a breach between high school 21st century skill levels and collegiate level necessities. With these findings, it is suggested that instructors become choice architects, giving them the unique ability to nudge high school policy makers and students towards identifying the gaps between the analog and digital worlds of academia, generating more successful students as they transition to university online courses.
ContributorsHorn, Timothy (Author) / Patel, Mookesh (Thesis advisor) / Giard, Jacques (Committee member) / Marsh, Josephine (Committee member) / Arizona State University (Publisher)
Created2015
Description
Observational tutoring has been found to be an effective method for teaching a variety of subjects by reusing dialogue from previous successful tutoring sessions. While it has been shown content can be learned through observational tutoring it has yet to been examined if a secondary behavior such as goal-setting can be influenced. The present study investigated if observing virtual humans engaging in a tutoring session on rotational kinematics with embedded positive goal oriented dialogue would increase knowledge of the material and perpetuate a shift an observer's goal-orientation from performance avoidance goal orientation (PAVGO) to learning goal orientation (LGO). Learning gains were observed in pre to post test knowledge retention tests. Significant changes from pretest to posttest occurred across conditions for LGO. Additionally, significant changes from PAVGO pretest to posttest were observed in the control condition however PAVGO did not significantly change in the experimental condition.
ContributorsTwyford, Jessica (Author) / Craig, Scotty D. (Thesis advisor) / Niemczyk, Mary (Committee member) / Kuzel, Michael (Committee member) / Arizona State University (Publisher)
Created2014
Description
Computational thinking, the creative thought process behind algorithmic design and programming, is a crucial introductory skill for both computer scientists and the population in general. In this thesis I perform an investigation into introductory computer science education in the United States and find that computational thinking is not effectively taught at either the high school or the college level. To remedy this, I present a new educational system intended to teach computational thinking called Genost. Genost consists of a software tool and a curriculum based on teaching computational thinking through fundamental programming structures and algorithm design. Genost's software design is informed by a review of eight major computer science educational software systems. Genost's curriculum is informed by a review of major literature on computational thinking. In two educational tests of Genost utilizing both college and high school students, Genost was shown to significantly increase computational thinking ability with a large effect size.
ContributorsWalliman, Garret (Author) / Atkinson, Robert (Thesis advisor) / Chen, Yinong (Thesis advisor) / Lee, Yann-Hang (Committee member) / Arizona State University (Publisher)
Created2015