Investigating the population-dependent characteristic and transitional process of learning progression using model analysis: A case study of force and motion

• In Event: AERA Poster Session Wednesday 1:45pm
  In Poster Session: Supporting Sensemaking in Science Teaching and Learning

Abstract

To explore the characteristic of population-dependence of learning progression and conceptualize the sophistication and dynamics of students’ learning process, this study first duplicate Fulmer et al.’s research using data from 1619 Chinese students. The findings show notable differences in item difficulty and model fit between the two groups, with the Chinese sample showing overlapping thresholds that weaken the learning progression’s discriminative validity. Secondly, by applying model analysis, this study investigated the intermediate states along the learning progression and found that students are often situated in “model-mixing zones”. These findings challenge the assumption of “theory-like coherence” underlying traditional LP models and suggest the need for more population and context-sensitive progression frameworks.

Author

• YI DING, Nanjing University Of Information and Science Technology