Matching Items (3)

150942-Thumbnail Image.png

DSP algorithm and software development on the iPhone/iPad platform

Description

The ease of use of mobile devices and tablets by students has generated a lot of interest in the area of engineering education. By using mobile technologies in signal analysis

The ease of use of mobile devices and tablets by students has generated a lot of interest in the area of engineering education. By using mobile technologies in signal analysis and applied mathematics, undergraduate-level courses can broaden the scope and effectiveness of technical education in classrooms. The current mobile devices have abundant memory and powerful processors, in addition to providing interactive interfaces. Therefore, these devices can support the implementation of non-trivial signal processing algorithms. Several existing visual programming environments such as Java Digital Signal Processing (J-DSP), are built using the platform-independent infrastructure of Java applets. These enable students to perform signal-processing exercises over the Internet. However, some mobile devices do not support Java applets. Furthermore, mobile simulation environments rely heavily on establishing robust Internet connections with a remote server where the processing is performed. The interactive Java Digital Signal Processing tool (iJDSP) has been developed as graphical mobile app on iOS devices (iPads, iPhones and iPod touches). In contrast to existing mobile applications, iJDSP has the ability to execute simulations directly on the mobile devices, and is a completely stand-alone application. In addition to a substantial set of signal processing algorithms, iJDSP has a highly interactive graphical interface where block diagrams can be constructed using a simple drag-n-drop procedure. Functions such as visualization of the convolution operation, and an interface to wireless sensors have been developed. The convolution module animates the process of the continuous and discrete convolution operations, including time-shift and integration, so that users can observe and learn, intuitively. The current set of DSP functions in the application enables students to perform simulation exercises on continuous and discrete convolution, z-transform, filter design and the Fast Fourier Transform (FFT). The interface to wireless sensors in iJDSP allows users to import data from wireless sensor networks, and use the rich suite of functions in iJDSP for data processing. This allows users to perform operations such as localization, activity detection and data fusion. The exercises and the iJDSP application were evaluated by senior-level students at Arizona State University (ASU), and the results of those assessments are analyzed and reported in this thesis.

Contributors

Agent

Created

Date Created
  • 2012

155399-Thumbnail Image.png

The maker movement, the promise of higher education, and the future of work

Description

The 21st century will be the site of numerous changes in education systems in response to a rapidly evolving technological environment where existing skill sets and career structures may cease

The 21st century will be the site of numerous changes in education systems in response to a rapidly evolving technological environment where existing skill sets and career structures may cease to exist or, at the very least, change dramatically. Likewise, the nature of work will also change to become more automated and more technologically intensive across all sectors, from food service to scientific research. Simply having technical expertise or the ability to process and retain facts will in no way guarantee success in higher education or a satisfying career. Instead, the future will value those educated in a way that encourages collaboration with technology, critical thinking, creativity, clear communication skills, and strong lifelong learning strategies. These changes pose a challenge for higher education’s promise of employability and success post-graduation. Addressing how to prepare students for a technologically uncertain future is challenging. One possible model for education to prepare students for the future of work can be found within the Maker Movement. However, it is not fully understood what parts of this movement are most meaningful to implement in education more broadly, and higher education in particular. Through the qualitative analysis of nearly 160 interviews of adult makers, young makers and young makers’ parents, this dissertation unpacks how makers are learning, what they are learning, and how these qualities are applicable to education goals and the future of work in the 21st century. This research demonstrates that makers are learning valuable skills to prepare them for the future of work in the 21st century. Makers are learning communication skills, technical skills in fabrication and design, and developing lifelong learning strategies that will help prepare them for life in an increasingly technologically integrated future. This work discusses what aspects of the Maker Movement are most important for integration into higher education.

Contributors

Agent

Created

Date Created
  • 2017

155553-Thumbnail Image.png

Microlearning with mobile devices: effects of distributed presentation learning and the testing effect on mobile devices

Description

This study investigated the effects of distributed presentation microlearning and the testing effect on mobile devices and student attitudes about the use of mobile devices for learning in higher education.

This study investigated the effects of distributed presentation microlearning and the testing effect on mobile devices and student attitudes about the use of mobile devices for learning in higher education. For this study, a mobile device is considered a smartphone. All communication, content, and testing were completed remotely through participants’ mobile devices.

The study consisted of four conditions: (a) an attitudinal and demographic pre-survey, (b) five mobile instructional modules, (c) mobile quizzes, and (d) an attitudinal post-survey. A total of 311 participants in higher education were enrolled in the study. One hundred thirty-seven participants completed all four conditions of the study. Participants were randomly assigned to experimental conditions in a 2 x 2 factorial design. The levels of the first factor, distribution of instructional content, were: once-per-day and once-per-week. The levels of the second factor, testing, were: a quiz after each module plus a comprehensive quiz and a single comprehensive quiz after all instruction. The dependent variable was learning outcomes in the form of quiz-score results. Attitudinal survey results were analyzed using Principal Axis Factoring to reveal three components, (a) student perceptions about the use of mobile devices in education,

(b) student perceptions about instructors’ beliefs for mobile devices for learning, and (c) student perceptions about the use of mobile devices post-instruction.

The results revealed several findings. There was no significant effect for type of delivery of instruction in a one-way ANOVA. There was a significant effect for testing in a one-way ANOVA There were no main effects of delivery and testing in a 2 x 2 factorial design and there was no main interaction effect, and there was a significant effect of testing on final quiz scores controlling for technical beliefs in a 2 x 2 ANCOVA. The significant difference in testing was contradictory to some literature.

Ownership of personal mobile devices in persons aged 18–29 is practically all-inclusive. Thus, future research on student attitudes and the implementation of personal smartphones for microlearning and testing is still needed to develop and integrate mobile-ready content for higher education.

Contributors

Agent

Created

Date Created
  • 2017