Model-Based Science Learning And The Learning Environment Design

Based on the complexity analysis of scientific models, model-based reasoning, and modeling, this thesis suggests that the main characteristics on model-based science learning include: the significance of the scientific knowledge comes from the nature of models; learning goals should involve with the characteristics and significance of models; model-based reasoning and modeling are basic for learning. Hence, it chose the features of Constructivist learning environment to form an analysis framework for learning environment which support the model-based science learning (MBSL), and it also respectively studied the features of Constructivist learning environment with class and with computer. Through comparative study of factors and strategies in the class environment and computer environment, it fixes the study of design on knowledge and learning support, for solution on the provided research problems. The learning support is consisted of resources and supportive strategies. After analyzing the complexity of knowledge, it provides two principles for knowledge design, that is, curriculum design should consider complexity variation at both horizontal level and vertical level; models in the knowledge design should start from simple ones to complex ones, and from general ones to specific ones. A framework for the design of learning support is extracted from the learning environment analysis discussed above. Integrating the resource support and strategy support, principles of learning support design for MBSL learning environment are provided: examine prior ideas of students; design for rich resources; use a series of questions to scaffold inquiry process and meta-cognition; add grounding phase into design to provide relative information and adopt relative skills.