CAD course creative projects necessitate subjective feedback. In academia, peer review is a widely used instrument to gather diverse and timely feedback which stimulates learning and engagement in students who review one another. To date, however, no effort to summarize and score subjective content from peer review text via sentiment analysis has been attempted in an educational setting, including CAD courses many of which naturally employ a project-based architecture. This is perhaps due in part to a lack of specifically tuned tools. Towards meeting this need, we introduce a new lexicon compiled from actual peer review text, implemented specifically in a CAD-course context, and compare it to other publicly available lexicons. HeLPS, our domain-dependent lexicon, performed more concisely and accurately in our CAD courses and consistently tagged high-quality positive and negative sentiment with a lexicon a fraction of the size of others. Both qualitative and quantitative evidence suggest that HeLPS is the preferred option for identifying subjective opinion towards CAD course projects.
A Domain-Dependent Lexicon to Augment CAD Peer Review
Z. Beasley and L. Piegl
Computer-Aided Design and Applications, Vol 18, No 1, pp. 186-198, 2021
We present a comprehensive framework for evaluating line chart smoothing methods under a variety of visual analytics tasks. Line charts are commonly used to visualize a series of data samples. When the number of samples is large, or the data are noisy, smoothing can be applied to make the signal more apparent. However, there are a wide variety of smoothing techniques available, and the effectiveness of each depends upon both nature of the data and the visual analytics task at hand. To date, the visualization community lacks a summary work for analyzing and classifying the various smoothing methods available. In this paper, we establish a framework, based on 8 measures of the line smoothing effectiveness tied to 8 low-level visual analytics tasks. We then analyze 12 methods coming from 4 commonly used classes of line chart smoothing-rank filters, convolutional filters, frequency domain filters, and subsampling. The results show that while no method is ideal for all situations, certain methods, such as Gaussian filters and Topology-based subsampling, perform well in general. Other methods, such as low-pass cutoff filters and Douglas-Peucker subsampling, perform well for specific visual analytics tasks. Almost as importantly, our framework demonstrates that several methods, including the commonly used uniform subsampling, produce low-quality results, and should, therefore, be avoided, if possible.
LineSmooth: An Analytical Framework for Evaluating the Effectiveness of Smoothing Techniques on Line Charts
P. Rosen, G.J. Quadri
IEEE Transactions on Visualization and Computer Graphics (VAST 2020)
Scatterplots are used for a variety of visual analytics tasks, including cluster identification, and the visual encodings used on a scatterplot play a deciding role on the level of visual separation of clusters. For visualization designers, optimizing the visual encodings is crucial to maximizing the clarity of data. This requires accurately modeling human perception of cluster separation, which remains challenging. We present a multi-stage user study focusing on 4 factors-distribution size of clusters, number of points, size of points, 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. Finally, we demonstrate how these models can be used to optimize visual encodings on real-world data.
Modeling the Influence of Visual Density on Cluster Perception in Scatterplots Using Topology
G.J. Quadri, P. Rosen
IEEE Transactions on Visualization and Computer Graphics (InfoVis 2020)
Congratulations to Tanmay ‘TJ’ Kotha who successfully defended his MS thesis, titled “Establishing Topological Data Analysis: A Comparison of Visualization Techniques”.
TJ has already joined Amazon as a Software Engineer.
Congratulations to Zach Beasley who successfully defended his dissertation, “Sentiment Analysis in Peer Review”, on May 29.
Line charts are commonly used to visualize a series of data values. When the data are noisy, smoothing is applied to make the signal more apparent. Conventional methods used to smooth line charts, e.g., using subsampling or filters, such as median, Gaussian, or low-pass, each optimize for different properties of the data. The properties generally do not include retaining peaks (i.e., local minima and maxima) in the data, which is an important feature for certain visual analytics tasks. We present TopoLines, a method for smoothing line charts using techniques from Topological Data Analysis. The design goal of TopoLines is to maintain prominent peaks in the data while minimizing any residual error. We evaluate TopoLines for 2 visual analytics tasks by comparing to 5 popular line smoothing methods with data from 4 application domains.
TopoLines: Topological Smoothing for Line Charts
P. Rosen, A. Suh, C. Salgado, M. Hajij
EuroVis Short Papers
Peer review is a widely utilized pedagogical feedback mechanism for engaging students, which has been shown to improve educational outcomes. However, we find limited discussion and empirical measurement of peer review in visualization coursework. In addition to engagement, peer review provides direct and diverse feedback and reinforces recently-learned course concepts through critical evaluation of others’ work. In this paper, we discuss the construction and application of peer review in a computer science visualization course, including: projects that reuse code and visualizations in a feedback-guided, continual improvement process and a peer review rubric to reinforce key course concepts. To measure the effectiveness of the approach, we evaluate student projects, peer review text, and a post-course questionnaire from 3 semesters of mixed undergraduate and graduate courses. The results indicate that course concepts are reinforced with peer review—82% reported learning more because of peer review, and 75% of students recommended continuing it. Finally, we provide a road-map for adapting peer review to other visualization courses to produce more highly engaged students.
Leveraging Peer Feedback to Improve Visualization Education
Z. Beasley, A. Friedman, L. Piegl, P. Rosen
IEEE Pacific Visualization Symposium (PacificVis)
Major League Hacking named CSE students Akash Singh and Jamshidbek Mirzakhalov two of the top 50 hackers. MLH is the official student hackathon league which powers almost 2,000 competitions and each year they choose the top 50 hackers out of nearly 100,000 hackers/mentors/organizers to be in the Top 50 hackers list. This year USF was one of 10 universities to have more than one student featured in the list.
Singh’s first hackathon was MangoHacks, after which he changed his major from biology to computer science. Now, Singh is a research assistant in the USF Graphics and Visualization Lab and the president of Society for Competitive Hackers.
“Hackathons are an experience rather than a competition…We get an environment to test our skills, gain new ones and most importantly meet programmers from all over the world. The experience itself boosts your confidence and introduces you to the world waiting for you once you graduate,” Singh says. In the future he hopes to pursue his PhD in computer science with a concentration in Data Science and Data Visualization. To find out more click here.
Original Story: https://www.usf.edu/engineering/cse/newsroom/02192020-mlh.aspx
Congratulations to Ghulam Jilani Quadri who successfully passed his Major Research Area Presentation, “Modelling Effective Visualization Using Graphical Encoding Perception”, on November 18.
Junyi Tu successfully defended his dissertation, “Efficient Algorithms and Applications in Topological Data Analysis”, on November 7, 2019!
Prof. Tu will be joining the faculty of Salisbury University, where he will teach Computer Science as a Visiting Assistant Professor in the Spring, switching to a tenure-track Assistant Professor in the Fall.
We wish Prof. Tu good fortune on his future career.
See the Salisbury University Computer Science Department to learn more.