Publications

Zeng M-R, Haudek KC, Kaldaras L, Krajcik J. Exploring object detection approaches to analyze students’ drawn scientific models. Proceedings of the 6th Annual Meeting of the International Society of the Learning Sciences. 2026;2. https://2026.isls.org/docs/ICLS%20Volume%202026.pdf.

Franovic CG-C, Kaldaras L, Tang W, Akgun S, Krajcik J, Haudek KC. Designing and validating three-dimensional, learning-progression-aligned cognitive feedback for AI-generation. In: National Association for Research in Science Teaching Annual Conference. Seattle, WA: NARST; 2026.

Kaldaras L, Franovic CG-C, Chu Y, Tang J, Krajcik J, Haudek KC. Training AI To Produce Guiding and Meaningful Feedback To Support Learning and Foster Knowledge-In-Use. In: National Association for Research in Science Teaching Annual Conference. Seattle, WA: NARST; 2026.

Kaldaras L, Franovic CG-C, Tang W, et al. Learning Progression-Guided AI Evaluation to Support Feedback on Multimodal Scientific Modeling Tasks. In: AERA Annual Conference. Los Angeles, CA: AERA; 2026.

Zeng M-R, Haudek KC, Kaldaras L, Krajcik J. Exploring object recognition approaches to analyze students’ draw models. In: National Association for Research in Science Teaching Annual Conference. Seattle, WA: NARST; 2026.

Djagba P, Haudek KC, Franovic CG-C, Kaldaras L. Exploring Data Augmentation and Resampling Strategies for Transformer-Based Models to Address Class Imbalance in AI Scoring of Scientific Explanations in NGSS Classroom. In: National Association for Research in Science Teaching Annual Conference. Seattle, WA: NARST; 2026.

Li T, Haudek KC, Krajcik J. Utilizing Deep Learning AI to Analyze Scientific Models: Overcoming Challenges. Journal of Science Education and Technology. 2025. doi:10.1007/s10956-025-10217-0.

Wang H, Haudek KC, Manzanares A, Romulo C, Royse EA, Azzarello C. Extending a pretrained language model (BERT) using an ontological perspective to classify students’ scientific expertise level from written responses. Disciplinary and Interdisciplinary Science Education Research. 2025;7. doi:10.1186/s43031-025-00149-5.

Kaldaras L, Li T, Djagba P, Haudek KC, Krajcik J. Learning Progression-Guided AI Evaluation of Scientific Models Integrating Writing and Drawing to Support Multi-Modal Knowledge In Use. In: AERA Annual Conference. Denver, CO: AERA; 2025.

Kaldaras L, Li T, Djagba P, Haudek KC, Krajcik J. Learning Progression-Guided AI Evaluation of Scientific Models To Support Diverse Multi-Modal Understanding in NGSS Classroom. In: NARST Annual Conference. National Harbor, MD: NARST; 2025.

Akgun S, Haudek KC, Kaldaras L, Krajcik J. Collaborating with Teachers to Generate ML-Based Feedback: Contextualizing and Developing Meaningful and Relevant Feedback. In: NARST Annual Conference. National Harbor, MD: NARST; 2025.

Li T, Kaldaras L, Haudek KC, Krajcik J. Feedback on Utilizing Deep Learning AI to Analyze Scientific Models. In: NARST Annual Conference. National Harbor, MD: NARST; 2025.

Haudek KC, Zhai X. Examining the Effect of Assessment Construct Characteristics on Machine Learning Scoring of Scientific Argumentation. International Journal of Artificial Intelligence in Education. 2024;34. doi:10.1007/s40593-023-00385-8.

Kaldaras L, Haudek KC, Krajcik J. Employing automatic analysis tools aligned to learning progressions to assess knowledge application and support learning in STEM. International Journal of STEM Education. 2024;11(1). doi:10.1186/s40594-024-00516-0.

Royse EA, Manzanares A, Wang H, et al. FEW questions, many answers: using machine learning to assess how students connect food–energy–water (FEW) concepts. Humanities and Social Sciences Communications. 2024;11. doi:10.1057/s41599-024-03499-z.

Wilson CD, Haudek KC, Osborne JF, et al. Using automated analysis to assess middle school students competence with scientific argumentation. Journal of Research in Science Teaching. 2024;61(1). doi:10.1002/tea.21864.

Kaldaras L, Li T, Haudek KC, Krajcik J. Developing Rubrics for AI Scoring of NGSS Learning Progression-Based Scientific Models. In: AERA Annual Conference. Philadelphia, PA: American Educational Research Association; 2024. doi:10.3102/2109181.

Li T, Kaldaras L, Haudek KC, Krajcik J. Utilizing Deep Learning AI to Evaluate Scientific Models: Overcoming the Challenges. In: NARST Annual Conference. Denver, CO: NARST; 2024.

Kaldaras L, Li T, Haudek KC, Krajcik J. Rubric Development for AI Scoring of NGSS Learning Progression-Based Scientific Models To Support Individual Opportunity To Learn. In: NARST Annual Conference. Denver, CO: NARST; 2024.

Shiroda M, Doherty JH, Haudek KC. Exploring Attributes of Successful Machine Learning Assessments for Scoring of Undergraduate Constructed Response Assessment Items. In: Zhai X, Krajcik J, eds. Uses of Artificial Intelligence in STEM Education. 1st ed. Oxford University Press; 2024. doi:10.1093/oso/9780198882077.003.0007.

Wang H, Li T, Haudek KC, et al. Is ChatGPT a Threat to Formative Assessment in College-Level Science? An Analysis of Linguistic and Content-Level Features to Classify Response Types. 2023. doi:10.1007/978-981-99-7947-9_13.

Shiroda M, Doherty J, Scott EE, Haudek KC. Covariational reasoning and item context affect language in undergraduate mass balance written explanations. Advances in Physiology Education. 2023. doi:10.1152/advan.00156.2022.

Zhai X, Haudek KC, Ma W. Assessing Argumentation Using Machine Learning and Cognitive Diagnostic Modeling. Research in Science Education. 2023;53(2). doi:10.1007/s11165-022-10062-w.

Shiroda M, Fleming M, Haudek KC. Ecological diversity methods improve quantitative examination of student language in short constructed responses in STEM. Frontiers in Education. 2023;8. doi:10.3389/feduc.2023.989836.

Sripathi KN, Moscarella RA, Steele M, et al. Machine Learning Mixed Methods Text Analysis: An Illustration From Automated Scoring Models of Student Writing in Biology Education. Journal of Mixed Methods Research. 2023:15586898231153946. doi:10.1177/15586898231153946.

Doherty JH, Cerchiara JA, Scoot EE, et al. Oaks to arteries: The Physiology Core Concept of flow down gradients supports transfer of student reasoning. Advances in Physiology Education. 2023. doi:10.1152/advan.00155.2022.

Doherty JH, Scott EE, Cerchiara JA, et al. What a Difference in Pressure Makes! A Framework Describing Undergraduate Students’ Reasoning about Bulk Flow Down Pressure Gradients. CBE—Life Sciences Education. 2023;22:ar23. doi:10.1187/cbe.20-01-0003.

Haudek KC, Romulo C, Anderson S, et al. Development of a Next Generation Concept Inventory with AI-based Evaluation for College Environmental Programs. In: AERA Annual Meeting. Chicago, IL: American Educational Research Association; 2023. doi:10.3102/IP.23.2014058.

Kaldaras L, Haudek KC. Validation of automated scoring for learning progression-aligned Next Generation Science Standards performance assessments. Frontiers in Education. 2022;7. doi:10.3389/feduc.2022.968289.

Kaldaras L, Yoshida N, Haudek KC. Rubric development for AI-enabled scoring of three-dimensional constructed-response assessment aligned to NGSS learning progression. Frontiers in Education. 2022;7. doi:10.3389/feduc.2022.983055.

Uhl JD, Shiroda M, Haudek KC. Developing assessments to elicit and characterize undergraduate mechanistic explanations about information flow in biology. Journal of Biological Education. 2022:1-20. doi:10.1080/00219266.2022.2041460.

Jeong S, Clyburn J, Bhatia N, McCourt J, Lemons PP. Student Thinking in the Professional Development of College Biology Instructors: An Analysis through the Lens of Sociocultural Theory. CBE—Life Sciences Education. 2022;21:ar30. doi:10.1187/cbe.21-01-0003.

Shiroda M, Scott E, Doherty JH, Haudek KC. Covariational reasoning and item context affect student language in undergraduate mass balance explanations. In: Society for Advancement of Biology Education Research Annual Meeting. Minneapolis, MN: SABER; 2022.

Uhl JD, Sripathi KN, Meir E, Merrill J, Urban-Lurain M, Haudek KC. Automated Writing Assessments Measure Undergraduate Learning after Completion of a Computer-Based Cellular Respiration Tutorial. CBE—Life Sciences Education. 2021;20:ar33. doi:10.1187/cbe.20-06-0122.

Shiroda M, Uhl JD, Urban-Lurain M, Haudek KC. Comparison of Computer Scoring Model Performance for Short Text Responses Across Undergraduate Institutional Types. Journal of Science Education and Technology. 2021:1-12. doi:10.1007/s10956-021-09935-y.

Uhl JD, Sripathi KN, Saldanha JN, et al. Introductory biology undergraduate students mixed ideas about genetic information flow. Biochemistry and Molecular Biology Education. 2021;49(3). doi:https://doi.org/10.1002/bmb.21483.

Zhai X, Haudek KC, Wilson C, Stuhlsatz M. A Framework of Construct-Irrelevant Variance for Contextualized Constructed Response Assessment. Frontiers in Education. 2021;6:409. doi:10.3389/feduc.2021.751283.

Uhl JD, Merrill JE, Urban-Lurain M, Haudek KC, Sripathi KN, Meir E. Automated Writing Assessment of Undergraduate Learning After Completion of a Computer-based Cellular Respiration Tutorial. In: Society for Advancement of Biology Education Research. Virtual: SABER; 2021. https://beyondmultiplechoice.org/sites/default/files/2021-07/Uhl_Juli_SABER2021_slides.pdf.

Shiroda M, Fleming M, Haudek KC. Applying Ecological Diversity Methods to Improve Examination of Constructed Responses. In: Society for Advancement of Biology Education Research. Virtual: SABER; 2021. https://beyondmultiplechoice.org/sites/default/files/2021-07/Shiroda_SABER%202021%20-%20Ordination2.pdf.

Uhl JD, Haudek KC. Student Explanations about Molecular Processes During Information Flow and Transfer in Biology. In: Society for Advancement of Biology Education Research. Virtual: SABER; 2021. https://beyondmultiplechoice.org/sites/default/files/2021-07/Uhl_Juli_SABER2021Poster(2).pdf.

Uhl JD, Haudek KC. Undergraduate Student Explanations About Molecular Processes in Information Flow and Transfer in Biology. In: NARST Annual Conference. Virtual: National Association for Research on Science Teaching and Learning; 2021. https://beyondmultiplechoice.org/sites/default/files/2021-03/Uhl_Undergraduates_InformationFlow_NARST%202021.pdf.

Haudek KC, Zhai X. Exploring the Effect of Assessment Construct Complexity on Machine Learning Scoring of Argumentation. In: NARST Annual Conference. Virtual: National Association for Research on Science Teaching and Learning; 2021. https://beyondmultiplechoice.org/sites/default/files/2021-03/Haudek_ExploringConstructComplexityonML_NARST2021.pdf.

Jeong S, Clyburn J, Lemons PP. Vygotskian professional development for biology instructors focusing on student thinking. In: National Association of Research in Science Teaching Annual International Conference. virtual: NARST; 2021.

Noyes K, McKay RL, Neumann M, Haudek KC, Cooper MM. Developing Computer Resources to Automate Analysis of Students’ Explanations of London Dispersion Forces. Journal of Chemical Education Research. 2020. doi:10.1021/acs.jchemed.0c00445.

Zhai X, Haudek KC, Shi L, Nehm RH, Urban-Lurain M. From substitution to redefinition: A framework of machine-learning based science assessment. Journal of Research in Science Teaching. 2020. doi:10.1002/tea.21658.

Jescovitch LN, Scott E, Cerchiara JA, et al. Comparison of Machine Learning Performance Using Analytic and Holistic Coding Approaches Across Constructed Response Assessments Aligned to a Science Learning Progression. Journal of Science Education and Technology. 2020. doi:10.1007/s10956-020-09858-0.

Bierema AM-K, Hoskinson A-M, Moscarella RA, et al. Quantifying cognitive bias in educational researchers. International Journal of Research \& Method in Education. 2020. doi:10.1080/1743727X.2020.1804541.

Yoho R, Kohn K, Haudek KC, Merrill JE, Urban-Lurain M. Exploring the meaning of function as a complex idea embedded within the crosscutting concept of structure and function. Journal on Excellence in College Teaching. 2020;31.

Zhai X, Yue Y, Pellegrino JW, Haudek KC, Shi L. Applying machine learning in science assessment: a systematic review. Studies in Science Education. 2020;56:111-151. doi:10.1080/03057267.2020.1735757.

You HS, Haudek KC, Merrill JE, Urban-Lurain M. Construct validity of computer scored constructed response items in undergraduate introductory biology courses. In: Rasch Measurement: Applications in Quantitative Educational Research. 1st ed. Singapore: Springer Singapore; 2020:223-240. doi:10.1007/978-981-15-1800-3_12.