摘要
自动化项目生成是近年来兴起的测量领域,是一种以项目认知加工理论为基础的原则性项目设计(principled item design)模式。其中,如何在项目认知模型基础上,通过任务结构分析的方式系统全面的鉴别和提取任务特征是一个关键环节。基于已有文献中代数应用题的命题分析法、网络语言分析法、关系-函数分析法、任务分析地图等四种结构分析方法,研究探索了能够服务于自动化项目生成的代数应用题任务结构分析方法。该分析表明,前三种方法分别对应于个体解题过程需要形成的三种中介表征,即问题陈述背后的命题表征、事件时空关系的情境模型、以及变量间数量关系的问题模型,第四种方法从过程角度分析了问题解决的认知需求。然而,要实现项目生成的特征提取需求,尚需对现有四种方法所揭示问题特征的心理现实性、特征提取的系统性和完备性、任务领域的适用范围、以及不同方法的整合等问题开展进一步研究。
Automatic item generation is a principled approach to item design that is grounded on cognitive theory of item solution, of which a key step is to identify and extract task features systematically via principled structure analyses of the items. The current study aims to develop such a principled structure analysis method of algebra story problems that serves the purpose of automatic item generation by synthesizing four structure analysis methods that are currently available in the literature, that is, propositional analysis, network language analysis, relation-functional analysis and task analysis map. It is shown that task features extracted from the first three methods correspond to the three types of interim mental representations formed during algebra story problem solving, that is, semantic structure, situational model and problem model, respectively, whereas those from the fourth reveal the cognitive demands at various stages of problem solving. However, further studies are required to investigate the psychological reality, thoroughness, and the range of applicability for such methods before an integrated method of structure analysis of algebra story problems can be formulated for automatic item generation.
出处
《心理科学进展》
CSSCI
CSCD
北大核心
2014年第3期558-570,共13页
Advances in Psychological Science
基金
国家自然科学基金(31171000)
上海市教育科学2011年度重点项目(A1117)资助
关键词
自动化项目生成
代数应用题
项目认知模型
任务结构分析方法
automatic item generation
algebra story problem
cognitive model
principled structure analysis method
