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执业医师资格考试系统的实现与优化
Implementation and Optimization of National Medical Licensing Examination System
【作者】 刘海燕；
【作者基本信息】 山东师范大学 ， 工程硕士（专业学位）， 2019， 硕士
【摘要】 随着互联网技术与教育领域的相互碰撞和融合,教育教学工作已经从传统的课堂教学模式逐渐扩展至更加便捷的网络教学模式。在线考试系统能够跨越时空和地域的限制,实现对学生知识掌握程度的高效检验,已发展成为新型教育评测的重要方式。执业医师资格考试是保证医学类专业人才质量的重要考试,设计和实现符合其特征的在线考试系统可以帮助学生完善医学知识结构,提高全国统一性执业医师资格考试的通过率。本文在充分考虑执业医师资格考试特性与用户需求的基础上,设计和实现了一套基于B/S架构的执业医师资格考试系统,并使用人工智能算法与数据挖掘技术对其进行了优化。本文的主要工作总结如下:1.实现了执业医师资格考试系统。结合相关理论、技术和需求分析,确立了整体设计目标,并使用SSH框架实现和整合了考试系统的各个功能模块。2.设计了符合执业医师资格考试的题型。根据执业医师资格考试系统的要求,本文设计了七种题型,有助于促进题库管理的合理化和规范化。3.设计了基于改进遗传模拟退火算法的智能组卷策略。通过分析组卷的相关理论和要求,将改进的遗传模拟退火算法作为智能组卷策略,便于保证组卷的分数、题型、题量和难度等约束条件符合教学要求,使得组卷结果更加客观和科学。4.设置综合考试和自主测试两种考试方式。综合考试模块用于学生在固定考试时间内完成教师设置的试卷并提交。考试结束后教师和学生可以对自动评分结果进行查阅。自主测试模块帮助学生查缺补漏,巩固知识。学生可以结合自身情况选择课程、章节和题型题量抽取试题进行自测,交卷后系统自动评阅成绩,并显示正确答案。5.使用数据挖掘技术构建成绩分析平台。首先进行考情分析,采用多层次数据分析框架对成绩数据的相关指标进行可视化,便于教师了解学生的认知能力和知识水平;其次是基于改进的Apriori算法挖掘课程关联性,通过分析考试过程中产生的大量数据为教学管理提供决策依据,便于建设智慧化的考试管理模式,提升教学质量。
【Abstract】 With the collision and integration of Internet technology and education,the teaching work has gradually expanded from the traditional classroom teaching mode to the convenient online teaching mode.The online examination system can overcome the limitations of time,space and region,and further test the degree of knowledge of students effectively.It has become an important way of evaluating the education level.The National Medical Licensing Examination is critical to ensure the quality of medical professionals.To design and implement a online examination system that meets its characteristics can effectively help students to consolidate the structure of medical knowledge,and improve the pass rate of the National Medical Licensing Examination.Based on the characteristics of National Medical Licensing Examination and the needs of the practitioners,this paper designed an online examination system which is based on B/S structure.And the system was optimized by using artificial intelligence algorithms and data mining technology.The main work of this paper are as follows:1.A National Medical Licensing Examination system was implemented.This paper combined relevant theory,technology and needs analysis to establish the overall design goals,and made use of the SSH framework to integrate the various functional modules of the examination system.2.Question types that meet the requirements of National Medical Licensing Examination were designed.According to the requirements of the system,this paper designed seven types of questions,which will help to promote the rationalization and standardization of management of the question bank.3.An intelligent test paper generation strategy based on improved genetic simulated annealing algorithm was designed.By analyzing the theory and requirement of generating test papers,this paper regarded the improved genetic simulated annealing algorithm as an intelligent generating strategy,so as to ensure that the constraints such as score,type,quantity,difficulty and coverage of knowledge points of papers meet the requirements of education,and make the results of generating test papers more objective and scientific.4.Two kinds of examination methods were set up: comprehensive test and self-determination test.The comprehensive examination module is used for students to complete and submit test papers set by teachers within a fixed examination time.After the examination,teachers and students can check the results of automatic scoring by the system.The self-determination test module helps students check for missing and consolidate knowledge.Students can choose courses,chapters and questions taking their own situation into account,and extract the questions which meet the needs.After submitting the papers,the system automatically evaluates the results and displays the correct answers.5.The performance analysis platform was built by data mining technology.Firstly,this paper used multi-level data analysis framework to visualize the relevant indicators of performance data,so as to facilitate teachers to understand the cognitive ability and knowledge level of students.Secondly,based on the improved Apriori algorithm,the relevance between courses is mined and analyzed.It provides decision-making support for teaching management by analyzing a large number of data generated during the examination process,which facilitates the construction of an intelligent examination management model and improves the quality of teaching.