Using Personalized Computer Adaptive Testing to Evaluate Competency-Based Mathematics in Secondary Schools

Although computerized adaptive testing (CAT) provides a promising route to personalized assessments, it has been falling short in providing the detailed personal feedback that is necessary for a competency-based curriculum beyond the typical pass/fail scores. Personalizing competency-based assessments to deliver accurate feedback per learner has been hindered by examiner biases and exorbitant implementation costs. Evidence suggests that the knowledge space theory (KST) is valuable in illustrating the knowledge characteristics of individual learners based on their responses to a set of academic problems. This study sought to explore the application of CAT integrated with KST to evaluate mathematical competencies in a competency-based curriculum. Forty-two learners were selected using cluster sampling to participate in quasi-experimental research comparing learner outcomes in a mathematics-based CAT and traditional paper-pencil competency tests. The study found that KST-integrated CAT demonstrated consistent and reliable results across the three trials, unlike the conventional test, which showed glaring inconsistencies. By integrating CAT with KST to create a Competency-Based Computerized Adaptive test (CBCAT), the study underscores CAT’s potential to revolutionize assessments within competency-based education frameworks. By addressing gaps in current assessment systems, CAT can improve student outcomes, particularly in mathematics, and foster a supportive learning environment that aligns with modern educational goals.