王战军,1956年生,现任北京理工大学学位与研究生教育研究中心主任,教授、博士生导师;清华大学教授、博士生导师;教育部高等教育教学评估中心副主任。
30多年来,一直从事高等教育管理、教学、科研与评估研究;主要研究方向为主要研究方向为高等教育管理与评估、教育发展战略、管理信息系统等;曾策划、研制并组织开展多项全国学位与研究生教育评估项目;组织参与多项全国高等教育评估项目;主持参与并完成国家自然科学基金和国家社会科学基金项目9项;发表论文90多篇,其中代表性的有:《中国高等教育质量保障的新理念和新制度》(《清华大学教育研究》2014.3)、《加强研究生教育科学研究促进研究生教育改革与发展》(《学位与研究生教育》2014.8)、《我国研究生教育的国际影响力》(《国家教育行政学院学报》,2013.2)、《研究生教育评估新思维》(《清华大学教育研究》,2012.1)、《研究生质量评估:模型与框架》(《高等教育研究》,2012.3)、《建立健全新时期研究生教育质量保障体系》(《中国高等教育》2012.6)、《中国高水平大学50强》(《教育研究》,2010.10);出版著作多部,代表性的有《中国研究生教育研究进展报告(2013)》《中国研究生教育质量年度报告(2013)》《学位与研究生教育评估理论与方法》《研究型大学与高等教育强国》、The Construction and Development of Research UniversitiesinChina,《学位与研究生教育评估技术与实践》等。
目录
Introduction Education Evaluation Moving Towards the Era of Monitoring Evaluation
Chapter 1 Driving Forces of HEME
1.1 Theoretical evolution and developmental space of education evaluation
1.2 Institutional reasons for emerging monitoring evaluation in higher education
1.3 Technical power for applying HEME
Chapter 2 Fundamental Connotation of HEME
2.1 Theoretical implications of HEME
2.2 Typical characteristics of HEME
2.3 The main purpose of HEME
2.4 Basic functions of HEME
Chapter 3 Implementing Approaches to HEME
3.1 The organization mode for HEME
3.2 Implementation steps and conditions of HEME
3.3 System guarantee for HEME
Chapter 4 Data Collection and Processing in HEME
4.1 Data scope and classification in HEME
4.2 Data collection methods in HEME
4.3 Data cleaning, data integration and data reduction
4.4 Data warehouse and platform of monitoring evaluation
Chapter 5 Data Analysis and Data Mining in HEME
5.1 Method overview for data analysis and data mining in monitoring evaluation
5.2 Data analysis methods and models for space status monitoring
5.3 Data analysis methods and models for time status monitoring
5.4 Data mining and big data analysis in monitoring evaluation
5.5 Cloud-based data processing for monitoring and evaluation
Chapter 6 Data Visualization in HEME
6.1 The meaning and functions of data visualization
6.2 Visualization methods of single-factor data
6.3 Data visualization methods for double factors
6.4 Data visualization methods for three factors
6.5 Data visualization methods for multi-factors
6.6 The quality monitoring tree method for full factors
Chapter 7 Monitoring and Early Warning of Graduate Education in Colleges and Universities
7.1 Monitoring purpose and object
7.2 Models for monitoring and early warning
7.3 Analysis and discussion on monitoring results
7.4 Application discussion on monitoring method
Chapter 8 Monitoring Teaching Resources in Colleges and Universities Based on Online Analysis Data Mining
8.1 Monitoring purpose and objects
8.2 Monitoring data and indicators
8.3 Monitoring of course resources
8.4 Monitoring of teaching human resources
8.5 Monitoring of teaching time input
8.6 Monitoring of class size in teaching
8.7 Discussion on monitoring method application
Chapter 9 University Teaching Quality Monitoring Based on Quality Monitoring Tree
9.1 Monitoring purpose and objects
9.2 The basic principle of quality monitoring tree construction
9.3 University teaching quality monitoring based on monitoring tree
9.4 Analysis and discussion on monitoring results
Chapter 10 Case Study on Higher Vocational Education Monitoring Evaluation
10.1 Monitoring purpose and objects
10.2 Methods for teaching monitoring evaluation in higher vocational colleges
10.3 Case study on teaching quality monitoring in higher vocational colleges
10.4 Discussion on application of monitoring platform
Chapter 11 Graduate Student Education Monitoring Based on Big Data from Social Media
11.1 Monitoring purpose and objects
11.2 Quality monitoring methods and data sources for graduate student education
11.3 Results and discussion of quality monitoring method for graduate student education
Chapter 12 Outlook
12.1 Information society gives rise to HEME
12.2 HEME is achieved by a powerful nation of education
12.3 Monitoring evaluation will become the main type and mainstream paradigm of education evaluation in the world
Reference
Afterword