Steve Geinitz

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[last name] at msudenver dot edu

github researchgate linkedin googlescholar

Bio/Background

My name is Steve Geinitz and I’m an associate professor of Computer Science at Metropolitan State University of Denver. I hold a PhD in Applied Statistics from the University of Zurich, an MSc in Computer Science, and BSc’s in Computer Science and Mathematics. My professional experience ranges from small start-ups to large global tech companies, but always with an aim of wielding data science and machine learning to improve an organization’s/company’s products and services. This underlying goal continues in education as well, where I am developing and experimenting with novel pedagogical techniques to improve student outcomes. A full CV can be found here.

Teaching

Spring 2026

  • CS 3120: Introduction to Machine Learning
  • DSML 4220: Deep Learning

Fall 2025

  • CS 3120: Introduction to Machine Learning
  • CS 4050: Algorithms and Algorithm Analysis

Other Courses

  • CS 2050: Computer Science 2 - Data Structures
  • CS 3250: Intro to Software Development Methods and Tools
  • CS 2240: Discrete Structures (Discrete Mathematics for CS)
  • CS 3240: Theory of Computation
  • CS 39AA: Natural Language Processing w/ Deep Learning

Projects/Research

For interested students, there are a few projects available to work on. There may be funding available to work on these depending on the semester, but there is always the opportunity to extend these into a research project. Within MSU, a project can culminate in publication in the Rowdy Scholar, a presentation at the Undergraduate Research Conference, or a publication in an academic journal.

Canvigator: A digital teaching assistant for Canvas

Canvigator is an evolving piece of software built on top of the Canvas LMS that provides additional utilities and enhances tried-and-true instructional methods. Longer-term development goals include the integration of machine learning models that can provide personalized follow-up assessments, predict student performance trajectories, and provide the instructor with detailed insights about how students are progressing through the course content.

  • Benefits and usage examples: By automating collaborative quizzes, intelligently assigning student pairs, generating tailored follow-up questions, and analyzing quiz outcomes to highlight where students may need support, Canvigator helps reduce administrative overhead and provides richer visibility into class learning patterns.

  • Skills a student researcher needs to contribute: Useful preparation includes Python programming and basic software-engineering practices. Experience with machine learning and/or data analysis is helpful but not required. More importantly, a curiosity about learning analytics and a willingness to work iteratively on applied software systems is what is needed.

Technology-Enhanced Pedagogical Methods

This line of research investigates and develops scalable, technology-supported extensions of proven teaching strategies such as collaborative learning, peer instruction, and continuous assessment. The goal is to design digital tools and algorithms that help instructors implement these practices more effectively and personalize them to the needs of different learners. Research threads may include modeling student knowledge over time (e.g. knowledge tracing), generating formative assessments, and evaluating how technology influences participation, understanding, and academic outcomes.

  • Benefits and usage examples: These methods can support dynamic student pairings, adaptive quizzes, data-informed study recommendations, and real-time feedback loops between students and instructors. Benefits include improved engagement, more accurate identification of at-risk learners, and enhanced opportunities for students to learn through structured collaboration.

  • Skills a student researcher needs to contribute: A background in statistics or machine learning is valuable, as is familiarity with Python or another language for data analysis. Students interested in educational techniques, human-centered computing, and/or experimentation in educational settings will find natural entry points. A comfort with designing small studies or analyzing data is also useful. Above all, most important is a desire to improve student outcomes and a willingness to commit to carrying out a study from start to finish.

Publications/Presentations

Singh, S., Rajan, R., Geinitz, S., Peprah, K., Jay, S. (under review) Exploring the Pedagogical Potential: An Investigation into Faculty and Students’ Perceptions of Integrating Generative AI in the Classroom).

Geinitz, S. (2026, January). ArguBot Arena: Prompt Engineering a Debate on Responsible AI. The 16th Symposium on Educational Advances in Artificial Intelligence (EAAI-2026); Model AI Assignments Track

Geinitz, S., Rajan, R., Peprah, K., Schmidt, K.S., Singh, S., Jay, S. (2025, November - proceedings in press). Generative AI as a Tool for Learning: Higher Education Student Perceptions and Performance across Disciplines. In International Conference on Intelligent Multimodal Communication and Learning Technologies. Springer LNCS.

Geinitz, S. (2025). Improving student learning and socialisation via technology-enhanced collaboration. International Journal of Technology Enhanced Learning (IJTEL).

Geinitz, S. (2024, September). Dynamic Duo: Enhancing Collaborative Learning Through Strategic Student Pairings. In International Conference on Interactive Collaborative Learning (pp. 27-37). Cham: Springer Nature Switzerland.

Geinitz, S. (2023, September). PICA: A Data-Driven Synthesis of Peer Instruction and Continuous Assessment. In Joint European Conference on Machine Learning and Knowledge Discovery in Databases (pp. 3-17). Cham: Springer Nature Switzerland.

Runge, J., Geinitz, S., and Ejdemyr, S. (2020). Experimentation and performance in advertising: An observational survey of firm practices on Facebook. Expert Systems with Applications, 158, 113554.

Geinitz, S., Furrer, R., and Sain, S. R. (2015). Bayesian multilevel analysis of variance for relative comparison across sources of global climate model variability. International Journal of Climatology, 35(3).

Furrer, R., Geinitz, S., and Sain, S. R. (2012). Assessing variance components of general circulation model output fields. Environmetrics, 23(5), 440-450.

Ward, T. J., Palmer, C. P., Houck, J. E., Navidi, W. C., Geinitz, S., and Noonan, C. W. (2009). Community woodstove changeout and impact on ambient concentrations of polycyclic aromatic hydrocarbons and phenolics. Environmental science and technology, 43(14), 5345-5350