School of Engineering Department of Computer Science and Engineering 139 Paper-based VR/AR Interaction Supervisor: MA Xiaojuan / CSE Student: TSANG Cheuk Nam / COMP Course: UROP1100, Fall Augmented reality is one of the new frontiers for bridging the digital world and the real world. One of the more immersive approaches utilizes an optical see-through head mounted display as an extension of paperbased interactions, but this approach offers limited immersion due to blending of light from both the projected image and environmental source which can lower immersion and legibility. In this study, we summarized the common limitations of optical see-through displays and designed a color compensation algorithm to rectify the effect of color blending on color accuracy. We further extend the comparison between potential approaches of color correction and their computational cost when implemented with constrained resources on mobile AR devices such as the Microsoft HoloLens. Predicting Student Performance on an E-Learning Platform Supervisor: MA Xiaojuan / CSE Student: ZHANG Yuanhao / COMP Course: UROP1100, Fall Community Question Answering websites (CQAs) such as Stack Exchange, Quora, Zhihu, and Answers.com serve as online knowledge hubs where people can ask their questions and answer one another. Therefore, attracting users' attention to make more and better contributions is crucial to ensure these websites' sustainability. Gamification, the use of game design elements in non-game contexts, is one way to attract users. In this paper, we investigate Winter Bash (WB), a seasonal gamification scheme on Stack Exchange, to understand the barriers that impede users from fully engaging with this type of gamification. We run a thematic analysis on text-based contents from Stack Exchange and summarize the main barriers. Finally, after constructing a coding scheme, we try to decipher it and provide some suggestions for CQA users and website to avoid or mitigate such barriers. Predicting Student Performance on an E-Learning Platform Supervisor: MA Xiaojuan / CSE Student: ZHANG Yuanhao / COMP Course: UROP2100, Spring Stack Overflow(SO) is an online Community Question Answering website (CQA) that provides a platform for programmers to exchange knowledge in the computer science field. In our study, we conduct a quantitative research to explore the relationship between the attractiveness of a post on SO and three different features of its title: title length, the number of punctuation marks in the title, the frequency of less common words in the title. With a rich dataset of 40675 posts, we define the features and run hypothesis tests, the result of which presents evidence that all of the three title features are correlated with the posts' attractiveness. Further, we find that linear regression is the best model to predict the attractiveness of a post based on the three features.