Volume-volume matching

Oftentimes in visualization, the goal of using volume datasets is not just to visualize them but also to analyze and compare them. In order to compare the two volumes, we cannot take all the voxels into consideration. The size of a typical volume data set is quite large (maybe a billion or more voxels), thus it is not feasible to compare all these voxels to all in the other volume set. This project uses optimization methods to find the best orientation for aligning a second volume data set with a first. Using these methods, we will be able to determine how well one medical data volume matches against a "normally-developed" volume of the same type

Integrating Learning Analytics to Measure Message Quality in Large Online Conversations

Research on computer-supported collaborative learning (CSCL) often employs content analysis as an approach to investigate message quality in asynchronous online discussions using systematic message-coding schemas. Although this approach helps researchers count the frequencies by which students engage in different socio-cognitive actions, it does not explain how students articulate their ideas in categorized messages. This study investigates the effects of a recommender system on the quality of students’ messages from voluminous discussions. We employ learning analytics to produce a quasi-quality index score for each message. Moreover, we examine the relationship between this score and the phases of a popular message-coding schema. Empirical findings show that a custom CSCL environment extended by a recommender system supports students to explore different viewpoints and modify interpretations with higher quasi-quality index scores than students assigned to the control software. Theoretical and practical implications are also discussed

Volume-Volume Matching

Abstract approved