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- All Subjects: Learning Management System
- Creators: Gutierrez, Veronica
- Creators: McKenna, Ann
Description
For my Barrett the Honors College senior thesis project, I decided to utilize my knowledge of curriculum design to create a set of learning Modules. I was influenced by my involvement in the Next Generation Service Corps to create these Modules around college student community impact. In the end I developed 6 Modules, each with 4-5 lessons and activities that focused on topics such as volunteerism, civic engagement, and meaningful careers. With interviews rolling through during the design process, I was able to iterate my design as I built it. The design was tested with 14 college students with positive feedback and engagement during the week-long period that it was available. Through this research and design, I found that such a collection of Modules could be beneficial to students to excite them about their potential and educate them about the opportunities that exist for them to take advantage of. This research could serve as a useful tool within the ASU community as an opportunity for the students to build up meaningful skills to create impact. ASU is passionate about education translating into real world applications and creating “changemakers”, and this collection has the opportunity to do just that.
ContributorsLascala, Francesca Louise (Author) / Barab, Sasha (Thesis director) / Gutierrez, Veronica (Committee member) / School for the Future of Innovation in Society (Contributor, Contributor) / Walter Cronkite School of Journalism & Mass Comm (Contributor) / Watts College of Public Service & Community Solut (Contributor) / Barrett, The Honors College (Contributor)
Created2020-05
Description
Online education is fast growing due to its accessibility and scalability, but engineering has fallen behind other fields in adopting and researching the online educational format. Student course-level attrition is a significant issue in online courses. The goal of this dissertation is to better understand the factors that impact course level persistence decisions among online undergraduate engineering students. Three different research methodologies were employed for this study: a systematic literature review (SLR), learning analytics and data mining, and multi-level modeling.The SLR focuses on understanding the temporal trends and findings from research in online engineering education. A total of thirty-nine articles published between 2011 to 2020 met inclusion criteria, and the synthesis of these articles revealed five themes: content design and delivery, student engagement and interactions, assessment, feedback, and challenges in online engineering. Theoretical, methodological, and publication trends across the forty articles were also summarized.
Data for the second study was compiled from 81 courses contained within three online, ABET-accredited undergraduate engineering degree programs at a large, public institution in the southwestern United States. The students' learning management system (LMS) interaction data was utilized to create features that represent the amount of time students spent on different course activities and how those times differed from “typical” interaction patterns among students in the same course. Association rule mining was used to develop rules that describe the behavior of students who completed the course (i.e., completers) and those who opted to withdraw (i.e., leavers). The best measure of student engagement was determined to be the mathematical difference between the percentages of completer and leaver rules met by each student.
Finally, multi-level modeling was used to examine the impact of interpersonal interactions on online undergraduate engineering students' course-level persistence intentions. The data for this study was gathered from online courses during the 2019-2020 academic year. Students completed questionnaires about their course and related persistence intentions twelve times during their 7.5-week online course. Students’ perceptions of the course LMS dialog, instructor practices, and peer support were found to significantly predict their course persistence intentions.
ContributorsKittur, Javeed (Author) / Bekki, Jennifer M (Thesis advisor) / Brunhaver, Samantha R (Thesis advisor) / McKenna, Ann (Committee member) / Arizona State University (Publisher)
Created2022