How To Teach Knowledge Management
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1 STATE UNIVERSITY OF NEW YORK COLLEGE OF TECHNOLOGY CANTON, NEW YORK MINS/CITA 430 Data and Knowledge Management Prepared by: Charles Fenner Revised by: Eric Cheng CANINO SCHOOL OF ENGINEERING TECHNOLOGY DEPARTMENT OF COMPUTER INFORMATION SYSTEMS Spring 2012
2 Revised 02/2012 MINS/CITA 430 Data and Knowledge Management A. TITLE: Data and Knowledge Management B. COURSE NUMBER: MINS/CITA 430 C. CREDIT HOURS: 3 credit hours D. WRITING INTENSIVE COURSE (OPTIONAL): N/A E. COURSE LENGTH: 15 weeks F. SEMESTER(S) OFFERED: Fall/Spring G. HOURS OF LECTURE, LABORATORY, RECITATION, TUTORIAL, ACTIVITY: 3-hour lectures per week H. CATALOGUE DESCRIPTION: This course focuses on the development of a knowledge-management system using an organization s tacit and explicit knowledge to execute its strategy. The course explores practices entailed in developing a knowledge infrastructure, managing the interaction of people and technology, valuing knowledge assets, leveraging teams, and transferring knowledge across organizations. I. PRE-REQUISITES/CO-COURSES: MINS 300 Management Information Systems or permission of the instructor J. GOALS (STUDENT LEARNING OUTCOMES): By the end of this course, the student will be able to: Course Objective a. Interpret the concepts of knowledge management b. Analyze knowledge processes within an organization c. Evaluate approaches that organizations may take to make a significant contribution to an organization s knowledge processes, and analyze the issues involved d. Apply knowledge management technologies to make intellectual capital decisions in knowledge intensive organization e. Analyze the underlying impact of macroeconomic industry and organizational effects issues on human capital metrics f. Analyze practical situations, preparing, and proposing recommendations for enhancement of Institutional SLO 3. Prof. Competence 3. Prof. Competence
3 knowledge management within an organization K. TEXTS: Irma Becerra-Fernandez, Avelino Gonzalez, Rajiv Sabherwal, Knowledge Management Challenges, Solutions, and Technologies, Pearson Education Inc., Prentice Hall (2004). L. REFERENCES: Elias M. Awad, Hassan M. Ghaziri, Knowledge Management, Pearson Education Inc., Prentice Hall (2004). Irma Becerra-Fernandez, Dorothy Leidner (Editor), Knowledge Management: An Evolutionary View, Publisher: M E Sharpe Inc (2008) Ian Watson (2002). Applying Knowledge Management: Techniques for Building Corporate Memories. Morgan Kaufmann. Madanmohan Rao (2004). Knowledge Management Tools and Techniques: Practitioners and Experts Evaluate KM Solutions. Butterworth-Heinemann. Amrit Tiwana (2002). The Knowledge Management Toolkit: Orchestrating IT, Strategy, and Knowledge Platforms (2nd Edition). Prentice Hall. Stuart Barnes (ed) (2002). Knowledge Management Systems Theory and Practice. Thomson Learning. Chris Collison, Geoff Parcell (2001). Learning to Fly: Practical Lessons from one of the World's Leading Knowledge Companies. Capstone. Peter F. Drucker, David Garvin, Leonard Dorothy, Straus Susan, John Seely Brown (1998). Harvard Business Review on Knowledge Management. Harvard Business School Press. Thomas H. Davenport, Laurence Prusak (2000). Working Knowledge. Harvard Business School Press. ISBN: M. EQUIPMENT: Technology Enhanced Classroom N. GRADING METHOD: Standard A-F grading O. MEASUREMENT CRITERIA/METHODS:
4 Essays, quizzes, exams. P. DETAILED TOPICAL OUTLINE: See attached sheet Q. LABORATORY OUTLINE: N/A
5 DETAILED TOPIC OUTLINE MINS 430 Data and Knowledge Management I. Principles of Knowledge Management TOPICS A. Overview of Knowledge Management 1. What is Knowledge Management 2. Knowledge Management Systems B. The Nature of Knowledge 1. Alternative Views of Knowledge 2. Different Types of Knowledge 3. Locations of Knowledge C. Knowledge Management Solutions 1. Knowledge Management Processes 2. Knowledge Management Systems 3. Knowledge Management Infrastructure D. Organizational Impacts of Knowledge Management 1. Impact on People 2. Impact on Processes 3. Impact on Products 4. Impact on Organizational Performance E. Factors Influencing Knowledge Management 1. Effects of Task Characteristics 2. Effects of Knowledge Characteristics 3. Effects of Organizational and Environmental Characteristics F. Knowledge Management Assessment of an Organization 1. Types of KM Assessment 2. Assessment of Knowledge Management Solutions 3. Assessment of Knowledge 4. Assessment of Impacts II. Knowledge Management Technologies A. Technologies to Manage Knowledge 1. Artificial Intelligence 2. Digital Libraries, 3. Repositories
6 B. Preserving and Applying Human Expertise: Knowledge-Based Systems 1. Representing Knowledge 2. Automated Reasoning Process 3. Developing Knowledge-Based Systems 4. Knowledge-Based System Tools C. Using Past History Explicitly as Knowledge: Case-Based Systems 1. Weaknesses of Rule-Based Systems 2. Basic Concepts in Case-Based Reasoning 3. Indexing and Case Library Organization 4. Matching and Retrieval 5. Evaluation and Adaptation D. Knowledge Elicitation: Converting Tacit Knowledge to Explicit 1. Manual Knowledge Elicitation 2. Facilitating the Knowledge Elicitation Process 3. Automating the Knowledge Capture Process E. The Computer as a Medium for Sharing Knowledge 1. World Wide Web 2. WEB Search Engines 3. Network Security 4. Workflow Systems 5. Document Management via the Web F. Discovering New Knowledge: 1. Symbolic Approach 2. Artificial Neural Networks 2. Statistical Methods III. Knowledge Management Systems A. Knowledge Discovery: Systems that Create Knowledge 1. Designing the Knowledge Discovery Systems 2. Discovering Knowledge on the Web 3. Data Mining and Customer Relationship Management 4. Barriers to the Use of Knowledge Discovery Systems B. Knowledge Capture Systems: Systems that Preserve and Formalize Knowledge; 1. Concept Maps 2. Context Based Reasoning 3. Barriers to the Use of Knowledge Capture Systems C. Knowledge Sharing Systems: Systems that Organize and Distribute Knowledge 1. Designing the Knowledge Sharing Systems 2. Lessons Learned Systems
7 3. Barriers to the Use of Knowledge Sharing Systems D. Knowledge Application Systems: Systems that Utilize Knowledge 1. Technologies for Knowledge Application Systems 2. Developing Knowledge Application Systems 3. Types of Knowledge Application Systems 4. Limitations of Knowledge Application Systems IV. The Future of knowledge management A. Protecting Intellectual Property B. Knowledge Management: A New Paradigm for Decision Making C. Looking at the Future
STATE UNIVERSITY OF NEW YORK COLLEGE OF TECHNOLOGY CANTON, NEW YORK MINS/CITA 315. Decision Support Systems
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