About

John R. Hott is an Associate Professor in the Department of Computer Science at the University of Virginia. His research focuses on CS Education, measuring and analyzing evolving networks, collaborations in Digital Humanities, algorithms, network analysis, theory of computation, data science, and computer architecture.

Research topics

• Computer Science
• Sociology
• Political Science
• Psychology
• Mathematics education
• Pedagogy
• Medicine
• Medical education
• Artificial Intelligence
• Virology
• Multimedia
• Software engineering
• Knowledge management
• Management
• Programming language
• Computer hardware
• Public relations
• World Wide Web
• Mathematics
• Statistics
• Epistemology

Selected publications

• Providing Choice of Programming Language: Student Outcomes in an Algorithms Course

  2026-02-13

  articleOpen access1st authorCorresponding

  Students learn multiple programming languages during their undergraduate studies in Computer Science. In some cases, students learn at least two languages during their first two courses, such as Java and Python. While the transition between languages early in the curriculum is well-studied and usually scripted, little is known about students' language preferences and outcomes when given a choice in later courses. We provided students in a third-in-a-sequence major-required algorithms course the choice of language on each programming assignment (PA). Following a CS1 course in Python and a CS2 course in Java, students were asked to complete their PAs in either Java or Python, given equivalent scaffolding code. We conducted pre-course surveys of language preferences and analyzed the language use and resulting overall performance of 268 students across two semesters. On average, students had more self-reported familiarity and had taken more courses in Java, but felt Python had a better reputation. Additionally, while students tended to program in the language they were more familiar with, over 25% of students completed at least one PA in the other language. In two individual PAs (one per semester), students who used Python scored significantly higher than those using Java. However, there was no statistically significant difference in overall scores across PAs, problem sets, and quizzes based on their chosen PA language. Students also faced similar struggle---i.e., average number of submissions---on PAs regardless of language. Therefore, educators of upper-level courses should not worry about the impact of programming language choices on student outcomes.

  PublisherDOI

• Auto-grading in Computing Education: Perceptions and Use

  2025-02-12 · 2 citations

  articleOpen accessSenior author

  Auto-grading technologies have become increasingly prevalent in computing education, driven by the need to handle growing class sizes and provide timely and effective feedback. We conducted a survey of 44 computer science instructors at various institutions in order to gather instructor experience and use of auto-graders, the features instructors value most, and the challenges and limitations faced when using these tools. We specifically asked about factors such as grading strategies and policies, opinions on existing tools, and other automated grading methods they employ. Our results indicated that instructors prefer tools that offer significant customizability and integration capabilities, with functionality and program output-based grading as the most commonly used approaches. They emphasized the need for integrated auto-grading solutions that include robust core features and prioritize extensibility to better align with pedagogical goals and to support instructors in managing the increasing demands of computer science education. Based on these findings, we conclude that existing solutions should be improved to address instructor-reported preferences and diverse educational needs.

  PublisherDOI

• Escaping the CS Dungeon: Modern College Curricula within and Beyond Computing

  2025-08-21

  articleSenior author
  PublisherDOI

• Student Outcomes When Provided Programming Language Choice in an Algorithms Course

  2025-07-30 · 1 citations

  article1st authorCorresponding
  PublisherDOI

• ASCI: AI-Smart Classroom Initiative

  2025-02-12 · 1 citations

  articleOpen access

  The Artificial Intelligence Smart Classroom Initiative (ASCI) presents a re-imagined set of online course tools, designed primarily to support growing computer science classes. The system has four primary tools: an office hours queue, an automatic student grouping algorithm, a course-specific local large-language model (LLM), and administration tools for detecting students and TAs that need support. These tools interoperate to improve the quality of one another (e.g., LLM conversations support students directly in the office hours queue) and are enhanced by synchronizing data from multiple external sources such as Piazza, Gradescope, and Canvas. The system has been deployed in multiple courses over the past three semesters: initially as a FIFO queue, then supporting manual grouping and smart grouping of office hour attendees, and recently including LLM support. Preliminary results indicate that students who were grouped using the tool were more likely to return to the queue more than twice as often (on average) than those who were not. However, while grouping in office hours has the potential to decrease student wait times, teaching assistants and students tend to favor one-on-one meetings over group meetings. This might be improved in the future with updates to the software, TA training, and incorporation of other supporting tools (e.g., LLM technology). The other, newer, tools will be more thoroughly evaluated in future semesters.

  PublisherDOI

• Analyzing Student Performance with Free Late Submission Days

  2024-03-14 · 3 citations

  article1st authorCorresponding

  We investigate the effects of a flexible late policy on student performance submission behavior across two semesters of a lower-level required course (LL) and an upper-level elective (UL). The first semester late policies in both courses were strict: LL incorporated a 10% penalty per day for two days; UL rarely allowed late submissions. In each course, the late policy was relaxed in the second semester to provide two no-penalty late days for each assignment.

  PublisherDOI

• Office Hours and Online Forum Engagement in Introductory CS Courses

  2024-10-13

  articleSenior author

  This research full paper explores the connection between office hours use and online forum engagement in introductory computer science courses. Office hours (OH) and online question-and-answer (Q&A) forums provide a platform for students to interact with their classmates and instructors. We investigate the relationship between student engagement in an online discussion forum (Piazza) and utilization of office hours across 5 semesters of an introductory CS course. We explored the correlation between Piazza utilization and OH attendance, discerned disparities between in-person and online OH involvement, and analyzed the distinct approaches of men and women in engaging with course resources. We found that active Piazza users visit OH more than inactive Piazza users. More specifically, students who interact above average on Piazza in each metric observed - asks, answers, posts, and views - attend OH more than those who are below average in each metric. Additionally, students who attend OH at least once tend to post, ask, answer, and view posts on Piazza more frequently than those who have never attended OH. This indicates that above average help-seeking students on Piazza and in OH tend to engage with available resources more than those who did not seek help as often. This quantifies how often - and through which methods - students seek help. Based on prior research and our findings, we find it likely that students often begin by seeking answers on Piazza. If they find the response unsatisfactory, they then resort to OH for clarification. We also examined the modality of office hours, comparing in-person and online interactions; there is no significant statistical difference in the number of OH visits between those who attend virtually vs those who attend in person. However, online OH visits tended to take longer than in-person visits. In terms of the relationship between engagement and gender, our findings show that women visit OH more than men, both in person and online, and take longer in their OH visits. These findings emphasize the importance of course engagement resources in assisting with learning while also highlighting factors that affect engagement, such as gender, mode of engagement, and usage of other resources, giving instructors a better understanding of which populations tend to engage with specific course resources.

  PublisherDOI

• Towards More Efficient Office Hours for Large Courses: Using Cosine Similarity to Efficiently Construct Student Help Groups

  2024-03-14 · 2 citations

  article1st authorCorresponding

  As undergraduate enrollment in computer science rises, instructors continue to investigate methods to improve the student experience at scale. One aspect commonly used in courses at scale is queue-driven office hours, in which students join an online queue and meet with teaching assistants on a first-come, first-serve basis (FIFO).

  PublisherDOI

• Project-Based and Assignment-Based Courses: A Study of Piazza Engagement and Gender in Online Courses

  2023-06-29 · 4 citations

  articleOpen access

  Project-based (PB) learning has become increasingly popular in computer science education, particularly as studies have found that the teaching style better prepares students for future careers and improves learning outcomes through increased student engagement. Online forum usage is one measurable component of engagement. In order to study the impact of PB learning on online forum engagement, Piazza usage data from seven online computer science courses at a higher education institution were collected and examined. We analyzed the differences in online forum usage between PB and assignment-based (AB) learning, in addition to differences between men and women in each course type. Specifically, this study builds upon and replicates a previous study on Piazza that measured student engagement, anonymity usage, and peer parity. We found that students in PB courses were less actively engaged in online forums than students in AB courses; they were less likely to ask and answer questions on Piazza but were more likely to view posts and be logged on more days. Across both course types, students posted anonymously a similar amount as a proportion of the total number of questions and answers and experienced a proportionally similar amount of peer parity. Our findings mirror prior results on gender engagement on Piazza. Across both PB and AB courses, women were more engaged, asked and viewed more questions, posted anonymously more frequently, and were less likely to experience peer parity than men.

  PublisherOA PDFDOI

• Providing a Choice of Time Trackers on Online Assessments

  2023-03-02 · 2 citations

  articleOpen access1st authorCorresponding

  Online assessments allow instructors to facilitate exams and quizzes in both virtual and large classes. Having a clear online timer during these assessments is vital to help students manage their time. However, these same timers can be a cause of anxiety, affecting student performance. Our goals were to determine (i) which types of visualizations are currently in use, (ii) which styles of online timer were preferred by students, and (iii) if providing students a choice of timer impacted their performance.

  PublisherOA PDFDOI

Frequent coauthors

• Vincent Berry

  Université de Montpellier

  6 shared

• Jeremiah Blanchard

  University of Florida

  5 shared

• Brian Plancher

  4 shared

• Richard Glassey

  KTH Royal Institute of Technology

  4 shared

• Nada Basit

  University of Virginia

  4 shared

• Raymond Pettit

  University of Virginia

  2 shared

• Mark Zarb

  Robert Gordon University

  2 shared

• David E. Williams

  2 shared

Labs

• John R. Hott's LabPI

  Research in algorithms, collaboration, gender engagement, Wikipedia, underrepresented groups, mystery novels, Twitter data, voting systems, historical networks, and more.

Awards & honors

• ACM@UVA Teacher of the Year 2024
• ACM@UVA Rising Star Faculty 2023
• Raven Fellowship 2015