Tracking Learning Trajectories in Fraction Addition and Subtraction Using Longitudinal Cognitive Diagnosis Modeling Framework

• In Event: Advances in Psychometric and Machine Learning Approaches to Assessment

Abstract

This study developed and evaluated a cognitively diagnostic assessment and adaptive intervention for tracking learning trajectories in fraction addition and subtraction. A recursive multiphase mixed methods design guided the five phases: (1) cognitive model development, (2) cognitively diagnostic assessment development, (3) attribute mastery diagnosis, (4) adaptive intervention development, and (5) learning trajectory tracking. The cognitive model developed consisted of seven attributes measured by 30-item Cognitively Diagnostic Fraction Addition and Subtraction Test (CDFAST). Attribute mastery results showed most students lacked essential skills for fraction addition and subtraction. An adaptive intervention called Fraction DRIVE was developed to address unmastered attributes. Longitudinal cognitive diagnosis modeling demonstrated intervention’s effectiveness in improving both group-level and individual-level learning outcomes of students in fraction addition and subtraction.

Authors

• Alvin Daiz Tenorio, University of the Philippines

• Kevin Carl Pena Santos, University of the Philippines

• Jose Quito Pedrajita, University of the Philippines