Competency graphs are not influenced by demographic missing data, but any later association analyses are.

Context

Definitions

  • A student is considered to have achieved a competency if they score a 5 or higher on that competency. Students could have scored 1 to 8, and were scored by their instructor using a common rubric. In this way, students either achieve or do not achieve a competency, a binary outcome. Therefore, we would be interested in estimating the proportion of students achieving each competency in each semester, regardless of the course or instructor.

  • Students belong to specific semester and instructor-course group.

    • Semesters (time): Fall 2022, Spring 2023, Fall 2023, Spring 2024, Fall 2024, Spring 2025
    • Instructor-Course Groups (sampling): 3016-WCP, 3026-RBD, 3026-MPA, 3027-MPA, 3028-RBD, 3016-MPA

Competencies & Domains

  • Tecnología: gráfica/excel, línea de tendencia
  • Investigación científica: hipótesis, justificación de experimento, identificación del diseño experimental, crítica del experimento, conclusiones
  • Información: evaluación de fuentes, citas APA
  • Comunicación escrita

This report focuses on the “Investigación científica” domain, first considering its five competencies and then domain overall.

Boostrap CI

Resampling at the instructor–course (cluster) level within each semester make the SEs and CIs robust to within-instructor correlation and heteroskedasticity. This ensures that the CIs better reflect the uncertainty in the estimates of the percentage of students achieving the given competency/domain.

Approach: cluster bootstrap percentile CI of the student-weighted proportion per semester

Plots

Competency III: Formulating Hypothesis

Competency IV: Justifying Experiment

Competency V: Identifying Experimental Design

Competency VI: Critiquing Experiment

Competency VII: Reaching Conclusions

Domain: Scientific Research