Evaluation of Learning Progression Across Cognitive Domains: Using TIMSS Data

• In Event: Research on Cognitive, Reading, and Resilience Outcomes in Large-Scale Assessments

Abstract

Learning progression (LP) serves a crucial role in assessing students' knowledge and skills, complementing the summative form of assessment by monitoring their progress over time and across grades. This study focuses on developing math cognitive domain learning progression for fourth graders, utilizing the 2015 TIMSS data. Applying three cognitive diagnostic models (CDMs) to the TIMSS data containing 25 items, students were effectively classified into appropriate learning levels across cognitive domains). Among the three CDMs, the Deterministic inputs, noisy "and" gate model (DINA), and the HO-DINA demonstrated superior fit and appropriateness in evaluating learning. The hypothesized LP's unstructured hierarchical structure was found to be more reasonable than the linear hierarchical structure, and it was further refined based on attribute mastery probability.

Author

• Olasunkanmi James Kehinde, Norfolk State Uinversity