Research & Scholarship

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Interests

Statistics and Data Science Education
Educational Assessment

Selected Publications

Hu, A., Hatfield, N. J., & Beckman, M. D. (in review). Exploring Individuals’ Computational Thinking with Data.

Beckman, Burke, Fiochetta, Fry, Lloyd, Patterson, Tang, (2024). Developing Consistency Among Undergraduate Graders Scoring Open-Ended Statistics Tasks. Preprint URL: https://arxiv.org/abs/2410.18062

Phadke, S., Beckman, M. D., & Lock Morgan, K. (2024). Examining the role of context in statistical literacy assessment. Statistics Education Research Journal, 23(1). https://doi.org/10.52041/serj.v23i1.529

Li, Z., Lloyd, S., Beckman, M. D., & Passonneau, R. J. (2023). Answer-state Recurrent Relational Network (AsRRN) for Constructed Response Assessment and Feedback Grouping. Findings of the Conference on Empirical Methods in Natural Language Processing (EMNLP) 2023.

Lloyd, S. E., Beckman, M., Pearl, D., Passonneau, R., Li, Z., & Wang, Z. (2022). Foundations for AI-Assisted Formative Assessment Feedback for Short-Answer Tasks in Large-Enrollment Classes. In Proceedings of the eleventh international conference on teaching statistics. Rosario, Argentina.

Beckman, M. D., Cetinkaya-Rundel, M., Horton, N. J., Rundel, C. W., Sullivan, A. J., & Tackett, M. (2021). Implementing Version Control With Git and GitHub as a Learning Objective in Statistics and Data Science Courses. Journal of Statistics and Data Science Education, 29(1). URL: https://doi.org/10.1080/10691898.2020.1848485

Beckman, M. D., delMas, R. (2018). Statistics students’ identification of inferential model elements within contexts of their own invention. ZDM–International Journal of Mathematics Education, 50(7).

Beckman, M. D., delMas, R., and Garfield, J. (2017). Cognitive transfer outcomes for a simulation-based introductory statistics curriculum. Statistics Education Research Journal, 16(2), pp. 419-440. URL: https://iase-web.org/documents/SERJ/SERJ16(2)_Beckman.pdf

Presentations

Recent & Upcoming

Click markers for detail.

Sponsored Research

Diversity Track: Comparative analysis of AI models and human judgments for evaluation of student writing with and without non-normative use of English language.

• Sponsor: Penn State Center for Socially Responsible Artificial Intelligence (Award #025243)
• Role: Principle Investigator
• Other Senior Personnel: Rebecca Passonneau (co-PI); ChanMin Kim (co-PI); Dennis Pearl (co-PI)
• Amount: $24,620 Subaward
• Dates: 2025 - 2026

Project CLASSIFIES (Common Language Assessment in Studying Statistics with Instructional Feedback and Increased Enrollment Scalability)

• Sponsor: NSF Improving Undergraduate STEM Education (Award #2236150)
• Role: Principal Investigator
• Other Senior Personnel: Dennis Pearl (co-PI; Penn State University); Rebecca Passonneau (co-PI; Penn State University)
• Amount: $300,000
• Dates: 2023 - 2025 (expected)

MCA: Cultivating a Leader in Statistics and Data Science Education through Strategic Partnerships

• Sponsor: NSF Mid-Career Advancement; Directorate for STEM Education (Award #2423026)
• Role: Senior Personnel (Mentoring Partner)
• Other Senior Personnel: Jennifer Broatch (PI; Arizona State University)
• Amount: $88,273 Subaward
• Dates: 2024 - 2027 (expected)

Fostering Engaged Team-Based Learning in Asynchronous Online and Hybrid Learning Environments

• Sponsor: NSF Improving Undergraduate STEM Education (Award #2417294)
• Role: co-Investigtor
• Other Senior Personnel: Sy-Miin Chow (PI; Penn State University); Prabhani Kuruppumullage Don (Co-PI; Penn State University)
• Amount: $400,000
• Dates: 2024 - 2026 (expected)

Tombros Fellowship: Comprehensive Assessment of Curricular Goals for the Undergraduate Statistics Major

• Sponsor: Penn State Center of Excellence for Science Education
• Role: Principal Investigator (i.e., Tombros Fellow)
• Amount: $20,279.45
• Dates: 2017 - 2018

Research Advising (Graduate)

Elizabeth Eisenhauer (PhD; 2022)

• Dissertation Title: Advances in stochastic models for animal movement and assessment of attitudes toward probability
• co-advisor: Ephraim Hanks
• Employment: Statistician; Westat

Sayali Phadke (PhD; 2022)

• Dissertation Title: Dissertation: Measurement, assessment, and improvement of statistical literacy in relevant contexts
• co-advisor: David Hunter
• Employment: Assistant Professor of Statistics; Penn State Behrend

Alyssa Hu (PhD; Expected 2025)

• research interests: computational thinking, assessment tools for program evaluation, educational measurement.

Susan Lloyd (PhD; Expected 2025)

• research interests: innovative approaches to educational assessment implementation and topological data analysis, topological data analysis.
• co-advisor: Nicole Lazar

Elle Tang (MS; Expected 2025)

• research interests: Interests include statistical genomics and evaluation of linguistic bias in educational assessment.
• co-advisor: Qunhua Li

Recent Posters with Students

(student collaborators in bold)

• Hu, A., Hatfield, N., J., Beckman, M., D. (2024). Exploring Individuals’ Computational Thinking with Data. Electronic Conference on Teaching Statistics. Virtual.

• Lloyd, S., Beckman, M., D. (2024). Feasibility Study for Developing and Validating an Instrument that Includes Interactions Among Learning Objectives Related to Confidence Intervals. Electronic Conference on Teaching Statistics. Virtual.

• Lloyd, S., Beckman, M. (2023). Measuring Statistical Literacy Surrounding Confidence Intervals. USCOTS Research Satellite. University Park, PA.

• Lloyd, S. E., Beckman, M., Pearl, D., Passonneau, R., Li, Z., & Wang, Z. (2022). Foundations for NLP-Assisted Formative Assessment Feedback for Short-Answer Tasks in Large-Enrollment Classes. Joint Statistical Meetings. Washington, D. C.

• Lloyd, S. E., Beckman, M., Pearl, D., Passonneau, R., Li, Z., & Wang, Z. (2022). Foundations for AI-Assisted Formative Assessment Feedback for Short-Answer Tasks in Large-Enrollment Classes. Electronic Conference on Teaching Statistics. Virtual.

• Eisenhauer, E., Beckman, M. (2021). Survey of Attitudes toward Probability. US Conference on Teaching Statistics. Virtual (COVID-19). URL: https://www.causeweb.org/cause/uscots/uscots21/th-06-survey-attitudes-toward-probability

• Gong, Y., Pearl, D., Beckman, M., Hatfield, N., Zhang, S. (2021). The value of Log files of students’ interaction with software applications: Replicating a Bayesian Network analysis across multiple years data. US Conference on Teaching Statistics. Virtual (COVID-19). URL: https://www.causeweb.org/cause/uscots/uscots21/program/posters

• Hu, A., Beckman, M., Pearl, D. (2021). Program Evaluation Through Alignment of Student Perceptions, Outcomes Assessment, and Faculty Perceptions in an Undergraduate Statistics Major. US Conference on Teaching Statistics. Virtual (COVID-19). URL: https://www.causeweb.org/cause/uscots/uscots21/tu-01-program-evaluation-through-alignment-student-perceptions-outcomes-assessment>

• McNamara, A., Legacy, C., Rao, V., delMas, R., Zieffler, A., Beckman, M., Basner Butler, E. (2021). Computing in the Statistics Curriculum: Lessons Learned from the Educational Sciences. US Conference on Teaching Statistics. Virtual (COVID-19). URL: https://www.causeweb.org/cause/uscots/uscots21/tu-03-computing-statistics-curriculum-lessons-learned-educational-sciences

• Yan, W., Pearl, D., Beckman, M. (2019). Assessing Student Conceptual Competencies using Bayesian Networks. US Conference on Teaching Statistics. University Park, PA.

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Updated: December 2024