基于程序性知识学习的项目状态转移函数与多分知识结构

doi:10.16451/j.cnki.issn1003-6059.202203003

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摘要在程序性知识的评估中,技能是指与项目的解决相关的操作路径.基于程序性知识的学习评价,文中提出由项目自身的状态结构诱导多分知识结构的方法,目的是建立适用于问题解答的多分评估体系.首先,根据各项目的解答或操作过程设定响应值,得到项目特定的响应值集.通过项目状态转移函数定义项目状态空间,将问题空间推广到多分情形.然后,由操作路径导出合取的技能映射,讨论由合取的技能映射诱导的多分知识结构.结果表明由技能映射通过合取模型诱导的多分知识结构满足逐项交封闭.最后,给出诱导多分知识结构的算法步骤,并举例说明算法的有效性.

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	孙晓燕
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关键词 ：项目状态转移函数, 操作路径, 合取的技能映射, 多分知识结构

Abstract：In the assessment of procedural knowledge, skills refer to the operation paths relevant to the solution of an item. Based on the learning assessment of procedural knowledge, a method of delineating polytomous knowledge structure from the state structure of the item itself is proposed to establish a polytomous assessment system for problem solving. Firstly, the response values are set according to the solution or operation process of each item to obtain the item-specific response value set. The item state space is defined by item state transition function, and the problem space is extended to polytomous case. Then,the conjunctive skill maps are derived from the operation paths, and the polytomous knowledge structures delineated by the conjunctive skill maps are discussed. The results show that the polytomous knowledge structure delineated by a skill map based on the conjunctive model satisfies the item-wise intersection closure. Finally, the algorithm steps of delineating polytomous knowledge structure are given, and the effectiveness of the proposed algorithm is illustrated by an example.

Key words： Item State Transition Function Operation Path Conjunctive Skill Map Polytomous Knowledge Structure

收稿日期: 2021-11-11

ZTFLH:

TP 182

基金资助:国家自然科学基金项目(No.11871259,11971287)、福建省自然科学基金项目(No.2019J01748,2020J02043)资助

通讯作者: 李进金,博士,教授,主要研究方向为一般拓扑学、粗糙集、概念格.E-mail: jinjinlimnu@126.com.

作者简介: 孙晓燕,硕士,讲师,主要研究方向为一般拓扑学、粗糙集、概念格.E-mail:sxy96001@aliyun.com.

引用本文:

孙晓燕, 李进金. 基于程序性知识学习的项目状态转移函数与多分知识结构[J]. 模式识别与人工智能, 2022, 35(3): 223-242. SUN Xiaoyan, LI Jinjin. Item State Transition Functions and Polytomous Knowledge Structures Based on Procedural Knowledge Learning. Pattern Recognition and Artificial Intelligence, 2022, 35(3): 223-242.

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http://manu46.magtech.com.cn/Jweb_prai/CN/10.16451/j.cnki.issn1003-6059.202203003 或 http://manu46.magtech.com.cn/Jweb_prai/CN/Y2022/V35/I3/223

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