Category: Publications

Through The Looking Glass: Insights Into Visualization Pedagogy Through Sentiment Analysis Of Peer Review Text

We discuss the construction and application of peer review in two visualization courses from different colleges at the University of South Florida. We then analyze student projects and peer review text via sentiment analysis to infer insights for visualization educators, including the focus of course content, engagement across student groups, student mastery of concepts, course trends over time, and expert intervention effectiveness. Continue reading

Polarity In The Classroom: An Application Leveraging Peer Sentiment Towards Scalable Assessment

In this work, we detail the process by which we create our domain-dependent lexicon and aspect-informed review form as well as our entire sentiment analysis algorithm which provides a fine-grained sentiment score from text alone. We analyze the validity from our corpus of over 6800 peer reviews from nine courses to understand the viability of sentiment in the classroom for increasing the information from and reliability of grading open-ended assignments in large courses. Continue reading

Modeling The Influence Of Visual Density On Cluster Perception In Scatterplots Using Topology

We present a multi-stage user study focusing on 4 factors—distribution size of clusters, number of points, size ofpoints, and opacity of points—that influence cluster identification in scatterplots. From these parameters, we have constructed 2 models, a distance-based model, and a density-based model, using the merge tree data structure from Topological Data Analysis. Our analysis demonstrates that these factors play an important role in the number of clusters perceived, and it verifies that the distance-based and density-based models can reasonably estimate the number of clusters a user observes.

Continue reading

Linesmooth: An Analytical Framework For Evaluating The Effectiveness Of Smoothing Techniques On Line Charts

We present a comprehensive framework for evaluating line chart smoothing methods under a variety of visual analytics tasks. The framework is based on 8 measures of the line smoothing effectiveness tied to 8 low-level visual analytics tasks. We analyze 12 methods coming from 4 commonly used classes of line chart smoothing---rank filters, convolutional filters, frequency domain filters, and subsampling. Continue reading