teaching
with technology - 2006 recipients
Department of Industrial Engineering
Web-Based Training Modules
for Undergraduate Students in Information Engineering
These modules will be used primarily to provide introductory material for students enrolled in IE 421, Information System Analysis and Design. This course is required for undergraduate students majoring in Industrial and Information Engineering historical enrollment has included a few students from the other Engineering Departments and the Statistics Department including some graduate students. We are going to develop more courses in Information Engineering since the name change of our department from Industrial Engineering to Industrial and Information Engineering. The modules proposed in this proposal will serve as fundamental knowledge of Information Engineering. We will add more modules or contents as more courses are added to our curriculum.
Students in other engineering disciplines could also benefit from this series of modules. The importance of Information Engineering for all engineers is evidenced by the Criteria for Accrediting Engineering Programs developed by ABET, the Accreditation Board for Engineering and Technology. Section 1, General Criteria for Basic Level Programs, Criterion 3, Program Outcomes and Assessment, states the following:
“Engineering programs must demonstrate that their graduates have: . . . (b) an ability to design and conduct experiments, as well as to analyze and interpret data”
http://www.abet.org/images/Criteria/eac_criteria_b.pdf
Most of the undergraduate engineering degree programs have one or more courses that would benefit from the use of this module series. In particular, students in the design-oriented courses and the capstone senior project courses should have some background in data acquisition, modeling, and analysis.
One additional constituency that would benefit from this module series – every year several graduate students are admitted to the MSIE program with undergraduate backgrounds from engineering disciplines other than IE. Most of these students are deficient in the area of data modeling and are required to take a prerequisite course that does not carry graduate credit prior to enrolling in their IE courses. This module series would provide an alternative method of satisfying the program deficiency.
Module 1: Introduction to Information Engineering
Information Engineering may be defined objectively as this field is relatively new and still considered an interdisciplinary area. The fundamental theories of Information Engineering from psychology to computer science will be briefly introduced in this module. An understanding of the fundamentals is thus essential for students in disciplines including engineering, business, and the sciences. This module provides the background for the subsequent modules by defining key concepts of Information Engineering including definitions of Information Engineering in various sources. Upon successful completion of the module, the student should be able to understand the essence of information and the needs of information engineering.
Module 2: Data Acquisition
There are several key concepts in data acquisition: randomness, sample size, and experimental design. Data acquisition plays an important role in engineering analyses. An understanding of these concepts is essential to their application in data analysis and in evaluating scenarios having uncertain outcomes. The Data Acquisition module provides the background for the subsequent modules by describing key concepts and discussing their properties, underlying assumptions, and potential applications. Upon successful completion of this module, the student should be able to design a data acquisition plan, understand how to acquire desired data sets, and understand the properties of the acquired data.
Module 3: Data Modeling
Data modeling is to develop an accurate model to analyze the data objects and represent their relationships. It is a crucial process in data representation and database design. The Data Modeling module provides the background for subsequent modules by defining the Entity Relationship Diagrams (ERD), ERD to relation schema transformation, and data normalization. Upon successful completion of this module, the student could be able to analyze data objects, draw ERD, transform ERD to relation schema, identify normal forms, and normalize the data models.
Module 4: Information Retrieval
Information retrieval (IR) deals with the representation, storage, organization of, and access to information items. It has changed considerably in the last decade with the expansion of the Web (World Wide Web) and the advent of modern relational databases. A well understanding of IR is thus essential to students in multiple disciplines including engineering, business, education, and sciences. The Information Retrieval module provides the background knowledge and hands-on techniques of IR. The students who successfully complete this module should be able to retrieve information from well-structured databases and the Web effectively.
Module 5: Data Analysis
Data analysis is the foundation for system analysis and for evaluating scenarios that involve uncertain outcomes. An understanding of data analysis techniques is essential for students in disciplines including engineering, business, and the sciences. The Data Analysis module provides the background for the subsequent module, Data Mining, by defining basic concepts of probability and statistics and presenting the statistical inference. Upon successful completion of the module, the student should be able to understand some popular probability distributions, graphic tools for data analysis, parameter estimation, and statistical inference.
Link to Full Industrial Engineering Proposal (DOC) |
