Handbook of Operations Research and Management Science in Higher Education

Description

This handbook covers various areas of Higher Education (HE) in which operations research/management science (OR/MS) techniques are used. Key examples include: international comparisons, university rankings, and rating academic efficiency with Data Envelopment Analysis (DEA); formulating academic strategy with balanced scorecard; budgeting and planning with linear and quadratic models; student forecasting; E-learning evaluation; faculty evaluation with questionnaires and multivariate statistics; marketing for HE; analytic and educational simulation; academic information systems; technology transfer with systems analysis; and examination timetabling. Overviews, case studies and findings on advanced OR/MS applications in various functional areas of HE are included. Preface The Contents In Summary Audience Acknowledgments Contents About the Editor and Contributors About the Editor Contributors Part I Reviews/Overviews 1 Operations Research and Management Science in Higher Education: An Overview 1.1 Introduction 1.2 OR/MS Publications 1.2.1 Reviews on OR in HE 1.2.2 HE in OR Journals 1.3 OR Methods and Functional Areas in HE: Articles in the WoS 1.3.1 OR Methods in HE: Articles Frequency 1.3.1.1 Groups of OR Methods 1.3.1.2 Specific OR Methods 1.3.2 Functional Areas in HE: Articles Frequency 1.3.2.1 Groups of Functional Areas 1.3.2.2 Specific Functional Areas 1.3.3 Group of OR Methods by Group of Functional Areas 1.3.4 Specific OR Methods by Specific Functional Areas 1.4 Issues in the HE Sector 1.4.1 Is HE a Product? 1.4.2 University–Industry Relations 1.4.3 Student Debt 1.4.4 Internationalization in HE 1.4.5 EduTech and the Fourth Industrial Revolution 1.4.6 The Pandemic and EduTech 1.4.7 Planning in HE 1.5 Summary and Conclusions Appendix 1: The number of articles published in 40 OR journals with HE in their title during 1980–2019 Appendix 2: The number of articles published in WoS Journals with HE and OR methoda 2000–2019 Appendix 3: The number of articles published in WoS journals with HE and functional areaa during 2000–2019 References 2 A Systematic and Updated Review of the Literature on Higher Education Marketing 2005 –2019 2.1 Introduction 2.2 Key Issues from the 2006 Literature Review 2.3 Methodology 2.4 Findings 2.4.1 The Marketization of Higher Education 2.4.2 Marketing Communications 2.4.2.1 Advertising 2.4.2.2 Social Media Marketing 2.4.3 Branding, Image, and Reputation 2.4.3.1 Image and Reputation 2.4.4 Marketing Strategy 2.4.4.1 Market Orientation 2.4.4.2 Segmentation, Targeting, and Positioning (STP) 2.4.4.3 The Marketing Mix 2.4.4.4 Relationship Marketing Strategy 2.4.5 Recruitment, Alumni, and Gift-Giving 2.5 Discussion Appendix 1 Summary of articles on marketization for the literature review Appendix 2 Summary of articles on marketing communications, advertising, and social media marketing for the literature review Appendix 3 Summary of articles on branding, image, and reputation for the literature review Appendix 4 Summary of articles on marketing strategy, market orientation, segmentation, targeting and positioning, marketing mix and relationship marketing for the literature review Appendix 5 Summary of articles on recruitment, alumni, and gift-giving for the literature review References 3 Overview of Simulation in Higher Education: Methods and Applications 3.1 Introduction 3.2 Analytic Simulations for Managerial Planning in HE 3.2.1 System Dynamic (SD) 3.2.2 Discrete-Event-Simulation (DES) and Monte Carlo 3.2.3 Agent-Based Simulation (ABS) 3.2.4 Deterministic and Other Analytic Simulation Models 3.2.5 Combining Analytic Simulation with OR and Other Methods 3.3 Educational Simulation in Higher Education (HE) 3.3.1 Educational Simulation by Method 3.3.1.1 Virtual Learning 3.3.1.2 Simulator 3.3.1.3 Games 3.3.1.4 Box Trainer 3.3.1.5 Manikin 3.3.2 Educational Simulation by Discipline 3.3.2.1 Health Disciplines 3.3.2.2 Simulations in Business Management 3.3.2.3 STEM Disciplines 3.4 Counting Articles About Simulation Methods and Their Area of Application in HE 3.4.1 Number of Articles on Simulation Methods 3.4.2 Number of Articles on Application Areas 3.4.3 Number of Articles on Simulation Methods by Application Areas in Higher Education 3.5 Summary and Conclusions Appendix 1: Synonyms for: (a) Higher Education (b) Simulation Method (c) Areas of Application Appendix 2: Taxonomy of Simulation Models Applied to Higher Education References 4 Managing Minds: The Challenges of Current Research Information Systems for Improving University Performance 4.1 Introduction 4.2 Context: Accountability and Transparency of Research Outputs 4.3 Libraries Are Not Universities: The Problematic Evolution of CRIS Programs 4.4 Academic Managerial Routines in Need of Reform 4.4.1 Recruitment of New Faculty 4.4.2 Promotion Procedures 4.4.3 Preparing Annual Reports 4.4.4 Preparing Accreditation and Assessment Portfolios 4.5 Conclusions References 5 Forecasting Methods in Higher Education: An Overview 5.1 Introduction 5.2 Review of Forecasting Methods: Passive Versus Active Forecasting 5.2.1 Time Series 5.2.2 Ratio Methods 5.2.3 Regression Analysis 5.2.4 Simulation Models 5.2.5 Data Mining (DM) 5.2.6 Questionnaires and Surveys 5.3 Types of Classifications of Forecasting Methods in HE 5.3.1 New Classification: Active Versus Passive Forecasting Methods 5.3.2 Other Classifications of Forecasting Methods 5.3.3 Managerial and Planning Aspects of Forecasting 5.4 Counting Number of Articles on Forecasting in HE by Method and by Area 5.4.1 Counting Number of Articles on Each Forecasting Method in HE 5.4.2 Counting Number of Articles in HE, and Forecasting, by Main Areas of Application 5.5 Summary and Conclusions Appendix 1 Appendix 2 Appendix 3 References 6 Survey of Methods for Ranking and Benchmarking Higher Education Institutions 6.1 Introduction 6.2 International Rankings of Universities 6.3 Training HEI Managers on OR Methodologies 6.4 Higher Education Institution Benchmarking Interactive Tool (HEIBIT) 6.4.1 Indicators Selection and PCA 6.4.2 Weighting and AHP 6.4.3 Normalization 6.4.4 Aggregation via PROMETHEE 6.4.5 Efficiency Analysis via DEA 6.4.6 Dynamic Efficiency Analysis via Malmquist Index 6.4.7 Global Sensitivity Analysis 6.5 Main Drawbacks of OR/MS Techniques in Ranking and Benchmarking of HEI Education Institutions 6.6 Conclusions and Future Development References 7 Balanced Scorecard in Strategic Planning of Higher Education: Review 7.1 Introduction 7.2 BSC Adapted to HE 7.2.1 BSC Perspectives in HE 7.2.2 Goals and Measures in HE for 4 BSC Perspectives 7.2.3 External and Internal Audiences in HE 7.3 Literature Review of BSC in HE 7.3.1 Articles of BSC in Various Levels of HE Systems 7.3.2 Country of BSC Applications 7.3.3 BSC Integrated with Other Methodologies 7.3.4 BSC Use for Quality Assurance 7.4 Advantages and Disadvantages of BSC in HE 7.5 Summary and Conclusions References 8 System Analysis of Technology Transfer Policies and Models in Higher Education 8.1 Introduction 8.2 The “Third Mission” and the Entrepreneurial University 8.3 Technology Commercialization 8.4 Technology Transfer 8.4.1 Mechanisms 8.4.2 Quantification Mechanisms of Technology Transfer 8.5 Case Study: Knowledge Transfer Processes in the Fields of Artificial Intelligence, Data Science, and Smart Robotics 8.6 Policy Implications: Technology Transfer Versus Technology Commercialization 8.6.1 Income Generation Versus Technology Diffusion 8.6.2 New Policies and TTO/TLO's Missions 8.6.3 IP Protection Policies and the Bayh–Dole Act 8.7 Concluding Remarks References 9 Models for Planning and Budgeting in Higher Education 9.1 Introduction 9.2 Types of Budgeting Systems 9.2.1 Incremental Budgeting (IB) 9.2.2 Zero-Based Budgeting (ZBB) 9.2.3 Planning–Programming–Budgeting System (PPBS) 9.2.4 Formula Budgeting 9.2.5 Responsibility-Centered Budgeting (RCB) 9.3 Expenses and Income in HEI Budgets—Estimation/Simulation 9.3.1 Budget Expense Structure 9.3.2 HEI Budget Income 9.3.3 Determining Versus Predicting in Budget Planning 9.3.4 Incentives for Maximal Performance in HE Via Budgeting 9.3.5 Planning in HE Versus Budgeting 9.3.6 Simulation/Optimization Scheme of Budget Planning in HEI 9.4 Optimization Models for Budget Allocation 9.4.1 Linear Model with One Period and One Hierarchical Level 9.4.2 Quadratic Model with One Period and One Hierarchical Level 9.4.2.1 Fairness of the Quadratic Versus the Linear Models for Budget Allocation 9.4.2.2 Dynamic Quadratic Budget Allocation with Only Bounds Constraints 9.4.2.3 Goal Programming and Absolute Value of the Objective Function 9.4.3 Linear Two-Level Hierarchical Budgeting Model Over Time 9.4.4 Linear Multilevel Hierarchical Linear Network Budgeting Model 9.4.5 Chance Constraint 9.5 Aggregated University Long-Run Budget Planning Model 9.6 Summary and Conclusions Appendix 1: Summary of the Literature on Planning and Budgeting in HE Appendix 2: Example for Linear and Quadratic Budget Allocation Modes References Part II New Methodologies 10 Funding Research in Higher Education Institutions: The Game Theory Approach 10.1 Introduction and Scientific Background 10.2 Background 10.2.1 Budget Allocation Problem in Higher Education 10.2.2 Budget Partition and Fairness Index 10.2.3 Efficiency 10.2.4 Literature References to Game Theory Applications in Higher Education 10.3 The Proposed Contest Model 10.4 The Bargaining Model for Budget Division 10.5 Contest Versus Budget Division 10.6 The Case of Several AUs with Identical Abilities 10.7 A Numerical Example 10.8 Conclusions References 11 A Fast Threshold Acceptance Algorithm for the Examination Timetabling Problem 11.1 Introduction 11.1.1 Related Work 11.1.2 This Chapter's Contribution 11.2 Notations and Abbreviations 11.3 Examination Timetabling Problems 11.3.1 Uncapacitated Toronto Dataset 11.3.2 The ITC 2007 Dataset 11.3.2.1 Mathematical Formulation 11.4 Proposed Approach 11.4.1 Threshold Acceptance Metaheuristic 11.4.2 Accelerated TA Metaheuristic for the ETP 11.4.2.1 Effect of the Cooling Schedule on the Exam Move Acceptance Rate 11.4.2.2 FastTA Algorithm for the ETP 11.4.3 Application to the Toronto Benchmark Set 11.4.3.1 Solution Representation and Fitness Function 11.4.3.2 Construction of Initial Feasible Timetables 11.4.3.3 Neighbourhood Operator 11.4.4 Application to the ITC 2007 Benchmark Set 11.4.4.1 Solution Representation and Fitness Function 11.4.4.2 Construction of Initial Feasible Timetables 11.4.4.3 Neighbourhood Operators 11.5 Experiments 11.5.1 Compared TA Variants 11.5.2 Parameter Settings 11.5.3 Comparison of FastTA and TA on the Toronto and ITC 2007 Sets 11.5.3.1 Discussion 11.5.4 Comparison with State-of-the-art Approaches—Toronto Dataset 11.5.4.1 Statistical Analysis 11.5.4.2 Discussion 11.5.5 Comparison with State-of-the-Art Approaches—ITC 2007 Dataset 11.5.5.1 Statistical Analysis 11.5.5.2 Discussion 11.6 Conclusions Appendix References 12 Robust Efficiency via Average Correlation: The Case of Academic Departments 12.1 Introduction 12.2 Robust Efficiency 12.3 Efficiency Methods Used for the Case Study 12.3.1 CRS Method 12.3.2 SE-CRS Method 12.3.3 VRS Efficiency Method 12.3.4 SE-VRS Method 12.3.5 CE-CRS Cross-Efficiency Method 12.3.6 Disc-CRS Efficiency Method Based on Discriminant Analysis 12.3.7 Disc-VRS Efficiency Method Based on Discriminant Analysis 12.3.8 CCA—Canonical Correlation Efficiency Method 12.3.9 REG—Regression SFA 12.3.10 Evaluation of the Efficiency Methods 12.4 The Case Study 12.5 Results of Academic Department Efficiencies 12.5.1 Choosing the Robust Efficiency Method 12.5.2 Robustness for Ranks of the Efficiencies 12.6 One Input and Three Outputs—Regression Efficiency 12.7 Conclusion Appendix 1 Appendix 2 Appendix 3 Appendix 4 Appendix 5 Appendix 6: The Regression Results for Calculating Regression Efficiency References Part III Applications 13 Course Evaluation Using Preference Disaggregation Analysis: The Case of an Information Communication Technology Course 13.1 Introduction 13.2 Theoretical Background 13.2.1 The Course Experience Questionnaire 13.3 Methodology 13.3.1 Sample 13.3.2 The MUSA Method 13.4 Results 13.4.1 Criteria Weights and their Variability 13.4.2 Satisfaction and Demanding Indices 13.4.3 Global Satisfaction Function 13.4.4 Partial Satisfaction Functions 13.4.5 Relative Action Diagram 13.4.6 Relative Improvement Diagram 13.5 Comparison Between Secondary and Tertiary Education Students' Course Evaluation 13.6 Conclusion Appendix 1: The Original and Adapted CEQ Items References 14 The Application of a Balanced Scorecard in Higher Education Institutions: A Case Study of Wuls 14.1 Introduction 14.2 A Short Presentation of the Warsaw University of Life Sciences 14.3 Context of BSC's Introduction 14.4 Choosing the General Approach 14.5 Defining the Project 14.6 Mission, SWOT, and Vision 14.7 Strategy Map 14.8 Operationalization and Further Development 14.9 Objective Results: Benchmark Analysis 14.10 Challenges and Lessons Learned References 15 E-Learning in Times of Crisis: An Incidental or FacilitativeEvent? 15.1 Introduction 15.2 Literature Review 15.2.1 Information and Communications Technology-based Teaching in Higher Education: For Whom? 15.2.2 Features of E-Learning 15.2.3 The Added Value of E-Learning: Indeed? 15.2.4 Technology as an Efficient Tool for Developing Learning Skills 15.3 The Challenges of the Transition to E-Learning 15.4 Research Method 15.4.1 Research Questions 15.4.2 Characteristics of the Sample 15.4.2.1 Student Characteristics 15.4.2.2 Characteristics of the Lecturers 15.5 Research Findings 15.5.1 Preferences Regarding E-Learning 15.5.1.1 Level of Preference 15.5.1.2 Preference for E-Learning by Features of Students' Studies 15.5.1.3 Type of Online Class Preferred by Students 15.5.1.4 Perception of E-Learning 15.5.1.5 Difficulties with Implementing E-Learning 15.5.1.6 Factors Affecting Personal Preference for E-Learning Among Students 15.5.1.7 Factors that Affect One's Personal Preference for E-Learning among Lecturers 15.6 Summary of the Findings and Discussion References 16 The Relative Efficiencies of Higher Education in OECDCountries 16.1 Introduction 16.2 Variables Affecting National HE Systems 16.2.1 The Economic Gains 16.2.2 Inputs and Output Chosen by Other OECD Efficiency Studies 16.2.3 Methodology—Robust Efficiency of Rank Scales in DEA Context 16.2.3.1 Constant Returns to Scale Efficiency and Inefficiency: Dichotomy 16.2.3.2 Variable Returns to Scale Efficiency and Inefficiency: Dichotomy 16.2.3.3 Super Efficiency Based on CRS and Cross Efficiency Rank-scaling Based on CRS Method 16.2.3.4 SE-VRS and CE-VRS Efficiency Based on VRS Rank-scaling Method 16.2.3.5 Disc/CRS and Disc/VRS Efficiency Rank-scaling Methods 16.2.3.6 Canonical Correlation Efficiency Rank-scaling Method 16.2.3.7 Regression Efficiency Rank-scale 16.2.3.8 Robust Efficiency 16.3 The Data on HE Inputs and Outputs of OECD Countries 16.4 Results 16.4.1 CRS Efficient and Inefficient Countries 16.4.2 VRS Efficient and Inefficient Countries 16.4.3 SE-CRS and SE-VRS Rank-scaling Efficiency Methods 16.4.4 CE-CRS and CE-VRS Rank-scaling Efficiency Methods 16.4.5 Multivariate Efficiency Results 16.5 The Case of 1 Input 16.6 Robust Solution 16.7 Other Explanatory Factors Affecting the Efficiency 16.8 Possible Risks of Over Access of HE 16.9 Summary and Conclusions Appendix 1: OECD data sources Appendix 2 Appendix 3: Regression of X1 with the 6 outputs Appendix 4: Efficiencya ranks of OECD countries Appendix 5: Correlations between efficiency models—Pearson and Spearman(rank) Appendix 6: Data of other explanatory variablesa Zi by OECD country References Index