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- 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 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