Knowledge Integration in Science Learning: Tracking Students' Knowledge Development and Skill Acquisition with Cognitive Diagnosis Models

Abstract

In scientific literacy, knowledge integration (KI) is a scaffolding-based theory to assist students' scientific inquiry learning. To drive students to be self-directed, many courses have been developed based on KI framework. However, few efforts have been made to evaluate the outcome of students' learning under KI instruction. Moreover, finer-grained information has been pursued to better understand students' learning and how it progresses over time. In this article, a normative procedure of building and choosing cognitive diagnosis models (CDMs) and attribute hierarchies was formulated under KI theory. We examined the utility of CDMs for evaluating students' knowledge status in KI learning. The results of the data analysis confirmed an intuitive assumption of the hierarchical structure of KI components. Furthermore, analysis of pre- and posttests using a higher-order, hidden Markov model tracked students' skill acquisition while integrating knowledge. Results showed that students make significant progress after using the web-based inquiry science environment (WISE) platform.