Saturday, October 27, 2007

Engineering Thinking

Check out the CDIO Syllabus, created by an international consortium of engineering schools to promote the reform of engineering education. Notice the thinking skills that it calls to be formed in student
2.1.1 Problem Identification and Formulation

Data and symptoms

Assumptions and sources of bias

Issue prioritization in context of overall goals

A plan of attack (incorporating model, analytical and numerical solutions, qualitative analysis, experimentation and consideration of uncertainty)
2.1.2 Modeling

Assumptions to simplify complex systems and environment

Conceptual and qualitative models

Quantitative models and simulations
2.1.3 Estimation and Qualitative Analysis

Orders of magnitude, bounds and trends

Tests for consistency and errors (limits, units, etc.)

The generalization of analytical solutions
2.1.4 Analysis With Uncertainty

Incomplete and ambiguous information

Probabilistic and statistical models of events and sequences

Engineering cost-benefit and risk analysis

Decision analysis

Margins and reserves
2.1.5 Solution and Recommendation

Problem solutions

Essential results of solutions and test data

Discrepancies in results

Summary recommendations

Possible improvements in the problem solving process
2.2.1 Hypothesis Formulation

Critical questions to be examined

Hypotheses to be tested

Controls and control groups
2.2.2 Survey of Print and Electronic Literature

The literature research strategy

Information search and identification using library tools (on-line catalogs, databases, search engines)

Sorting and classifying the primary information

The quality and reliability of information

The essentials and innovations contained in the information

Research questions that are unanswered

Citations to references
2.2.3 Experimental Inquiry

The experimental concept and strategy

The precautions when humans are used in experiments

Experiment construction

Test protocols and experimental procedures

Experimental measurements

Experimental data

Experimental data vs. available models
2.2.4 Hypothesis Test, and Defense

The statistical validity of data

The limitations of data employed

Conclusions, supported by data, needs and values

Possible improvements in knowledge discovery process
2.3.1 Thinking Holistically

A system, its behavior, and its elements

Trans-disciplinary approaches that ensure the system is understood from all relevant perspectives

The societal, enterprise and technical context of the system

The interactions external to the system, and the behavioral impact of the system
2.3.2 Emergence and Interactions in Systems

The abstractions necessary to define and model system

The behavioral and functional properties (intended and unintended) which emerge from the system

The important interfaces among elements

Evolutionary adaptation over time
2.3.3 Prioritization and Focus

All factors relevant to the system in the whole

The driving factors from among the whole

Energy and resource allocations to resolve the driving issues
2.3.4 Trade-offs, Judgement and Balance in Resolution

Tensions and factors to resolve through trade-offs

Solutions that balance various factors, resolve tensions and optimize the system as a whole

Flexible vs. optimal solutions over the system lifetime

Possible improvements in the system thinking used

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