Matching Items (3)
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- All Subjects: Self-efficacy
- All Subjects: Science history
- Creators: Wylie, Ruth
Description
This research aimed to analyze and ultimately understand the relationship between the four dimensions of the Technology Readiness Index (TRI) 2.0 (optimism, innovation, discomfort, and insecurity) when compared to self-efficacy and learning. The experiment design was a one-group pretest-posttest where a participant’s TRI 2.0 acted as a subject variable. This information was then correlated to changes in self-efficacy and content mastery (learning) from pre-/post-test scores pertaining to Google Sheets functions for introductory statistics. In-between the pre- and post-tests, a learning activity was presented which asked participants to analyze quantitative statistics using Google Sheets. Findings of this research demonstrated a statistically insignificant relationship between technology readiness and self-efficacy or learning. Alternatively, significance was observed in changes from pre- to post-test scores for both learning and self-efficacy where a relationship was found between the degree to which participants’ content mastery and self-efficacy change before and after a computer-supported learning activity is assigned. These findings directly contribute to current understanding of how and why individuals can effectively learn and perform in computer-supported learning environments.
ContributorsCervantes Villa, Sabrina Marie (Author) / Craig, Scotty D. (Thesis advisor) / Donner, Jodie (Committee member) / Roscoe, Rod (Committee member) / Wylie, Ruth (Committee member) / Arizona State University (Publisher)
Created2022
Description
The quality of support provided to students in higher education can have a powerful impact on the student’s experience, their perceptions of challenges, and their overall academic success, particularly retaining in and completing their degree. Though many universities create robust services to support undergraduate students, existing literature and efforts by universities may be lacking when it comes to doctoral student support. The purpose of this action research, mixed methods study was to evaluate academic support to first year doctoral students in the School of Life Sciences (SOLS) at Arizona State University, specifically addressing the following concepts related to their doctoral study: development of self-efficacy, awareness of requirements and policies, and sense of belonging. With Communities of Practice and self-efficacy theory providing a framework for this study, first year doctoral students in SOLS were invited to participate in a twelve-week, two-condition study during their first semester. The two-condition study involved a Personal Support and a Social Support condition, wherein Personal Support participants (n=8) received 1:1 academic advising and biweekly newsletters, while Social Support participants (n=14) engaged in biweekly advising sessions within groups of 3-6 students and an academic advisor. Results suggest Social Support significantly impacted SOLS doctoral student self-efficacy scores (z = -1.96, p = .05), it created an avenue for students to cultivate community with doctoral student peers thus benefiting sense of belonging, and collaborating with peers influenced awareness to the point of Social Support participants becoming a resource for other students not participating in the study. In contrast, Personal Support appeared to have less of an impact on self-efficacy, sense of belonging, and awareness. For students with vulnerable needs to disclose, Personal Support has the potential to reinforce self-efficacy, sense of belonging, and awareness, but the impacts are nominal otherwise. Furthermore, by the end of their first academic year Social Support participants had retained their self-efficacy and sense of belonging scores. Ultimately, the findings suggest the need for reevaluating how doctoral students are supported in and outside SOLS, with a specific discussion about incorporating Social Support as a permanent model for academic support.
ContributorsFranse, Kylie Rae (Author) / Wylie, Ruth (Thesis advisor) / Vogel, Joanne (Thesis advisor) / Farmer-Thompson, Antoinette (Committee member) / Arizona State University (Publisher)
Created2023
Description
The emergence of machine intelligence, which is superior to the best human talent in some problem-solving tasks, has rendered conventional educational goals obsolete, especially in terms of enhancing human capacity in specific skills and knowledge domains. Hence, artificial intelligence (AI) has become a buzzword, espousing both crisis rhetoric and ambition to enact policy reforms in the educational policy arena. However, these policy measures are mostly based on an assumption of binary human-machine relations, focusing on exploitation, resistance, negation, or competition between humans and AI due to the limited knowledge and imagination about human-machine relationality. Setting new relations with AI and negotiating human agency with the advanced intelligent machines is a non-trivial issue; it is urgent and necessary for human survival and co-existence in the machine era. This is a new educational mandate. In this context, this research examined how the notion of human and machine intelligence has been defined in relation to one another in the intellectual history of educational psychology and AI studies, representing human and machine intelligence studies respectively. This study explored a common paradigmatic space, so-called ‘cyborg space,’ connecting the two disciplines through cross-referencing in the citation network and cross-modeling in the metaphorical semantic space. The citation network analysis confirmed the existence of cross-referencing between human and machine intelligence studies, and interdisciplinary journals conceiving human-machine interchangeability. The metaphor analysis found that the notion of human and machine intelligence has been seamlessly interwoven to be part of a theoretical continuum in the most commonly cited references. This research concluded that the educational research and policy paradigm needs to be reframed based on the fact that the underlying knowledge of human and machine intelligence is not strictly differentiated, and human intelligence is relatively provincialized within the human-machine integrated system.
ContributorsGong, Byoung-gyu (Author) / McGurty, Iveta (Thesis advisor) / Wylie, Ruth (Committee member) / Dorn, Sherman (Committee member) / Zheng, Yi (Committee member) / Arizona State University (Publisher)
Created2021