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|本期目录/Table of Contents|

基于遗传算法的自动组题在心血管生理笔试中的应用与评价

《心脏杂志》[ISSN:1009-7236/CN:61-1268/R]

期数:
2018年第2期
页码:
244-247
栏目:
医学教育
出版日期:
2018-02-15

文章信息/Info

Title:
Application of genetic algorithm-based automatic test in cardiovascular physiology
作者:
张 星1; 贾 敏2; 杨红燕1; 邢文娟1; 董 玲1; 高 峰1
(第四军医大学：1.航空航天医学系,2.生理学教研室，陕西 西安 710032)
Author(s):
ZHANG Xing1;  JIA Min2;  YANG Hong-yan1;  XING Wen-juan1;  DONG Ling1;  GAO Feng1
(1.Department of Aerospace Medicine, 2.Department of Physiology, Fourth Military Medical University, Xi’an 710032, Shaanxi, China)
关键词:
自动组题; 遗传算法; 笔试; 教学评价; 心血管生理
Keywords:
automatic test;  genetic algorithm;  examination paper;  teaching evaluation;  cardiovascular physiology
分类号:
R331.3
DOI:
-
文献标识码:
A
摘要:
目的和方法计算机辅助教学的一个潜在应用就是从题库中自动组题，遗传算法(GA)被证明是比较有效的自动组题算法之一。我们通过GA构建了生理学自动组题系统，生成了8套满足用户要求的心血管生理试题，并对该系统进行了初步评价。结果 研究结果显示GA可以有效控制试卷的质量，采用满足同一要求的不同试卷对同一受试者进行测试时，测试结果误差控制在10%以内，对不同受试者采用不同试卷进行测试时，其结果无显著差异。而基于随机算法生成的不同试卷对受试者进行测试时，其结果存在显著差异。结论 基于GA的自动组题可以实现非试卷依赖性的学生能力和知识水平测试。
Abstract:
AIM and METHODSDatabase-based automatic test, a potential application of computer-aided instruction, generates customer-defined examination paper to evaluate students’ ability and knowledge and avoids subjective factors involved in examination. However, database-based automatic test is not widely used related to lack of well-established algorithms. Genetic algorithm has been reported as one of the most effective algorithms in automatic test. Thus, the present investigation established a genetic algorithm-based automatic test system and used this system in generating examination papers in evaluation of students’ ability and knowledge in cardiovascular physiology. RESULTS The results demonstrate that the system could well control the quality of examination papers. The result variation of different examination papers generated at the same level used for the same examinees was less than 10% and no significant difference was found when different papers were used for different examinees, while randomized algorithm produced significant difference. CONCLUSION Genetic algorithm-based automatic test system can be used in evaluation of students' ability and knowledge, which is independent of the paper itself.

参考文献/References

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[2]蔺长旺.教育测试新理论-IRT的研究与应用[J].外语教学与研究,2000,32(5):352-355.

[3]李卫东,黄河笑,郭俊文.IRT 在自适应考试中的应用[J].计算机工程,2001,27(7):179-181.

[4]Goldberg DE.Genetic algorithms in search, optimization and machine learning[M]. New Jersey,Addison-Wesley,1989.

[5]全惠云,范国闯,赵霆雷.基于遗传算法的试题库智能组卷系统研究[J].武汉大学学报(自然科学版),1999,45(5):758-760.

[6]张 星,廖琪梅.自动组题算法中适应度函数的构建方法[J].计算机工程与科学,2007,29(1):88-93.

备注/Memo

备注/Memo:
收稿日期：2016-11-11.作者简介：张星，讲师，博士 Email:Lbmed@163.com
更新日期/Last Update: 1900-01-01