Modern Measurement for Science Learning Progressions (Book by Routledge)

Session Type: Symposium

Abstract

Learning progressions (LP) are powerful frameworks for aligning and developing curriculum, instruction, and assessment. This symposium presents modern measurement and technologies for science learning progression. It includes an introduction of LP background, theories, methodologies, applications, and important implications. Additionally, it discusses validation used in LP research for a sound theoretical rationale and presents how empirical evidence is collected to validate an LP for science-in-use. It further elaborates how Bayesian networks models can be used to monitor students’ progression over time and illustrates how AI can be used to develop an LP for the three-dimensional science learning emphasized in Next Generation Science Standards (NGSS). These are based on Handbook of Research for Science Learning Progression (Jin, Yan, & Krajcik, in press).

Sub Unit

• Division D - Measurement and Research Methodologies / Division D - Section 1: Measurement and Research Methodologies

Chair

• Duanli Yan, Educational Testing Service

Papers

• Introduction of Science Learning Progression - Joseph S. Krajcik, Michigan State University

• Response to the Critiques of Learning Progression Research - Hui Jin, Georgia Southern University

• Validation of Learning Progressions for Knowledge-in-Use - Leonora Kaldaras, Texas Tech University

• The Application of Bayesian Networks in Learning Progression–Based Assessment - Duanli Yan, Educational Testing Service

• Integrating AI Into Learning Progression to Support Student Knowledge-in-Use: Opportunities and Challenges - Peng He, Washington State University

Discussant

• James W. Pellegrino, University of Illinois at Chicago