Browsing by Subject "Technology acceptance model"

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A comparison of the effects of mobile device display size and orientation, and text segmentation on learning, cognitive load, and user perception in a higher education chemistry course
(2015-05) Karam, Angela Marie; Resta, Paul E.; Liu, Min; Hughes, Joan E.; Riegle-Crumb, Catherine; Matthew, Eastin
This study aimed to understand the relationship between mobile device screen display size (laptops and smartphones) and text segmentation (continuous text, medium text segments, and small text segments) on learning outcomes, cognitive load, and user perception. This quantitative study occurred during the spring semester of 2015. Seven hundred and seventy-one chemistry students from a higher education university completed one of nine treatments in this 3x3 research design. Data collection took place over four class periods. The study revealed that learning outcomes were not affected by the mobile screen display size or orientation, nor was working memory. However, user perception was affected by the screen display size of the device, and results indicated that participants in the sample felt laptop screens were more acceptable for accessing the digital chemistry text than smartphone screens by a small margin. The study also found that neither learning outcomes, nor working memory was affected by the text segmentation viewed. Though user perception was generally not affected by text segmentation, the study found that for perceived ease of use, participants felt medium text segments were easier to learn from than either continuous or small test segments by a small margin. No interaction affects were found between mobile devices and text segmentation. These findings challenge the findings of some earlier studies that laptops may be better for learning than smartphones because of screen size, landscape orientation is better for learning than portrait orientation in small screen mobile devices, and meaningful text segments may be better for learning than non-meaningful, non-segmented, or overly segmented text. The results of this study suggest that customizing the design to the smartphone screen (as opposed to a one-size-fits-all approach) improves learning from smartphones, making them equal to learning from laptops in terms of learning outcomes and cognitive load, and in some cases, user perspective.
Following the familiar : the effects of exposure and gender on follow intent and credibility of journalists on Twitter
(2017-05-09) Boulter, Trent Royce; Coleman, Renita; Chen, Gina; Eastin, Matthew; Sylvie, George; West, Kate
This dissertation examines the effect of mere exposure and journalists’ gender on the credibility and follow intent of journalists on Twitter. Through the use of controlled experiments it was found that both exposure and journalists’ gender does significantly impact the credibility and follow intent enjoyed by journalists. This indicates the need that practicing journalists have to strategically consider their level of activity on Twitter, and how it can be used to strengthen their position as an information source in the current media environment. Results also suggest that elaboration and credibility serves as mediators for the effects discovered. Also established was the perception of female journalists being more credible and having a higher likelihood of being adopted as an information source than their male counterparts. Future avenues of research are discussed.
Inclusive learning with assistant chatbot in massive open online courses : examining students’ perceptions, utilizations, and expectations
(2024-05) Han, Songhee; Liu, Min, Ed. D.; Min Kyung Lee; Grace MyHyun Kim; Xiaofen Hamilton
This study examines students’ learning experiences with an assistant chatbot in professional development MOOCs designed for journalists. Utilizing a mixed-methods approach, it focuses on the students’ learning experience’s sub-domains, such as social presence, teaching and cognitive presence, self-regulation, ease of use, and behavioral intention. Employing the Community of Inquiry (CoI) framework and the Technology Acceptance Model (TAM), the study first assesses the impact of demographics like age, gender, region, and native language on these learning experiences. The study revealed that age and gender had no significant influence on learning experiences, while geo-cultural regions showed variations, particularly in social presence and, teaching and cognitive presence. Socioeconomic regions demonstrated more notable differences, especially between lower-middle and high-income areas. However, the native language did not significantly influence learning experiences. Second, structural equation modeling (SEM) validated several hypothesized relationships, highlighting the positive impact of self-regulation on various other learning domains. Interestingly, teaching and cognitive presence did not significantly influence behavioral intention, nor was there a significant relationship between behavioral intention and use time. Age and socioeconomic region factors were identified as full moderators, while gender was a partial moderator from multigroup SEM results. Third, an extensive analysis of student interactions with the chatbot was conducted using various data sources. This analysis revealed eight key topics of chatbot interactions and showed predominantly neutral sentiments in the chatbot text logs. However, survey and interview data indicated a generally positive perception of the chatbot, especially noting its operational effectiveness and ease of use. Sentiments varied across socioeconomic regions, with more positive feedback from lower-income regions, while those from higher-income regions had higher expectations. The study also observed differences in navigational patterns between chatbot users and non-users in the course. Chatbot users exhibited more diverse navigations, indicating deeper engagement with course materials and a higher completion rate. In contrast, non-users followed a more structured progression, mainly relying on the predefined course path. Finally, the study highlighted students’ expectations for the chatbot, emphasizing the need for improvements in response accuracy, diversity, and additional capabilities like multi-language support. The findings emphasize the role of demographic variables in shaping student interactions with chatbots in MOOCs and suggest that modifying chatbot responses for inclusiveness could be key in meeting diverse student needs. The implications include that adhering to Universal Design for Learning principles, empowered by current advancements in AI-based chatbot technology, and informed by the CoI and TAM, could better address the diverse needs in MOOCs, especially in chatbot-enhanced learning environments.