Data-Driven Equity: Transforming Assessment and Curriculum Design In Competency-Based Education
DOI:
https://doi.org/10.17161/cberj.v3no7.24673Keywords:
Competency-based education (CBE); Assessment fairness; Differential item functioning (DIF); Performance assessment; Equity in higher educationAbstract
This study investigates Western Governors University's systematic approach to integrating equity principles into its competency-based education model. As institutions of higher learning grapple with achievement gaps across diverse student populations, WGU's data-driven methodology provides valuable insights into identifying and addressing these gaps. Across ~30,000 students in 30 courses (32 different assessments) from January 1, 2023, to January 1, 2024, we applied item response theory residual DIF with iterative purification. Five assessments (ranging from 2% to 10% of items) were statistically flagged for DIF; none were confirmed as content-biased through expert review. Results demonstrate that while assessment bias constitutes one potential factor in performance discrepancies, a more comprehensive approach is necessary to address educational inequities. When DIF analysis revealed no assessment bias, the investigation expanded to other potential contributors, including learning resource bias, student readiness measures, and faculty intervention strategies. The study highlights WGU's multifaceted interventions, including AI-generated personalized learning activities, the Quality Interaction Measure for faculty feedback, and peer-moderated training designed to create more equitable learning environments. Beyond course-level interventions, we propose a multi-stage support system spanning the entire student journey, beginning with first-term readiness indicators and continuing through program-level mentorship. Concluding remarks suggest that institutions can effectively dismantle systemic barriers to equity by conducting comprehensive evaluations of institutional practices, establishing mentorship programs, and consistently evaluating outcomes through key performance indicators.
Downloads
References
American Educational Research Association, American Psychological Association, & National Council on Measurement in Education. (2014). Standards for educational and psychological testing. American Educational Research Association.
Anderson, L. W., Krathwohl, D. R. (2001). A Taxonomy for learning, teaching, and assessing: A Revision of Bloom’s Taxonomy of Educational Objectives. Longman.
Bensimon, E. M. (2007). The underestimated significance of practitioner knowledge in the scholarship on student success. The Review of Higher Education, 30(4), 441–469.
CAST. (2018). Universal Design for Learning guidelines version 2.2. CAST.
Crisp, G., & Cruz, I. (2009). Mentoring college students: A critical review of the literature between 1990 and 2007. Research in Higher Education, 50(6), 525–545.
Dorans, N. J., & Holland, P. W. (1993). DIF detection and description: Mantel–Haenszel and standardization. In P. W. Holland & H. Wainer (Eds.), Differential item functioning (pp. 35–66). Lawrence Erlbaum.
Hambleton, R. K. (2006). Good practices for identifying differential item functioning. Medical Care, 44(11, Suppl. 3), S182–S188.
Hattie, J., & Timperley, H. (2007). The power of feedback. Review of Educational Research, 77(1), 81–112.
Kane, M. T. (2013). Validating the interpretations and uses of test scores. Journal of Educational Measurement, 50(1), 1–73.
King, H. T. (2017). Reinventing Higher Education, Changing Lives: The Story of Western Governors University. Western Governors University.
Ladson-Billings, G. (2006). From the achievement gap to the educational debt: Understanding achievement in U.S. schools. Educational Researcher, 35(7), 3–12.
Lim, H., Choe, E. M., & Han, K. C. T. (2022). A residual-based differential item functioning detection framework in item response theory. Journal of Educational Measurement, 59(4), 678–701. https://doi.org/10.1111/jedm.12364
Mantel, N., & Haenszel, W. (1959). Statistical aspects of the analysis of data from retrospective studies. Journal of the National Cancer Institute, 22, 719–748.
Messick, S. (1989). Validity. In R. L. Linn (Ed.), Educational measurement (3rd ed., pp. 13–103). Macmillan.
Osterlind, S. J., & Everson, H. T. (2009). Differential item functioning. Sage.
Pane, J. F., Steiner, E. D., Baird, M. D., & Hamilton, L. S. (2015). Continued progress: Promising evidence on personalized learning. RAND Corporation.
Popham, W. J. (2017). Classroom assessment: What teachers need to know (8th ed.). Pearson.
Raju, N. S. (1988). The area between two item characteristic curves. Psychometrika, 53(4), 495–502.
Sireci, S. G., & Rios, J. A. (2013). Decisions that make a difference in detecting DIF. Educational Measurement: Issues and Practice, 32(2), 35–43.
Thissen, D., Steinberg, L., & Wainer, H. (1993). Detection of differential item functioning using the parameters of item response theory. In P. W. Holland & H. Wainer (Eds.), Differential item functioning (pp. 67–113). Lawrence Erlbaum.
Tinto, V. (2012). Completing college: Rethinking institutional action. University of Chicago Press.
U.S. Department of Education, Office of Vocational and Adult Education. (2012). Investing in America’s future: A blueprint for transforming career and technical education. https://files.eric.ed.gov/fulltext/ED536889.pdf
Walton, G. M., & Cohen, G. L. (2011). A brief social-belonging intervention improves academic and health outcomes of minority students. Science, 331(6023), 1447–1451.
Downloads
Published
Issue
Section
License
Copyright (c) 2026 Dr. Sean Gyll, Dr. Heather Hayes (Author)

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.