Theses and Dissertations
This collection includes both ASU Theses and Dissertations, submitted by graduate students, and the Barrett, Honors College theses submitted by undergraduate students.
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Description
The purpose of this action research study was to examine how membership within a virtual community of practice impacted individual professional development, knowledge exchange practices, and self-efficacy. The G-3/5/7 virtual community of practice (VCoP) website was created to provide members with access to a wide range of career-related content, while also bestowing them with the level of volition needed to be completely in control of when and how they consume content. Feedback from early cycles of research suggested the pilot version of the VCoP wasn’t perceived as user-friendly and didn’t provide a broad range of professional development-related content. Thus, the layout of the VCoP was completely redesigned, and content offerings in the content repository and on website pages were broadened. This action research study is grounded in social cognitive theory, social cognitive career theory, and the community of practice framework. Reviewed literature includes studies pertaining to mutual engagement within social learning environments, facilitating professional development, sustaining communities of practice, and implementing virtual communities of practice. Participants in this study included a combination of Department of the Army civilian and military employees. Over the course of 14 weeks, these employees were invited to voluntarily join the G-3/5/7 VCoP and freely access and use the site for any reason they deemed necessary. At the end of the 14-week period, participants completed a questionnaire and participated in semi-structured interviews. The result of the study revealed members generally found the G-3/5/7 VCoP website to be user-friendly. They also believed the website could help them accomplish professional development goals, exchange knowledge with peers, and produce higher quality work more efficiently. The analysis of results includes discussion on the triangulation of quantitative and qualitative data and connects results to the literature that influenced this study. Also, lessons learned, study limitations, implications for practice, and recommendations for future action research are discussed.
ContributorsRoy, Brennan M. (Author) / Marsh, Josephine (Thesis advisor) / Bankus, Tammy (Committee member) / Clausen, Jennifer (Committee member) / Arizona State University (Publisher)
Created2019
Description
Software engineering education today is a technologically advanced and rapidly evolving discipline. Being a discipline where students not only design but also build new technology, it is important that they receive a hands on learning experience in the form of project based courses. To maximize the learning benefit, students must conduct project-based learning activities in a consistent rhythm, or cadence. Project-based courses that are augmented with a system of frequent, formative feedback helps students constantly evaluate their progress and leads them away from a deadline driven approach to learning.
One aspect of this research is focused on evaluating the use of a tool that tracks student activity as a means of providing frequent, formative feedback. This thesis measures the impact of the tool on student compliance to the learning process. A personalized dashboard with quasi real time visual reports and notifications are provided to undergraduate and graduate software engineering students. The impact of these visual reports on compliance is measured using the log traces of dashboard activity and a survey instrument given multiple times during the course.
A second aspect of this research is the application of learning analytics to understand patterns of student compliance. This research employs unsupervised machine learning algorithms to identify unique patterns of student behavior observed in the context of a project-based course. Analyzing and labeling these unique patterns of behavior can help instructors understand typical student characteristics. Further, understanding these behavioral patterns can assist an instructor in making timely, targeted interventions. In this research, datasets comprising of student’s daily activity and graded scores from an under graduate software engineering course is utilized for the purpose of identifying unique patterns of student behavior.
One aspect of this research is focused on evaluating the use of a tool that tracks student activity as a means of providing frequent, formative feedback. This thesis measures the impact of the tool on student compliance to the learning process. A personalized dashboard with quasi real time visual reports and notifications are provided to undergraduate and graduate software engineering students. The impact of these visual reports on compliance is measured using the log traces of dashboard activity and a survey instrument given multiple times during the course.
A second aspect of this research is the application of learning analytics to understand patterns of student compliance. This research employs unsupervised machine learning algorithms to identify unique patterns of student behavior observed in the context of a project-based course. Analyzing and labeling these unique patterns of behavior can help instructors understand typical student characteristics. Further, understanding these behavioral patterns can assist an instructor in making timely, targeted interventions. In this research, datasets comprising of student’s daily activity and graded scores from an under graduate software engineering course is utilized for the purpose of identifying unique patterns of student behavior.
ContributorsXavier, Suhas (Author) / Gary, Kevin A (Thesis advisor) / Bansal, Srividya K (Committee member) / Sohoni, Sohum (Committee member) / Arizona State University (Publisher)
Created2016