D
Dezhi Wu
Researcher at University of South Carolina
Publications - 74
Citations - 1291
Dezhi Wu is an academic researcher from University of South Carolina. The author has contributed to research in topics: Computer science & Time management. The author has an hindex of 15, co-authored 60 publications receiving 1077 citations. Previous affiliations of Dezhi Wu include Sewanee: The University of the South & Southern Utah University.
Papers
More filters
Journal ArticleDOI
Predicting learning from asynchronous online discussions
Dezhi Wu,Starr Roxanne Hiltz +1 more
TL;DR: The results indicate that online discussions do improve students’ perceived learning and some implications for improving online discussions and for future research plans are presented.
Journal ArticleDOI
Computer-supported team-based learning: The impact of motivation, enjoyment and team contributions on learning outcomes
TL;DR: The study results indicate that motivation influences the relationship between team interactions and perceived learning, with the implication that students who perceive that the team interactions are adding value to their education will better enjoy learning and will experience higher-level learning outcomes.
Journal ArticleDOI
Mobile collaborative learning
Iris Reychav,Dezhi Wu +1 more
TL;DR: Analysis of the MCL process and learning impact with mobile tablets in both individual and group settings found performance and satisfaction with texts is higher with mobile groups, while videos are more influential for individual learning.
Proceedings ArticleDOI
A framework for classifying personalization scheme used on e-commerce Websites
TL;DR: An algorithm for classifying Web sites into high, medium and low personalization support is developed and applied to a set of well-known Web sites such as amazon.com.
Journal ArticleDOI
Are your users actively involved? A cognitive absorption perspective in mobile training
Iris Reychav,Dezhi Wu +1 more
TL;DR: The study findings indicate that the cognitive absorption plays a significant role in affecting users' deep involvement, which in turn impacts training outcomes, which is a major contributor to perceived learning.