Publications

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

Assessments that Support Learning

  1. Kingston N.M., Alonzo A, Long H and Swinburne Romine R (2022) Editorial: The use of organized learning models in assessment. Front. Educ. 7:1009446. doi: 10.3389/feduc.2022.1009446
  2. Kingston, N.M., Hess, J., Cope, D., Romine, R.S. (2022). On Determining the Efficacy of Using Learning Maps as an Organizing Structure for Formative Assessment: Some Lessons Learned in Hong Jiao and Robert Lissitz (Eds.) Enhancing Effective Instruction and Learning Using Assessment Data. Charlotte, NC: Information Age Publishing
  3. 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.
  4. 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.
  5. 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.
  6. 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.
  7. 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 Teaching Curriculum Resources ID Number: 140311.
  8. 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.
  9. 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. Karvonen, M., Kingston, N.M., Wehmeyer, M. & Thompson, W.J. (2020). New approaches to designing and administering inclusive assessments. Oxford Encyclopedia of Inclusive and Special Education.
  2. Wang, W., Kingston, N.M., Tiemann, G.C., Davis, M.H., Tonks, S., Hock, M. (2021). Applying evidence-centered design in the development of a multidimensional adaptive reading motivation measure. Educational Measurement: Issues and Practice, 40(4), 91-100.
  3. 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.
  4. 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.
  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 Teaching Curriculum Resources ID Number: 140311.
  6. 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.
  7. 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.