【摘要】： 針對當(dāng)前科學(xué)發(fā)現(xiàn)學(xué)習(xí)環(huán)境普遍存在的領(lǐng)域依賴和學(xué)習(xí)支持不足等問題,本文試圖建立一個獨立于特定領(lǐng)域的,更一般意義上的通用科學(xué)發(fā)現(xiàn)學(xué)習(xí)環(huán)境。為此,我們將這一問題分解為三個子問題加以解決:(1)科學(xué)發(fā)現(xiàn)學(xué)習(xí)的認知機制如何?(2)如何依據(jù)科學(xué)發(fā)現(xiàn)學(xué)習(xí)的認知機制,建構(gòu)科學(xué)發(fā)現(xiàn)學(xué)習(xí)環(huán)境的內(nèi)部數(shù)據(jù)模型?(3)在數(shù)據(jù)模型的基礎(chǔ)上,應(yīng)該如何設(shè)計科學(xué)發(fā)現(xiàn)學(xué)習(xí)環(huán)境的各種功能和數(shù)據(jù)視圖,以幫助學(xué)習(xí)者順利地完成科學(xué)發(fā)現(xiàn)學(xué)習(xí)? 首先,根據(jù)信息加工理論關(guān)于問題解決的研究框架,本文認為:科學(xué)發(fā)現(xiàn)學(xué)習(xí)屬于特定領(lǐng)域內(nèi)且定義良好的問題解決活動,本質(zhì)上屬于歸納推理;科學(xué)發(fā)現(xiàn)學(xué)習(xí)的任務(wù)環(huán)境由實驗?zāi)Ｐ秃涂茖W(xué)理論模型兩部分構(gòu)成,歸納邏輯就體現(xiàn)在其構(gòu)成要素概念和關(guān)系兩個方面;在信息加工的層面上,科學(xué)發(fā)現(xiàn)學(xué)習(xí)可以看作是學(xué)習(xí)者在假設(shè)空間和實驗空間進行的雙重搜索活動,可以采取“理論驅(qū)動的歸納”和“數(shù)據(jù)驅(qū)動的歸納”兩種策略,包括搜索假設(shè)空間、搜索實驗空間和證據(jù)評估三個核心子過程;為了順利完成科學(xué)發(fā)現(xiàn)學(xué)習(xí),學(xué)習(xí)者需要在領(lǐng)域知識和元知識兩個方面得到支持;此外,監(jiān)控與反思也是科學(xué)發(fā)現(xiàn)學(xué)習(xí)的重要環(huán)節(jié)。 其次,科學(xué)發(fā)現(xiàn)學(xué)習(xí)環(huán)境屬于典型的建構(gòu)主義式學(xué)習(xí)環(huán)境,其系統(tǒng)模型可以分解為領(lǐng)域知識模型、學(xué)習(xí)者模型和活動模型。其中,領(lǐng)域知識模型又分為仿真模型和解釋模型,前者可以利用知識表示框架結(jié)合仿真引擎的方法實現(xiàn),而后者則可以采用類似于知識庫的建模方法;學(xué)習(xí)者模型記錄了學(xué)習(xí)者的認知過程,映射著學(xué)習(xí)者問題空間中的假設(shè)空間和實驗空間;活動模型根據(jù)科學(xué)發(fā)現(xiàn)學(xué)習(xí)的基本過程為學(xué)習(xí)者配置規(guī)范化的交互空間,包括問題交互空間、假設(shè)交互空間、實驗交互空間和結(jié)論交互空間。 接著,在科學(xué)發(fā)現(xiàn)學(xué)習(xí)環(huán)境的界面設(shè)計上,本文總結(jié)了現(xiàn)有的研究成果,指出科學(xué)發(fā)現(xiàn)學(xué)習(xí)環(huán)境的界面設(shè)計應(yīng)將重點放在知識呈現(xiàn)、假設(shè)形成、實驗設(shè)計、數(shù)據(jù)處理和自我監(jiān)控五個方面。 最后,在上述研究的基礎(chǔ)上,本文設(shè)計開發(fā)了關(guān)于連續(xù)系統(tǒng)的通用科學(xué)發(fā)現(xiàn)學(xué)習(xí)環(huán)境的原型系統(tǒng)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.
【學(xué)位授予單位】：南京師范大學(xué)
【學(xué)位級別】：博士
【學(xué)位授予年份】：2008
【分類號】：G40

【引證文獻】

相關(guān)期刊論文 前3條

1 張朔瑋;姜小鷹;肖惠敏;張旋;林雁;;發(fā)現(xiàn)學(xué)習(xí)法在《護理研究》實驗課教學(xué)中的應(yīng)用研究[J];福建醫(yī)科大學(xué)學(xué)報(社會科學(xué)版);2013年01期

2 陳剛;;基于CBR的學(xué)習(xí)者知識點掌握水平預(yù)測方法研究[J];軟件導(dǎo)刊(教育技術(shù));2010年08期

3 陳剛;石晉陽;;基于GOMS模型的科學(xué)發(fā)現(xiàn)學(xué)習(xí)認知任務(wù)分析[J];現(xiàn)代教育技術(shù);2013年04期

相關(guān)碩士學(xué)位論文 前3條

1 錢逸舟;基于任務(wù)的學(xué)習(xí)環(huán)境設(shè)計研究[D];南京大學(xué);2011年

2 郭曉東;科學(xué)發(fā)現(xiàn)過程的心理學(xué)分析對中學(xué)物理教學(xué)的啟示[D];東北師范大學(xué);2012年

3 張朔瑋;《護理研究》教學(xué)中護生批判性思維能力培養(yǎng)的實踐研究[D];福建醫(yī)科大學(xué);2013年

，

本文編號：2233528