科学发现学习的认知机制与学习环境建模
发布时间：2018-09-09 20:49
【摘要】： 针对当前科学发现学习环境普遍存在的领域依赖和学习支持不足等问题,本文试图建立一个独立于特定领域的,更一般意义上的通用科学发现学习环境。为此,我们将这一问题分解为三个子问题加以解决:(1)科学发现学习的认知机制如何?(2)如何依据科学发现学习的认知机制,建构科学发现学习环境的内部数据模型?(3)在数据模型的基础上,应该如何设计科学发现学习环境的各种功能和数据视图,以帮助学习者顺利地完成科学发现学习? 首先,根据信息加工理论关于问题解决的研究框架,本文认为:科学发现学习属于特定领域内且定义良好的问题解决活动,本质上属于归纳推理;科学发现学习的任务环境由实验模型和科学理论模型两部分构成,归纳逻辑就体现在其构成要素概念和关系两个方面;在信息加工的层面上,科学发现学习可以看作是学习者在假设空间和实验空间进行的双重搜索活动,可以采取“理论驱动的归纳”和“数据驱动的归纳”两种策略,包括搜索假设空间、搜索实验空间和证据评估三个核心子过程;为了顺利完成科学发现学习,学习者需要在领域知识和元知识两个方面得到支持;此外,监控与反思也是科学发现学习的重要环节。 其次,科学发现学习环境属于典型的建构主义式学习环境,其系统模型可以分解为领域知识模型、学习者模型和活动模型。其中,领域知识模型又分为仿真模型和解释模型,前者可以利用知识表示框架结合仿真引擎的方法实现,而后者则可以采用类似于知识库的建模方法;学习者模型记录了学习者的认知过程,映射着学习者问题空间中的假设空间和实验空间;活动模型根据科学发现学习的基本过程为学习者配置规范化的交互空间,包括问题交互空间、假设交互空间、实验交互空间和结论交互空间。 接着,在科学发现学习环境的界面设计上,本文总结了现有的研究成果,指出科学发现学习环境的界面设计应将重点放在知识呈现、假设形成、实验设计、数据处理和自我监控五个方面。 最后,在上述研究的基础上,本文设计开发了关于连续系统的通用科学发现学习环境的原型系统GSDLE。
[Abstract]:Aiming at the problems of domain dependence and insufficient learning support in the current scientific discovery learning environment, this paper attempts to establish a general scientific discovery learning environment which is independent of a specific field and in a more general sense. Therefore, we decompose the problem into three sub-problems: (1) what is the cognitive mechanism of scientific discovery learning? (2) how to base on the cognitive mechanism of scientific discovery learning? (3) on the basis of the data model, how to design the various functions and data views of the scientific discovery learning environment in order to help the learners to complete the scientific discovery learning successfully? (3) on the basis of the data model, how to design the various functions and data views of the scientific discovery learning environment? Firstly, according to the research framework of information processing theory on problem solving, this paper holds that: scientific discovery learning belongs to a well-defined problem solving activity in a specific field, and in essence belongs to inductive reasoning; The task environment of scientific discovery learning consists of two parts: experimental model and scientific theoretical model. The inductive logic is embodied in the concept and relationship of its constituent elements, and in the level of information processing, Scientific discovery learning can be regarded as a dual search activity of learners in hypothesis space and experimental space. It can adopt two strategies of "theory-driven induction" and "data-driven induction", including search hypothesis space. Search for experimental space and evidence evaluation three core sub-processes; in order to successfully complete the scientific discovery learning learners need to be supported in both domain knowledge and meta knowledge; in addition monitoring and reflection is also an important link in scientific discovery learning. Secondly, the scientific discovery learning environment belongs to the typical constructivism learning environment, its system model can be decomposed into domain knowledge model, learner model and activity model. The domain knowledge model can be divided into simulation model and interpretation model. The former can be implemented by using knowledge representation framework combined with simulation engine, while the latter can adopt a modeling method similar to knowledge base. The learner model records the cognitive process of the learner, mapping the hypothesis space and the experimental space in the learner problem space, and the activity model allocates the standardized interactive space for the learner according to the basic process of scientific discovery learning. It includes problem interactive space, hypothetical interactive space, experimental interactive space and conclusion interactive space. Then, on the interface design of scientific discovery learning environment, this paper summarizes the existing research results, and points out that the interface design of scientific discovery learning environment should focus on knowledge presentation, hypothesis formation, experimental design. Data processing and self-monitoring. Finally, on the basis of the above research, this paper designs and develops a prototype system, GSDLE., for a general scientific discovery learning environment for continuous systems.
【学位授予单位】：南京师范大学
【学位级别】：博士
【学位授予年份】：2008
【分类号】：G40
本文编号：2233528
[Abstract]:Aiming at the problems of domain dependence and insufficient learning support in the current scientific discovery learning environment, this paper attempts to establish a general scientific discovery learning environment which is independent of a specific field and in a more general sense. Therefore, we decompose the problem into three sub-problems: (1) what is the cognitive mechanism of scientific discovery learning? (2) how to base on the cognitive mechanism of scientific discovery learning? (3) on the basis of the data model, how to design the various functions and data views of the scientific discovery learning environment in order to help the learners to complete the scientific discovery learning successfully? (3) on the basis of the data model, how to design the various functions and data views of the scientific discovery learning environment? Firstly, according to the research framework of information processing theory on problem solving, this paper holds that: scientific discovery learning belongs to a well-defined problem solving activity in a specific field, and in essence belongs to inductive reasoning; The task environment of scientific discovery learning consists of two parts: experimental model and scientific theoretical model. The inductive logic is embodied in the concept and relationship of its constituent elements, and in the level of information processing, Scientific discovery learning can be regarded as a dual search activity of learners in hypothesis space and experimental space. It can adopt two strategies of "theory-driven induction" and "data-driven induction", including search hypothesis space. Search for experimental space and evidence evaluation three core sub-processes; in order to successfully complete the scientific discovery learning learners need to be supported in both domain knowledge and meta knowledge; in addition monitoring and reflection is also an important link in scientific discovery learning. Secondly, the scientific discovery learning environment belongs to the typical constructivism learning environment, its system model can be decomposed into domain knowledge model, learner model and activity model. The domain knowledge model can be divided into simulation model and interpretation model. The former can be implemented by using knowledge representation framework combined with simulation engine, while the latter can adopt a modeling method similar to knowledge base. The learner model records the cognitive process of the learner, mapping the hypothesis space and the experimental space in the learner problem space, and the activity model allocates the standardized interactive space for the learner according to the basic process of scientific discovery learning. It includes problem interactive space, hypothetical interactive space, experimental interactive space and conclusion interactive space. Then, on the interface design of scientific discovery learning environment, this paper summarizes the existing research results, and points out that the interface design of scientific discovery learning environment should focus on knowledge presentation, hypothesis formation, experimental design. Data processing and self-monitoring. Finally, on the basis of the above research, this paper designs and develops a prototype system, GSDLE., for a general scientific discovery learning environment for continuous systems.
【学位授予单位】：南京师范大学
【学位级别】：博士
【学位授予年份】：2008
【分类号】：G40
【引证文献】
相关期刊论文 前3条
1 张朔玮;姜小鹰;肖惠敏;张旋;林雁;;发现学习法在《护理研究》实验课教学中的应用研究[J];福建医科大学学报(社会科学版);2013年01期
2 陈刚;;基于CBR的学习者知识点掌握水平预测方法研究[J];软件导刊(教育技术);2010年08期
3 陈刚;石晋阳;;基于GOMS模型的科学发现学习认知任务分析[J];现代教育技术;2013年04期
相关硕士学位论文 前3条
1 钱逸舟;基于任务的学习环境设计研究[D];南京大学;2011年
2 郭晓东;科学发现过程的心理学分析对中学物理教学的启示[D];东北师范大学;2012年
3 张朔玮;《护理研究》教学中护生批判性思维能力培养的实践研究[D];福建医科大学;2013年
，本文编号：2233528
本文链接：https://www.wllwen.com/jiaoyulunwen/jgkg/2233528.html
