(Prerequisite: EDLS 8130) (3 Semester Hours) (Field experience is required) Analysis of knowledge management systems for complex organizations with applications for governance, leadership, and policy.
I. Purpose of the Course
Investigates the use of data systems for organizational management and policy development. Uses techniques of knowledge management systems, data mining, and forecasting tools to effectively integrate diverse data sets, such as demographics, facilities needs, planning documents, assessment data, human resource data, and financial data. Topics include the development and use of demographic models, GIS models, database design, forecasting tools, and simulation tools. Teaching strategies include case studies, readings, simulations, and skills development experiences.
II. Objectives of the Course
Upon the completion of this course students should be able to:
Demonstrate the basic concepts related to the field of knowledge management (KM), including the underpinning theoretical descriptions of what is knowledge and what is KM through product development.
Critique and improve existing KM systems and assess the organizational impacts that those systems are having.
Manage a KM assessment, and how to carry an organizational KM audit.
Use the intelligent technologies that are the foundation of many KM systems.
Demonstrate proficiency with the different types of KM systems: knowledge discovery, knowledge capture, knowledge sharing, and knowledge application systems, across technologies.
Provide an organization with tailored models for use of knowledge discovery systems and data mining , including how to apply some of the basic mining techniques to support decision-making.
III. Text and Additional Reading Resources
Texts (Required)
Hanke, John Dean W. Wichern. Business Forecasting and Student CD Package, 8th Edition. (2004) Prentice Hall.
Brown, J. S and Duguid, P. (2000) The Social Life of Information. Boston: Harvard Business School Press.
Texts (Optional)
Argyris, C. and Schon, D.A (1978) Organizational Learning: A Theory of Action Perspective. London: Addison-Wesley.
Becerra-Fernandez, Irma, and Avelino Gonzalez, Rajiv Sabherwal. Knowledge Management: Challenges, Solutions, and Technologies. (2004) Prentice Hall.
Burgoyne, J. G., Pedler, M. and Boydell, T. (1993) Towards the Learning Company: Concepts and Practices. London: McGraw-Hill.
Davenport, and Prusak, L. (1998) Working Knowledge. HBS Press
Dixon, N (1999) The Organizational Learning Cycle: How We Can Learn Collectively? (2nd Edn.) London: McGraw-Hill.
Easterby-Smith, M. and Lyles, M (2003) Blackwell Handbook of Organizational Learning and Knowledge Management. Oxford: Blackwell.
Easterby-Smith, M., Burgoyne, J. & Araujo, L. (1999) Organizational Learning and the Learning Organization. London: Sage.
Easterby-Smith, M., Snell, R. and Gherardi, S. (Eds)(1998) 'Organizational learning and the learning organization: Diverging communities of practice?' Management Learning, Special Issue, 29(3).
Krogh, G and Roos, J (Eds) (1996) Managing Knowledge: Perspectives on Cooperation and Competition. London: Sage.
Lave, J. and Wenger, E. (1991) Situated Learning: Legitimate Peripheral Participation. Cambridge: Cambridge University Press
Moingeon, B. and Edmondson, A. (1996) Organizational Learning and Competitive Advantage. London: Sage
Newell, S., Robertson, M., Scarbrough, H. and Swan, J. (2002) Managing Knowledge Work. Basingstoke: Palgrave.
Nonaka, I. and Takeuchi, H. (1995) The Knowledge-Creating Company: How Japanese Companies Create the Dynamics of Innovation. Oxford: Oxford University Press.
Pedler, M., Burgoyne, J. and Boydell, T. (1991) The Learning Company: A Strategy for Sustainable Development. London: McGraw-Hill.
Semler, R. (1993) Maverick! The Success Story Behind the World's Most Unusual Workplace. London: Arrow
Senge, P. (1991) The Fifth Discipline: The Art and Practice of the Learning Organization. London: Century Business
Swieringa, J. and Wierdsma, A. (1992) Becoming a Learning Organization: Beyond the Learning Curve. Wokingham: Addison-Wesley.
IV. Prerequisite Skills for the Course: Educational Research and Introduction to Statistics
Class Attendance and Participation: Class attendance and preparation is mandatory. Students are expected to attend all the classes, except when precluded by emergencies. If you will be absent from class for any reason, please notify me in advance.
Student Accommodations Under the Americans with Disabilities Act: See UHCL Catalog
Academic Honesty Policy: See UHCL Catalog
VI. Week-to-Week Schedule
Week 1. Introduction to Knowledge Management and Forecasting
Week 2: Forecasting: An Introduction Reading: Hanke, Ch 1
Week 3: Knowledge Management: A Review of Basic Data and Statistical Concepts Reading: Hanke, Ch 2 Case #1: Using Knowledge management in an educational setting
Week 4: Exploring Data Patterns and Choosing a Forecasting Technique. Reading: Hanke, Ch 3 Quiz #1
Week 5: Demographics, Moving Averages and Smoothing Methods Reading: Hanke, Ch 4 Case #2: Demographic analysis for school districts: Long term planning
Week 6: Knowledge Management through Time Series and Their Components Reading: Hanke, Ch 5
Week 7: Simple Linear Regression and Multiple Regression Analysis Part I Reading: Hanke, Ch 6 Case#3: The Why's of District Management
Week 8: Simple Linear Regression and Multiple Regression Analysis Part II Reading: Hanke, Ch 7
Week 9: Simple Linear Regression and Multiple Regression Analysis Part III Quiz #2
Week 10: Use of GIS Systems for policy and forecasting, Part I. Case #4: The GIS Case Study
Week 11: Use of GIS Systems for policy and forecasting, Part II Quiz #3
Week 12: Judgmental Forecasting and Forecast Adjustments. Case #5: Forecasting Simulations Reading: Hanke, Ch 8,9
Week 13: Managing the Forecasting Process. Reading: Hanke, Ch 10
Week 14: Presentation of projects Project paper due
Untitled Document
If you have any questions, please contact the Office of Academic Advising at 281/283-3600 or email at Education@uhcl.edu
