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.
Displaying 1 - 5 of 5
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- All Subjects: Educational technology
- Creators: Wolf, Leigh
- Creators: Gary, Kevin
Description
One persisting problem in Massive Open Online Courses (MOOCs) is the issue of student dropout from these courses. The prediction of student dropout from MOOC courses can identify the factors responsible for such an event and it can further initiate intervention before such an event to increase student success in MOOC. There are different approaches and various features available for the prediction of student’s dropout in MOOC courses.In this research, the data derived from the self-paced math course ‘College Algebra and Problem Solving’ offered on the MOOC platform Open edX offered by Arizona State University (ASU) from 2016 to 2020 was considered. This research aims to predict the dropout of students from a MOOC course given a set of features engineered from the learning of students in a day. Machine Learning (ML) model used is Random Forest (RF) and this model is evaluated using the validation metrics like accuracy, precision, recall, F1-score, Area Under the Curve (AUC), Receiver Operating Characteristic (ROC) curve. The average rate of student learning progress was found to have more impact than other features. The model developed can predict the dropout or continuation of students on any given day in the MOOC course with an accuracy of 87.5%, AUC of 94.5%, precision of 88%, recall of 87.5%, and F1-score of 87.5% respectively. The contributing features and interactions were explained using Shapely values for the prediction of the model. The features engineered in this research are predictive of student dropout and could be used for similar courses to predict student dropout from the course. This model can also help in making interventions at a critical time to help students succeed in this MOOC course.
ContributorsDominic Ravichandran, Sheran Dass (Author) / Gary, Kevin (Thesis advisor) / Bansal, Ajay (Committee member) / Cunningham, James (Committee member) / Sannier, Adrian (Committee member) / Arizona State University (Publisher)
Created2021
Description
The purpose of this study was to evaluate the role a peer-driven technology acceptance model (PDTAM) in the form of a Community of Practice (CoP) played in assisting users in the acceptance of Trellis technologies at the University of Arizona. Constituent Relationship Management (CRM) technologies are becoming more common in higher education, helping to track interactions, streamline processes, and support customized experiences for students. Unfortunately, not all users are receptive to new technologies, and subsequent adoption can be slow. While the study of technology adoption literature provides insight into what motivates individuals to accept or reject new technologies, used herein was the most prevalent technology adoption theory – the Technology Acceptance Model (TAM; Davis, 1986). I used TAM to explore technology acceptance more spec user’s Perceived Ease of Use (PEU) and Perceived Usefulness (PU). In this MMAR study, I used TAM (Davis, 1986) as well as Everett Roger’s (1983) Diffusion Innovation Theory (DOI) to evaluate the impact of the CoP mentioned above on user adoption. Additionally, I added Perceived Value (PV) as a third construct to the TAM. Using pre-and post-intervention surveys, observation, and interviews, to both collect and analyze data on the impacts of my CoP intervention, I determined that the CoPs did assist in more thoroughly diffusing knowledge share, which reportedly led to improved PEU, PU, and PV in the treatment group. Specifically, the peer-to-peer mentoring that occurred in the CoPs helped users feel empowered to use the capabilities. Additionally, while the CoPs reportedly improved PEU, PU, and PV, the peer-to-peer model and the Trellis technologies still have not matured enough to realize their total value to campus.
ContributorsHodge, Nikolas (Author) / Beardsley, Audrey (Thesis advisor) / Neumann, William (Committee member) / Wolf, Leigh (Committee member) / Arizona State University (Publisher)
Created2022
Description
The purpose of this mixed methods action research project was to address the problem of practice of incorporating foundational grammar, spelling, and punctuation (GSP) instruction into community college journalism classes through the intervention of online interactive modules called The Story Mechanics Project (SMP). The modules were developed and piloted during the first two cycles of action research. Following feedback and changes in local context influencing the intervention’s need and purpose, the modules were modified and simplified for the current research cycle. The main areas of focus were the efficacy of intervention, student perceptions of self-efficacy, and insights from designing and facilitating the intervention through a lens of critical digital pedagogy. The intervention was carried out in an online, asynchronous introductory journalism class in the Spring 2022 semester. Quantitative and qualitative data were collected from a pretest/post-test skills assessment, a post-intervention survey with a retrospective component, final course writing assignment submissions, and the researcher blog. Results showed the intervention had a positive but insignificant impact on students’ GSP skills application and that it did not significantly affect student perceptions of self-efficacy in the GSP domains; there was no significant relationship between students’ perception of self-efficacy and their application of GSP skills in their writing submissions. Pedagogical insights regarding humanizing learning, balancing tensions, and releasing control emerged from qualitative analysis. Study limitations included a small sample size and a focus on GSP errors instead of correct usage. This study collaborated the need for a more effective way to teach story mechanics.
ContributorsCalo, Jeanette (Author) / Weinberg, Andrea (Thesis advisor) / Wolf, Leigh (Committee member) / Pilbeam, Renee (Committee member) / Arizona State University (Publisher)
Created2022
Description
Since the early 2000s the Rubik’s Cube has seen growing usage at speedsolving competitions and as an effective tool to teach Science, Technology, Engineering, Mathematics (STEM) topics at hundreds of schools and universities across the world. Recently, cube manufacturers have begun embedding sensors to enable digital face tracking. The live feedback from these so called “smartcubes” enables a new wave of immersive solution tutorials and interactive educational games using the cube as a controller. Existing smartcube software has several limitations. Manufacturers’ applications support only a narrow set of puzzle form factors and application platforms, fragmenting the ecosystem. Most apps require an active internet connection for key features, limiting where users can practice with a smartcube. Finally, existing applications focus on a single 3x3x3connection, losing opportunities afforded by new form factors. This research demonstrates an open-source smartcube application which mitigates these limitations. Particular attention is given to creating an Application Programming Interface (API) for smartcube communication and building representative solve analysis tools. These innovations have included successful negotiations to re-license existing open-source Rubik’sCube software projects to support deployment on multiple platforms, particularly iOS. The resulting application supports smartcubes from three manufacturers, runs on two platforms (Android and iOS), functions entirely offline after an initial download of remote assets, demonstrates concurrent connections with up to six smartcubes, and supports all current and anticipated smartcube form factors. These foundational elements can accelerate future efforts to build smartcube applications, including automated performance feedback systems and personalized gamification of learning experiences. Such advances will hopefully enhance the Rubik’s Cube’s value both as a competitive toy and as a pedagogical tool in educational institutions worldwide.
ContributorsHale, Joseph (Author) / Bansal, Ajay (Thesis advisor) / Heinrichs, Robert (Committee member) / Gary, Kevin (Committee member) / Arizona State University (Publisher)
Created2023
Description
Preservice teachers are faced with many challenges as they enter their first year of teaching. This is particularly true when dealing with future-ready skills, such as technology integration in K-12 classrooms, an area where many higher education or teaching faculty may not feel comfortable or fluent enough to support preservice teachers or to model in their own instruction.
This action research study aimed to understand how faculty develop Technological Pedagogical Content Knowledge (TPACK) in ways that will help them to enhance their instruction and model technology integration for preservice teachers. An online community was created that allowed teacher educators to interact synchronously or asynchronously to collaborate, learn, and practice new technological skills. This community served as a place for teacher educators to play with new technology and to share their ideas and practices with their peers—ideally to begin the process of developing the knowledge and fluency with technology that would allow them to better support teacher education students.
Both qualitative and quantitative data were used to explore faculty’s development of TPACK. A pre-survey, retrospective pre-survey, and post-survey were administered and analyzed. Also, interviews of participants and observations of the online community were used to collect qualitative data.
The results of the study showed an increase in participants’ confidence for selecting technologies to enhance their instruction after they participated in the online community. Also, the participants felt more confident using strategies that combine content, technologies, and teaching approaches in their classrooms or other learning environments.
In Chapter 5, a discussion of the findings was presented, in which several main implications are shared for researchers who might be engaged in similar work. Also, the lessons learned from this action research are explained, as well as the limitations experienced in this study.
This action research study aimed to understand how faculty develop Technological Pedagogical Content Knowledge (TPACK) in ways that will help them to enhance their instruction and model technology integration for preservice teachers. An online community was created that allowed teacher educators to interact synchronously or asynchronously to collaborate, learn, and practice new technological skills. This community served as a place for teacher educators to play with new technology and to share their ideas and practices with their peers—ideally to begin the process of developing the knowledge and fluency with technology that would allow them to better support teacher education students.
Both qualitative and quantitative data were used to explore faculty’s development of TPACK. A pre-survey, retrospective pre-survey, and post-survey were administered and analyzed. Also, interviews of participants and observations of the online community were used to collect qualitative data.
The results of the study showed an increase in participants’ confidence for selecting technologies to enhance their instruction after they participated in the online community. Also, the participants felt more confident using strategies that combine content, technologies, and teaching approaches in their classrooms or other learning environments.
In Chapter 5, a discussion of the findings was presented, in which several main implications are shared for researchers who might be engaged in similar work. Also, the lessons learned from this action research are explained, as well as the limitations experienced in this study.
ContributorsScott, Lynda (Author) / Henriksen, Danah (Thesis advisor) / Mishra, Punya (Committee member) / Wolf, Leigh (Committee member) / Leahy, Sean (Committee member) / Arizona State University (Publisher)
Created2019