Browsing by Subject "Technology-enhanced learning"

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Factors affecting faculty use of technology-enhanced instruction at research universities
(2011-05) Thomas, Carolyn Dianna; Reyes, Pedro, 1954-; Ashcroft, Judy C.; Ovando, Martha N.; Rice-Lively, Mary Lynn; Sharpe, Jr., Edwin R.
In traditional models of university education, students gather in classrooms, listen to instructors lecture on specific topics, and take notes with limited time for interaction. Students are then expected to spend additional time outside of the classroom reading textbooks, completing assignments, and preparing for tests. Instructors are viewed as experts providing information to students in much the same manner that they learned the content as students in college. The advent of the World Wide Web in 1991 allowed a dramatic change in the way students and faculty gather information, conduct research, and publish ideas. Internet-based technologies can transform traditional classroom experiences. Student participation can be increased by promoting group and collaborative learning online, and by the use of tools such as e-mail, discussion boards, and synchronous chat sessions. The objectives of this study were to 1) determine which factors affected faculty members’ use of technology integration in their instruction, and 2) determine what effect the integration of technology had on the role and teaching styles of faculty members. The study employed a mixed-methods research methodology. A survey instrument was developed and sent to faculty members at the five largest, public research institutions. The level of technology integration was calculated on a scale ranging from nonuse to full integration. The scale included practices such as providing online discussion areas, allowing/requiring students to use the Internet for assignments, and using anonymous online surveys to gather student feedback. Faculty members were then asked to elaborate on their answers. After the quantitative and qualitative survey results were analyzed, a select group of faculty members at one campus were interviewed to verify or dispute the findings. The survey results revealed the factors that significantly contributed to the overall level of technology integration were: instructional resources, professional and personal computer use, level of class, gender, title, and type of online teaching experience. One of the most significant factors was the level of instructional resources. Instructors with a facilitative teaching style were most likely to integrate technology into their instruction. This information can inform institutions when creating faculty development programs and budgeting limited resources.
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.