About

Mark Richard Floryan is an Associate Professor in the Department of Computer Science at the University of Virginia (UVa). He earned his Ph.D. in 2013 from the University of Massachusetts. His research centers on leveraging student data submitted to Intelligent Tutoring Systems (ITS) to facilitate the efficient development of intelligent educational systems. In addition to this core focus, he has interests in Serious Games, Gamification for Education, and Computer Science Education. Throughout his career, Professor Floryan has contributed to advancing educational technology by integrating data-driven approaches to improve learning experiences. He has been recognized for his teaching excellence, receiving the UVa 2026 All-University Teaching Award and the Trigon Engineering Society Thomas E. Hutchinson Faculty Award for his impact as an instructor. Beyond research and teaching, he has taken on leadership roles such as serving as the Organizing Co-Chair of the SIGCSE Technical Symposium Organizing Committee for 2028 and 2029. His recent work includes collaborative publications on AI Smart Classroom Initiative Tools presented at the SIGCSE Technical Symposium.

Research topics

• Computer Science
• Artificial Intelligence
• Psychology
• Political Science
• Operating system
• Applied psychology
• Nursing
• Software engineering
• Genetics
• Medicine
• Biology
• Computer vision
• Engineering

Selected publications

• 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

• Web-based Personalized Laboratories for Engineering Students

  2025-04-02

  articleOpen accessSenior author

  We developed software that provides intelligent hands-on bench-top dynamic help to students as they study in laboratories for introductory circuit analysis.Tutoring help is available at "teachable moments" as opposed to students waiting days or weeks for traditional teacher-graded labs reports.Quantitative and qualitative studies show that using the software leads to improved learning, verbalization and conceptual knowledge and relieves teachers' workload.Students also reported that they enjoyed using the software.

  PublisherOA PDFDOI

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

  2024-03-14 · 2 citations

  article

  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

• DiSCS: A New Sequence Segmentation Method for Open-Ended Learning Environments

  Lecture notes in computer science · 2021 · 1 citations

  • Computer Science
  • Artificial Intelligence
  • Computer Science
  PublisherDOI

• The Model of Gamification Principles for Digital Health Interventions: Evaluation of Validity and Potential Utility

  Journal of Medical Internet Research · 2020 · 59 citations

  1st authorCorresponding
  • Computer Science
  • Applied psychology
  • Computer Science

  BACKGROUND: Although gamification continues to be a popular approach to increase engagement, motivation, and adherence to behavioral interventions, empirical studies have rarely focused on this topic. There is a need to empirically evaluate gamification models to increase the understanding of how to integrate gamification into interventions. OBJECTIVE: The model of gamification principles for digital health interventions proposes a set of five independent yet interrelated gamification principles. This study aimed to examine the validity and reliability of this model to inform its use in Web- and mobile-based apps. METHODS: A total of 17 digital health interventions were selected from a curated website of mobile- and Web-based apps (PsyberGuide), which makes independent and unbiased ratings on various metrics. A total of 133 independent raters trained in gamification evaluation techniques were instructed to evaluate the apps and rate the degree to which gamification principles are present. Multiple ratings (n≥20) were collected for each of the five gamification principles within each app. Existing measures, including the PsyberGuide credibility score, mobile app rating scale (MARS), and the app store rating of each app were collected, and their relationship with the gamification principle scores was investigated. RESULTS: Apps varied widely in the degree of gamification implemented (ie, the mean gamification rating ranged from 0.17≤m≤4.65 out of 5). Inter-rater reliability of gamification scores for each app was acceptable (κ≥0.5). There was no significant correlation between any of the five gamification principles and the PsyberGuide credibility score (P≥.49 in all cases). Three gamification principles (supporting player archetypes, feedback, and visibility) were significantly correlated with the MARS score, whereas three principles (meaningful purpose, meaningful choice, and supporting player archetypes) were significantly correlated with the app store rating. One gamification principle was statistically significant with both the MARS and the app store rating (supporting player archetypes). CONCLUSIONS: Overall, the results support the validity and potential utility of the model of gamification principles for digital health interventions. As expected, there was some overlap between several gamification principles and existing app measures (eg, MARS). However, the results indicate that the gamification principles are not redundant with existing measures and highlight the potential utility of a 5-factor gamification model structure in digital behavioral health interventions. These gamification principles may be used to improve user experience and enhance engagement with digital health programs.

  PublisherOA PDFDOI

• A Preliminary Report on Hands-On and Cross-Course Activities in a College Software Testing Course

  2020 · 4 citations

  • Computer Science
  • Software engineering
  • Computer Science

  This report presents numerous interventions deployed in a college-level course on software testing. The aim of these interventions was to increase interest, motivation, and confidence in software testing among computer science majors. Four hands-on in-class activities (Agile Airplane Testing, Test-Driven Development Activities, Candy Testing, and Bypass Testing) were deployed and are described. In addition, students in the course participated in a cross-course activity in which the students produced tests for younger peers in an introductory (CS2) software development course. Students in the software testing course acted as test engineers while students in the earlier course acted as developers and used the tests provided, interacting with their peers when necessary. Preliminary results are presented. Students generally found the activities to be useful, engaging, and provided positive feedback. Developers in the earlier software development course produced more correct code when using test suites provided by upperclassmen, and survey results show small but positive gains in student interest and confidence in software testing.

  PublisherDOI

• Evolving Expert Knowledge Bases: Applications ofCrowdsourcing and Serious Gaming to AdvanceKnowledge Development for Intelligent TutoringSystems

  Scholarworks (University of Massachusetts Amherst) · 2019-04-11 · 1 citations

  book-chapterOpen access1st authorCorresponding

  This dissertation presents a novel effort to develop ITS technologies that adapt by observing student behavior. In particular, we define an evolving expert knowledge base (EEKB) that structures a domain's information as a set of nodes and the relationships that exist between those nodes. The structure of this model is not the particularly novel aspect of this work, but rather the model's evolving behavior. Past efforts have shown that this model, once created, is useful for providing students with expert feedback as they work within our ITS called Rashi. We present an algorithm that observes groups of students as they work within Rashi, and collects student contributions to form an accurate domain level EEKB. We then present experimentation that simulates more than 15,000 data points of real student interaction and analyzes the quality of the EEKB models that are produced. We discover that EEKB models can be constructed accurately, and with significant efficiency compared to human constructed models of the same form. We are able to make this judgment by comparing our automatically constructed models with similar models that were hand crafted by a small team of domain experts.\nWe also explore several tertiary effects. We focus on the impact that gaming and game mechanics have on various aspects of this model acquisition process. We discuss explicit game mechanics that were implemented in the source ITS from which our data was collected. Students who are given our system with game mechanics contribute higher amounts of data, while also performing higher quality work. Additionally, we define a novel type of game called a knowledge-refinement game (KRG), which motivates subject matter experts (SMEs) to contribute to an already constructed EEKB, but for the purpose of refining the model in areas in which confidence is low. Experimental work with the KRG provides strong evidence that: 1) the quality of the original EEKB was indeed strong, as validated by KRG players, and 2) both the quality and breadth of knowledge within the EEKB are increased when players use the KRG.

  PublisherDOI

• Effects of a Pathfinding Program Visualization on Algorithm Development

  2019-02-22 · 7 citations

  article

  Program Visualizations (PVs) have been used as educational tools to allow students to visually inspect the runtime behavior of their code. However, many of these systems act as low-level visual debuggers not high-level abstractions of program behavior. Additionally, evaluations of these systems tend to focus more on student engagement or opinion in using the system and not on artifacts produced using the system. This paper discusses the effectiveness of a PV developed to aide students in an undergraduate Artificial Intelligence class on a pathfinding homework assignment. Students in 4 semesters of the course were tasked to develop pathfinding algorithms for an agent to navigate worlds in cases of both certain and uncertain world information. Students in 2 semesters of the course were given access to a PV that allowed them to see a visual representation of their agent navigating the world in either information condition. The final agents developed by these students were compared with those developed by students who never received the PV. Comparisons were made on the performance of these agents in both cases of uncertain and certain world information on several test worlds. Student written reports for the Experimental condition were also analyzed. The results showed significant differences in the performance of the algorithms developed in both certain and uncertain world information. Student reflections on using the PV within the written reports provide insight into how the PV informed the design and development of their submission.

  PublisherDOI

• Principles of gamification for Internet interventions

  Translational Behavioral Medicine · 2019-04-04 · 42 citations

  article1st authorCorresponding

  Gamification is a popular method used to add entertaining and appealing dimensions to nongaming activities. Researchers of technology-based behavioral and mental-health-focused interventions have shown considerable interest in gamification to enhance engagement and adherence. There have been a number of gamification frameworks proposed, each with differences in focus but with overlapping similarities. A review of these frameworks highlight critical issues in gamification-lack of clear definitions, standards, and a need for an overarching model for applying gamification, rather than simply describing gamification. These issues leave researchers challenged to apply gamification to its full potential. This paper explores gamification as a construct and endeavors to define its core features. A useful way of evaluating the potential utility of gamification features in the context of an intervention is by distinguishing between exogenous applications of gamification (layering game mechanics externally upon a system) and endogenous application of gamification (developing mechanics intrinsic to the given experience). By then comparing and contrasting six gamification frameworks, components are identified that lay at the intersection and a theoretical model is proposed. A theory-driven set of gamification principles, organized into four categories, is developed and presented. Of particular interest is the utilization of this model as it relates to behavioral and mental-health-focused Internet-based interventions. To demonstrate the potential of this gamification framework, the generated principles are overlaid onto the established Model for Internet Interventions, extending it, and providing a more concrete foundation for researchers of Internet interventions. The presented model will assist researchers and developers who are interested in applying gamification to Internet interventions.

  PublisherDOI

• The Model of Gamification Principles for Digital Health Interventions: Evaluation of Validity and Potential Utility (Preprint)

  2019-10-04

  preprintOpen access1st authorCorresponding

  <sec> <title>BACKGROUND</title> Although gamification continues to be a popular approach to increase engagement, motivation, and adherence to behavioral interventions, empirical studies have rarely focused on this topic. There is a need to empirically evaluate gamification models to increase the understanding of how to integrate gamification into interventions. </sec> <sec> <title>OBJECTIVE</title> The model of gamification principles for digital health interventions proposes a set of five independent yet interrelated gamification principles. This study aimed to examine the validity and reliability of this model to inform its use in Web- and mobile-based apps. </sec> <sec> <title>METHODS</title> A total of 17 digital health interventions were selected from a curated website of mobile- and Web-based apps (PsyberGuide), which makes independent and unbiased ratings on various metrics. A total of 133 independent raters trained in gamification evaluation techniques were instructed to evaluate the apps and rate the degree to which gamification principles are present. Multiple ratings (n≥20) were collected for each of the five gamification principles within each app. Existing measures, including the PsyberGuide credibility score, mobile app rating scale (MARS), and the app store rating of each app were collected, and their relationship with the gamification principle scores was investigated. </sec> <sec> <title>RESULTS</title> Apps varied widely in the degree of gamification implemented (ie, the mean gamification rating ranged from 0.17≤m≤4.65 out of 5). Inter-rater reliability of gamification scores for each app was acceptable (κ≥0.5). There was no significant correlation between any of the five gamification principles and the PsyberGuide credibility score (&lt;i&gt;P&lt;/i&gt;≥.49 in all cases). Three gamification principles (supporting player archetypes, feedback, and visibility) were significantly correlated with the MARS score, whereas three principles (meaningful purpose, meaningful choice, and supporting player archetypes) were significantly correlated with the app store rating. One gamification principle was statistically significant with both the MARS and the app store rating (supporting player archetypes). </sec> <sec> <title>CONCLUSIONS</title> Overall, the results support the validity and potential utility of the model of gamification principles for digital health interventions. As expected, there was some overlap between several gamification principles and existing app measures (eg, MARS). However, the results indicate that the gamification principles are not redundant with existing measures and highlight the potential utility of a 5-factor gamification model structure in digital behavioral health interventions. These gamification principles may be used to improve user experience and enhance engagement with digital health programs. </sec>

  PublisherDOI

Frequent coauthors

• Beverly Park Woolf

  11 shared

• Toby Dragon

  Ithaca College

  5 shared

• Philip I. Chow

  University of Virginia

  3 shared

• Nicholas Lytle

  Georgia Institute of Technology

  3 shared

• Lee M. Ritterband

  University of Virginia

  3 shared

• Nada Basit

  University of Virginia

  2 shared

• Tom Murray

  Graphcore (United Kingdom)

  2 shared

• Stephen M. Schueller

  University of California, Irvine

  2 shared

Awards & honors

• All-University Teaching Award 2026
• Trigon Engineering Society Thomas E. Hutchinson Faculty Awar…
• Hartfield Jefferson Scholars Teaching Prize 2016-2017
• ACM Professor of the Year, UVa 2013-2014
• Best Paper Award Nomination; ASEE Zone 1 Conference 2014