Neal Kingston

Educational Psychology
Director of AAI
Primary office:
785-864-9705


Summary

Neal Kingston, Ph.D., is a University Distinguished Professor in the department of Educational Psychology at the University of Kansas, in which he also serves as the Director of Graduate Studies and Director of the Achievement and Assessment Institute (AAI). His research focuses on large-scale assessment, with particular emphasis on how it can better support student learning through the use of learning maps and diagnostic classification models. He has served as principal investigator or co-principal investigator for over 180 research grants. Of particular note was the Dynamic Learning Maps Alternate Assessment grant from the US Department of Education, which was at that time was the largest grant in KU history and which currently serves 21 state departments of education. Other recent testing projects include the Kansas Assessment Program, Career Pathways Collaborative, and Adaptive Reading Motivation Measures.

Dr. Kingston is known internationally for his work on large-scale assessment, formative assessment, and learning maps. He has served as a consultant or advisor for organizations such as the AT&T, College Board, Department of Defense Advisory Committee on Military Personnel Testing, Edvantia, General Equivalency Diploma (GED), Kaplan, King Fahd University of Petroleum and Minerals, Merrill Lynch, National Council on Disability, Qeyas (Saudi Arabian National Center for Assessment in Higher Education), the state of New Hampshire, the state of Utah, the U.S. Department of Education, and Western Governors University.

As Director of AAI, Dr. Kingston is responsible for multiple research centers with about 300 year round staff and about 150 temporary employees.

Education

Ph.D. Educational Measurement, Teachers College, Columbia University, New York, NY, 1983
M.Phil. Educational Measurement, Teachers College, Columbia University, New York, NY, 1983
M.Ed. Educational Measurement, Teachers College, Columbia University, New York, NY, 1978
M.A. Psychology in Education, Teachers College, Columbia University, New York, NY, 1977
B.A. Liberal Studies (concentrations in Biology & Education), State University of New York, Stony Brook, NY, 1974

Teaching

Currently, Dr. Kingston teaches a course in Classical Test Theory in the fall of odd numbered years, Item Response Theory in the spring of even numbered years, a course on meta-analysis in the fall of even numbered years, and an advanced seminar in a topic to be announced in the spring of odd numbered years.

Selected Publications

The following are a few categories of recent publications (publications may appear in more than one list):

Assessments that support learning

  1. Heritage, M. & Kingston, N.M. (2019). Classroom assessment and large-scale psychometrics: shall the twain meet? (a conversation with Margaret Heritage and Neal Kingston). Journal of Educational Measurement, 56(4), 670-685.
  2. Clark, A., Nash, B. Karvonen, M., & Kingston, N.M. (2017). Condensed Mastery Profile Method for Setting Standards for Diagnostic Assessment Systems. Educational Measurement: Issues and Practice. 36(4), 5–15.
  3. Kingston, N.M., Karvonen, M., Thompson, J.R., Wehmeyer, M.L., & Shogren, K.A. (2017). Fostering Inclusion of Students with Significant Cognitive Disabilities through the use of Learning Maps and Learning Map Based Assessments. Inclusion, 5(2), 110-120.
  4. Kingston, N.M. & Broaddus, A. (2017). The Use of Learning Map Systems to Support Formative Assessment in Mathematics. Education Sciences, 7 (41); doi:10.3390/educsci7010041.
  5. Kingston, N.M., Karvonen, M., Bechard, S., & Erickson, K. (2016). The Philosophical Underpinnings and Key Features of the Dynamic Learning Maps Alternate Assessment. Teachers College Record (Yearbook), 118(14). Retrieved September 1, 2016, from http://www.tcrecord.org ID Number: 140311.
  6. Popham, W. J., Berliner, D.C., Kingston, N., Fuhrman, S.H., Ladd, S.M., Charbonneau, J. & Chatterji, M. (2014). Can today's standardized tests yield instructionally useful data? Challenges, promises and the state of the art, Quality Assurance in Education, 22(4), 300-316.
  7. Bechard, S., Clark, A. K., Swinburne Romine, R., Karvonen, M., Kingston, N.M., & Erickson, K. (2019). Use of evidence-centered design to develop learning maps-based assessments. International Journal of Testing, 19:2, 188-205.

Students who face education or assessment challenges

  1. Davis, M. H., Wang, W., Kingston, N., Hock, M., Tonks, S. M., & Tiemann, G. (2020). Computer Adaptive Measure of Reading Motivation. Research in Reading, 43(4), 434-453.
  2. Kingston, N.M., Karvonen, M., Thompson, J.R., Wehmeyer, M.L., & Shogren, K.A. (2017). Fostering Inclusion of Students with Significant Cognitive Disabilities through the use of Learning Maps and Learning Map Based Assessments. Inclusion, 5(2), 110-120.
  3. Kingston, N.M., Karvonen, M., Bechard, S., & Erickson, K. (2016). The Philosophical Underpinnings and Key Features of the Dynamic Learning Maps Alternate Assessment. Teachers College Record (Yearbook), 118(14). Retrieved September 1, 2016, from http://www.tcrecord.org ID Number: 140311.
  4. Cho, H. & Kingston, N.M. (2013). Why IEP Teams Assign Low Performers with Mild Disabilities to the Alternate Assessment Based on Alternate Achievement Standards. Journal of Special Education, 47, 162-174.
  5. Cho, H., Wehmeyer, M. & Kingston, N.M. (2013). Factors that Predict Elementary Educators’ Perceptions and Practice in Teaching Self-Determination. Psychology in the Schools, 50: 770-780.

Psychometric methods

  1. Wang, W., Chen, J., & Kingston, N. (2020). How well do simulation studies inform decisions about multistage testing? Journal of Applied Measurement, 21(3), 1-11.
  2. Pan, Q., Qin, L., & Kingston, N. (2020). Growth Modeling in a Diagnostic Classification Model (DCM) Framework–A Multivariate Longitudinal Diagnostic Classification Model.
  3. Wang, W. & Kingston, N.M. (2020). Using Bayesian Nonparametric Item Response Functions to Check Parametric Model Fit. Applied Psychological Measurement.
  4. Wang, W, & Kingston, N.M. (2019). Adaptive testing with the Hierarchical Item Response Theory Model. Applied Psychological Measurement, 43(1), 51-67.
  5. Embretson, S.E. & Kingston, N.M. (2018). Automatic Item Generation: A More Efficient Process for Developing Mathematics Achievement Items? Journal of Educational Measurement. 55(1), 112-131.
  6. Adjei, S., Selent, D., Heffernan, N., Pardos, Z., Broaddus, A., Kingston, N. (2014). Refining Learning Maps with Data Fitting Techniques: Searching for Better Fitting Learning Maps. In Pardos & Stamper (Eds.) The 2014 Proceedings of International Educational Data Mining Society.
  7. Gu, F., Little, T., & Kingston, N.M. (2013). Misestimation of Reliability Using Coefficient Alpha and Structural Equation Modeling when Assumptions of Tau-Equivalence and Uncorrelated Errors are Violated. Methodology, 9, 30-40.

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