Inclusive learning with assistant chatbot in massive open online courses : examining students’ perceptions, utilizations, and expectations
| dc.contributor.advisor | Liu, Min, Ed. D. | |
| dc.contributor.committeeMember | Min Kyung Lee | |
| dc.contributor.committeeMember | Grace MyHyun Kim | |
| dc.contributor.committeeMember | Xiaofen Hamilton | |
| dc.creator | Han, Songhee | |
| dc.creator.orcid | 0000-0002-8984-7560 | |
| dc.date.accessioned | 2024-07-17T01:37:19Z | |
| dc.date.available | 2024-07-17T01:37:19Z | |
| dc.date.created | 2024-05 | |
| dc.date.issued | 2024-05 | |
| dc.date.submitted | May 2024 | |
| dc.date.updated | 2024-07-17T01:37:20Z | |
| dc.description.abstract | 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. | |
| dc.description.department | Curriculum and Instruction | |
| dc.format.mimetype | application/pdf | |
| dc.identifier.uri | ||
| dc.identifier.uri | https://hdl.handle.net/2152/126062 | |
| dc.identifier.uri | https://doi.org/10.26153/tsw/52601 | |
| dc.language.iso | English | |
| dc.subject | Community of inquiry | |
| dc.subject | Technology acceptance model | |
| dc.subject | Structural equation modeling | |
| dc.subject | Demographic factor | |
| dc.subject | Sentiment analysis | |
| dc.subject | Topic modeling | |
| dc.subject | Educational data mining | |
| dc.subject | Navigational pattern | |
| dc.subject | Chatbot | |
| dc.subject | Massive open online course | |
| dc.subject | Curriculum | |
| dc.subject | Learning experience | |
| dc.subject | Social presence | |
| dc.subject | Technology-enhanced learning | |
| dc.subject | Learning analytics | |
| dc.subject | Research and development | |
| dc.subject | Univeral design for learning | |
| dc.subject | R & D | |
| dc.title | Inclusive learning with assistant chatbot in massive open online courses : examining students’ perceptions, utilizations, and expectations | |
| dc.type | Thesis | |
| dc.type.material | text | |
| thesis.degree.college | College of Education | |
| thesis.degree.department | Curriculum and Instruction | |
| thesis.degree.discipline | Learning Technologies | |
| thesis.degree.grantor | The University of Texas at Austin | |
| thesis.degree.name | Doctor of Philosophy |
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