Research

Computer Science Education

My research in CS Ed has three main threads: academic integrity, collaboration, and tools and student success.

Academic Integrity

Academic integrity is usually discussed in terms of stopping or deterring cheating. We are instead interested in positive ways to promote academic integrity, through course policies, classroom interactions, and building a sense of community. I have had multiple discussions with students in courses on the practices that would help foster an atmosphere of honesty; we have tested a few examples including flexible collaboration policies and relaxed late penalites.

Citations

Brunelle, N., and Hott, J. R. March 2020. Ask Me Anything: Assessing Academic Dishonesty (Poster). In Proceedings of the 51st ACM Technical Symposium on Computing Science Education (SIGCSE), Portland, OR.
https://doi.org/10.1145/3328778.3372658

Brunelle, N., and Hott, J. R. March 2020. Fix the Course, Not the Student: Positive Approaches to Cultivating Academic Integrity (Birds of a Feather Session). In Proceedings of the 51st ACM Technical Symposium on Computing Science Education (SIGCSE), Portland, OR.
https://doi.org/10.1145/3328778.3372535

Collaboration

How do students collaborate? How can we foster healthy collaboration while maintaining academic integrity? We are looking to uncover how students collaborate if given freedom to choose and change their collaborators throughout the semester. We anticipate that this flexibility will help reduce cheating by promoting a sense of community. Our initial results, published in the 2021 SIGCSE Technical Symposium suggests that encouraging students to work in groups of 4-5 produces the best learning outcomes.

Citations

Lin, X., Connors, J., Lim, C., Hott, J. R. March 2021. How Do Students Collaborate? Analyzing Group Choice in a Collaborative Learning Environment. In Proceedings of the 52nd ACM Technical Symposium on Computing Science Education (SIGCSE), to appear.

Tools and Student Success

Citations

Thinnyun, A., Lenfant, R., Pettit, P., and Hott, J. R. March 2021. Gender and Engagement in CS Courses on Piazza. In Proceedings of the 52nd ACM Technical Symposium on Computing Science Education (SIGCSE), to appear.

Hott, J. R., Basit, N., Pettit, R., and Stone, D.. 2020. CS2 Graphical Photo Library Project. NCWIT EngageCSEdu.
https://www.engage-csedu.org/find-resources/cs2-graphical-photo-library-project

Evolving Networks

Network analysis, especially social network analytics, has become widespread due to the growing amount of linked data available. Many researchers have started to consider evolving networks, i.e. Time-Varying Graphs (TVGs), to begin to understand how these networks change over time. In this dissertation, we expand on current practice in three directions: we define a new concept of “node-identity class” to describe different “lenses” over an evolving network, we develop sampling methods to produce representative static graphs over a network as it evolves, and we utilize social network metrics to produce distributions characterizing the dynamics of the network’s evolution. By combining these different techniques, we uncover a change effect in metric value due to network activity across sampling methods and window sizes, and produce a differential measure D(G) that helps signal possibly significant network evolution. We evaluate these techniques on synthetically-generated datasets with prescribed dynamics to show their effectiveness at capturing and depicting those events. We then apply our techniques to analyze three real-world applications: the Nauvoo Marriage Project, consisting of an evolving Mormon marital network in mid-1800s Nauvoo, IL; the Social Networks and Archival Context Project’s historical social-document network; and an ArXiv co-authorship network. In each case, we were able to depict the network’s dynamics, highlight periods of network activity for further investigation, and guide domain-specific researchers to new insights. For the Nauvoo Marriage Project, through a comparison of the network across identity lenses, our metrics depicted an increased centrality under the patriarchal lens compared with that of the matriarchal lens. Indeed, the rapidity with which the patriarchal centrality “rebounds” suggests a desire of the Nauvoo community to form a strong patriarchal system.

Read more in my Dissertation

Digital Humanities and Computer Science Applications

Visualizations and Network Analysis of early Mormonism, with Kathleen Flake

Publications and Presentations

Samples and Research Code

Visualizations and Network Analysis with SNAC

Social Networks and Archival Context Project visualizations and analysis through the Institute for Advanced Technology in the Humanities and UVA Library.

Publications

Hott, J. R. March 2020. Using Temporal Network Analysis to Uncover Bias in Collections. code4lib, Pittsburgh, PA.
https://2020.code4lib.org/talks/Using-Temporal-Network-Analysis-to-Uncover-Bias-in-Collections

Hott, J. R., Martin, W. N., and Flake, K. 2018. Visualization of Complex Familial and Social Structures. Electronic Imaging, Burlingame, CA.

Hott, J. R., Martin, W. N., and Flake, K. 2016. Visualizing Dynamics of Complex Familial Structures (Poster). IEEE Information Visualization, Baltimore, MD.

Hott, J. R., Martin, W. N., and Flake, K. 2016. Identity Lenses in Analyzing Evolving Social Structures. Digital Humanities, Krakow, Poland.

Hott, J. R., Martin, W. N., and Flake, K. 2015. Visualizing and Analyzing Identity Classes in Evolving Social Structures. Chicago Colloquium on Digital Humanities and Computer Science, University of Chicago. Chicago, IL.

Hott, J. R., Martin, W. N., and Flake, K. 2015. Evolving Family Structures: Representation and Visualization. Family History Technology Workshop, Brigham Young University. Provo, UT.

Hott, J. R., Martin, W.N., et al. 2014. Evolving Social Structures: Networks with People as the Edges. Digital Humanities Forum, University of Kansas. Lawrence, KS. Best paper award.

Hott, J. R., Brunelle, N., Myers, J., Rassen, J. and shelat, a. 2012. KD-Tree Algorithm for Propensity Score Matching With Three or More Treatment Groups. Technical Report Series. Division of Pharmacoepidemiology And Pharmacoeconomics, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School. Boston, MA.

Noonan, R. E. and Hott, J. R. 2007. A course in software development. In Proceedings of the 38th SIGCSE Technical Symposium on Computer Science Education (Covington, Kentucky, USA, March 07 - 11, 2007). SIGCSE ‘07. ACM Press, New York, NY, 135-139.

Project Archive

k-point Matching using kd-trees and Voronoi diagrams

A Comparison of bug-finding tools

Interesting Course Projects