Course name:Applied Mathematics for Library and Information Science
Teaching medium: Bilingual teaching of Chinese and English
A. COURSE DESCRIPTION
2 credit, 24 Class Hours, 2rd Semester
This course mainly introduces the application of mathematical methods to characterize and analyze knowledge organization, bibliometrics and user behavior in library and information science, including the basic knowledge of higher mathematics, mathematical statistics, knowledge organization, retrieval language model, information analysis, bibliometrics laws, users’ data analysis and more. So that graduate students can master the basic mathematical and statistical methods applied to the research and practice of library and information science by class teaching and case analysis, cultivate the thinking of combining quantitative analysis with qualitative analysis and improve the comprehensive ability of analyzing and solving practical problems. It will lay a solid foundation for improving the level of graduate students’ research.
B. METHOD OF INSTRUCTION
PPT/Case study
C. COURSE OBJECTIVES(five or six objectives; general but comprehensive)
Objective 1:From the perspective of information management and analysis, let students be familiar with and master the basic knowledge and methods of higher mathematics and mathematical statistics, and understand the learning objects, teaching scope and teaching objectives of this course.
Objective 2:From the perspective of literature resource management, students should master the five laws of bibliometrics and their application fields, and be familiar with the main contents of resource management and resource evaluation.
Objective 3:Understand the main connotation and standard system of library resources and service evaluation, and be familiar with the methods and processes of library resources and service evaluation by using bibliometrics and basic statistical methods.
Objective 4:Understand the structure and modeling of literature and knowledge elements, be familiar with classic literature knowledge management model, and know the latest development direction of knowledge representation, knowledge modeling and knowledge mapping, so as to lay the foundation for content-based information organization and information analysis.
Objective 5:From the perspective of knowledge organization and knowledge extraction, we should understand the basic concepts and models of natural language processing technology, master the mathematical model of information retrieval language and its application in information retrieval system, and understand the whole process of knowledge unit from representation, modeling to topic extraction, correlation, retrieval, aggregation and graph display.
D. COURSE TOPICS/UNITS AND DATES
Topic 1: Advanced mathematics foundation (4 class hours)
Topic 2: Common mathematical statistical methods (4 class hours)
Topic 3: Knowledge element and knowledge modeling (4 class hours)
Topic 4: Natural language model and processing basis (4 class hours)
Topic 5: Information retrieval language and information retrieval system (4 class hours)
Topic 6:Mathematical basis and application of Bibliometrics (2 class hours)
Topic 7: Library resources and service evaluation (2 class hours)
E. TEXTBOOK(S) AND REQUIRED TOOLS OR SUPPLIES
Textbook (required):Zou Xiaoshou, Wang Xiaofeng, Deng Luohua. Applied mathematics for LIS[M]. Peking: China National Library Press, 2012.
Supplies and/or tools:
[1] Chen Heping. Quantitative Management in Library[M]. Chendu: Southwest Jiaotong University Press,1989.
[2] Tan Guolv. Higher Mathematics of Liberal Arts[M].Peking: Beihang University Press,2009.
[3] Yan Dagui, Yan Shang’An. Applied Mathematics for Engineering graduate students[M]. Peking, Higher Education Press, 2001.
F. GRADING PLAN
Course assessment:(1) 40 points for usual performance (attendance and interaction in class) ;(2) 60 points for case analysis report.
Classification criteria:
(1) 60-70: basically complete the teaching task of this course and preliminarily master the concepts and methods related to advanced mathematics and statistics;
(2) 71-80: complete the teaching contents and tasks in this course, and have a relatively comprehensive understanding of information retrieval and information resource management;
(3) 81-90: be able to complete the teaching tasks in this course and have a deep understanding of information analysis technology and methods;
(4) 91-100: can perfectly solve the teaching tasks in this course and can skillfully use information management technology to carry out information retrieval, information collection and information analysis, and library evaluation for performance and effectiveness.
G. COURSE COMPONENT SPECIFICS
Chapter I Advanced Mathematics Foundation (I) （2 hours）
1．Single variable differential calculus
2．Single variable integral calculus
3．Infinite series
Requirements for students:
Master the concepts of derivative, differential, integral and can operate.
Chapter II Advanced Mathematics Foundation (II) （2 hours）
1．Linear equations
2．Determinants
3．Matrix
4．Vector space
Requirements for students:
Master the computational methods of Linear equations, Matrix.
Chapter III Applied Mathematical Statistics (I) （2 hours）
1．Statistics
2．Sampling distribution
3．Parametric estimation
4．Hypothesis testing
Requirements for students:
Master the sampling methods,and can calculate formula, meaning and application of mean, median, variance and standard deviation.
Chapter IV Applied Mathematical Statistics (II) （2 hours）
1．Analysis of variance
2．Regression analysis
3．Time series：a case of documental grow
Requirements for students:
Master the basic principle and calculation method of linear regression equation.
Chapter V Literature and Knowledge Modeling （2 hours）
1．Data, information and knowledge
2．Metadata
3．Literature organization
4．knowledge organization
Requirements for students:
Master the principles and methods of document organization and knowledge organization.
Chapter VI Natural Language Understanding （2 hours）
1．Natural language composition
2．Lexical analysis
3．Semantic analysis
Requirements for students:
Understand the basic concepts and technical methods of natural language processing and read recent research papers in LIS.
Chapter VII Relationship Model （2 hours）
1．Relational data structure and definition
2．Relational algebra
3．Relationship evolution
4．Relational data
Requirements for students:
Understand the basic concepts of Relationship Model and read recent research papers in LIS.
Chapter VIII Mathematical Model of Information System （2 hours）
1．Concept space
2．Algebraic model of retrieval system
3．Collection model of retrieval system
4．Probabilistic model of retrieval system
Requirements for students:
Understand the principles of models of retrieval system.
Chapter IX Mathematical Modeling of Retrieve Language (2 hours）
1．Mathematical model of theme methods
2．Mathematical model of taxonomy
3．The application of mathematical model of retrieval language
Requirements for students:
Master the basic principle of Models of Retrieve Language.
Chapter X Library Resource Evaluation （2 hours）
1．Paper resource statistics
2．Electronic resources statistics
3．Performance evaluation of book resources
Requirements for students:
Understand the types and characteristics of library information resources, master the statistical evaluation methods of printed and electronic resources.
Chapter XI Library Service Evaluation （2 hours）
1．Open time design and evaluation
2．Statistics and Analysis of Readers in library
3．Service satisfaction survey
Requirements for students:
Understand all kinds of library services, and learn how to survey readers' satisfaction.
Chapter XII The Theory and Application of Bibliometrics （2 hours）
1．The mathematical basis of bibliometrics
2．The theory and application of Bradford's law
3．The theory and application of Zipf's law
Requirements for students:
Master the three laws of bibliometrics and understand their application fields.
