Adonis Diaries

Posts Tagged ‘taxonomy of methods

New semester, new approach to teaching this complex course of Human Factors in engineering?

Posted on October 26, 2008 (and written in April 6, 2006. Article #42 )

Usually, over 60 students enroll in my class, and the administration refused to split my course into 2 classes to maximize “profit”.

In retaliation, this semester only ten students were allowed to enrolled for my class; one is a computer engineer finishing his degree and the remaining are industrial engineers.  As a reminder, this course is required for Industrial engineers IE

The other engineering disciplines managed to open up new elective courses for each one of them, and were trying to market them at the expense of the wishes of many students who wanted to take my course and their petitions were denied.

With this reduced class number, I had to capitalize on the advantages of smaller classes, once the shock is under control.  

This semester, methods applied in human factors engineering are the focus: Having the previous semester in the body of varied knowledge in the course materials might encourage my class to appreciate the efforts and time invested by the pool of human factors researchers and professionals to make available practical design guidelines for the other engineering professions.

Whereas in the previous semesters I shun away from exposing my class to new methods, except teaching them explicitly the concept of controlled experimentations, like the differences among dependent, independent and controlled variables.

I endeavored to correct their misunderstanding, thinking that there was an abundance of knowledge to assimilate for a meager semester in the previous semesters, I boldly changed direction in my teaching approach by investing more time on exposing and explaining the various methods that human factors might be applying in their profession.  

The first assignment was using excel to compare 40 methods used in human factors, industrial engineering, industrial psychology, and designers of intelligent machines.  

This assignment was a version of article #14, about the taxonomy of methods, from 20 articles that I wrote the previous years and offered them as an introduction to the course, in addition to the course materials.

The students were supposed to select five categories from more than the dozen ways to classifying methods such as definition, purpose, applications, inputs, processes, procedures, output/product, mathematical requirements, disciplines teaching them, advantages, disadvantages, sources/links, connections with other methods, and comments.

I expected that, as engineers, they would logically select for the columns applications, input, procedure, output, and comments because they are what define a method. But somehow, they opted for applications, procedures, advantages, disadvantages, and comments mainly because it is how the internet offer information.  

After 3 students submitted their assignment on time I handed them over 40 summary sheets for the 16 methods used to analyzing a system or a mission, at least 2 sheets for each of 16 methods, one sheet on the purpose, input, procedure, and output/product of the method and the other sheets as examples of what the output is expected to look for presentation.

I then asked the less performing students to concentrate on only the 16 methods for their assignment and most of them did not submit this assignment even two months later.

So far I used up six sessions for methods or related topics such as the methods applied in the process of analyzing systems’ performance, psychophysical procedures, the fundamentals of controlled experimentation methods, human factors performance criteria, and what human factors “measure/data” in their experiments. 

As for the body of knowledge I extract a few facts from experiments and asked them to participate in providing me with the rationales or processes that might explain these facts.

For example, if data show that females on average are two third the strength of males then what could be the underlying causes for that discovery?  

Could that fact be explained by the length of the muscles, the cross section thickness of the muscles, the number of muscle fibers, or the length of the corresponding bones?

Facts are entertaining but I figured that they are big boys to be constantly entertained while shovelful of money is being spent for their university education.

Facts are entertaining but there have to come a time when these big boys stop and wonder at the brain power, Herculean patience, and hard work behind these amusing sessions.

The next assignment was to observe the business of the main family’s bread earner, note down the minute tasks of his typical day work, learn about the business by attempting to generate detailed answers from a questionnaire they have to develop based on a set of investigative query and problems related to human factors performance criteria in the assignment sheet…

And to report back what are the routine and daily tasks that enabled the students to join a university.  

Three students worked with their fathers’ in summer times and enjoyed the assignment; the remaining students could not shake off their 8th grade habits, wrote the questionnaire, mailed it, and waited for the answers.  

I was expecting that the students would apply the methodology they learned in analyzing systems such as activity, decision, and task analyses… but the good stuff was not forthcoming.

To encourage them to cater to the business that they might inherit, I assigned them a lecture project that would generate the requisite analyses with a clear objective of focusing on near-accidents, foreseeable errors, safety of the workers and health conditions in the workplace.

So far, the products of the two quizzes were complete failures. Funny, although most of the questions in the second quiz were from the same chapter sources as the first quiz, it is amazing how ill prepared are the students for assimilating or focusing on the essential ideas, concepts, and methods.

With a third of the semester over, I can points to only two students who are delivering serious investment in time, hard work, and excitement and are shooting for a deserved grade of A.

An exercise: taxonomy of methods

Posted on: June 10, 2009

Article #14 in Human Factors

I am going to let you have a hand at classifying methods by providing a list of various methods that could be used in Industrial engineering, Human Factors, Ergonomics, and Industrial Psychology.

This first list of methods is organized in the sequence used to analyzing part of a system or a mission;

The second list is not necessarily randomized, though thrown in without much order; otherwise it will not be an excellent exercise.

First, let us agree that a method is a procedure or a set of step by step process that our forerunners of geniuses and scholars have tested, found it good, agreed on it on consensus basis and offered it for you to use for the benefit of progress and science.

Many of you will still try hard to find short cuts to anything, including methods, for the petty argument that the best criterion to discriminating among clever people is who waste time on methods and who are nerds.

Actually, the main reason I don’t try to teach many new methods in this course (Human Factors in Engineering) is that students might smack run into a real occupational stress, which they are Not immune of, especially that methods in human factors are complex and time consuming.

Here is this famous list of a few methods and you are to decide which ones are still in the conceptual phases and which have been “operationalized“.

The first list contains the following methods:

Operational analysis, activity analysis, critical incidents, function flow, decision/action, action/information analyses, functional allocation, task, fault tree, failure modes and effects analyses, timeline, link analyses, simulation, controlled experimentation,  operational sequence analysis, and workload assessment.

The second list is constituted of methods that human factors are trained to utilize if need be such as:

Verbal protocol, neural network, utility theory, preference judgments, psycho-physical methods, operational research, prototyping, information theory, cost/benefit methods, various statistical modeling packages, and expert systems.

Just wait, let me resume.

There are those that are intrinsic to artificial intelligence methodology such as:

Fuzzy logic, robotics, discrimination nets, pattern matching, knowledge representation, frames, schemata, semantic network, relational databases, searching methods, zero-sum games theory, logical reasoning methods, probabilistic reasoning, learning methods, natural language understanding, image formation and acquisition, connectedness, cellular logic, problem solving techniques, means-end analysis, geometric reasoning system, algebraic reasoning system.

If your education is multidisciplinary you may catalog the above methods according to specialty disciplines such as:

Artificial intelligence, robotics, econometrics, marketing, human factors, industrial engineering, other engineering majors, psychology or mathematics.

The most logical grouping is along the purpose, input, process/procedure, and output/product of the method. Otherwise, it would be impossible to define and understand any method.

Methods could be used to analyze systems, provide heuristic data about human performance, make predictions, generate subjective data, discover the cause and effects of the main factors, or evaluate the human-machine performance of products or systems.

The inputs could be qualitative or quantitative such as declarative data, categorical, or numerical and generated from structured observations, records, interviews, questionnaires, computer generated or outputs from prior methods.

The outputs could be point data, behavioral trends, graphical in nature, context specific, generic, or reduction in alternatives.

The process could be a creative graphical or pictorial model, logical hierarchy or in network alternative, operational, empirical, informal, or systematic.

You may also group these methods according to their mathematical branches such as algebraic, probabilistic, or geometric.

You may collect them as to their deterministic, statistical sampling methods and probabilistic characters.

You may differentiate the methods as belonging to categorical, ordinal, discrete or continuous measurements.

You may wish to investigate the methods as parametric, non parametric, distribution free population or normally distributed.

You may separate them on their representation forms such as verbal, graphical, pictorial, or in table.

You may discriminate them on heuristic, observational, or experimental scientific values.

You may bundle these methods on qualitative or quantitative values.

You may as well separate them on their historical values or modern techniques based on newer technologies.

You may select them as to their state of the art methods such as ancient methods that new information and new paradigms have refuted their validity or recently developed.

You may define the methods as those digitally or analytically amenable for solving problems.

You may choose to draw several lists of those methods that are economically sounds, esoteric, or just plainly fuzzy sounding.

You may opt to differentiate these methods on requiring high level of mathematical reasoning that are out of your capability and those that can be comprehended through persistent efforts.

You could as well sort them according to which ones fit nicely into the courses that you have already taken, but failed to recollect that they were indeed methods worth acquiring for your career.

You may use any of these taxonomies to answer an optional exam question with no guarantees that you might get a substantial grade.

It would be interesting to collect statistics on how often these methods are being used, by whom, for what rational and by which line of business and by which universities.

It would be interesting to translate these methods into Arabic, Chinese, Japanese, Hindu, or Russian.

Types of thinking or methods for resolving problems? What methods your field of practice use?

Ten years ago, I was teaching an introductory class of Human Factors in Engineering. I had 60 students in class and the chairman refused to open a second class, and many of the students were in their third years of various engineering disciplines.

I asked the class: “What methods your field of specialty applies to resolving problems?

That was a pretty interesting question: The heavy silence and opened jaws convinced me that I threw a bomb in class.

I had to list over three dozen methods and asked them to “internet search” how these methods are used and how they are applied. Two students were very diligent and the remaining students copy/pasted a few methods: Too many methods and kind of verging on a philosophy course.

See this taxonomy of methods https://adonis49.wordpress.com/2009/06/10/an-exercise-taxonomy-of-methods/

Daniel Montano in his blog Framework21 posted

1. Look at problems in many different ways, and find new perspectives

Leonardo da Vinci believed that, to gain knowledge about the form of a problem, you begin by learning how to restructure it in many different ways. He felt that the first way he looked at a problem was too biased. Often, the problem itself is reconstructed and becomes a new one.

2. Visualize!

When Einstein thought through a problem, he always found it necessary to formulate his subject in as many different ways as possible, including using diagrams. He visualized solutions, and believed that words and numbers as such did not play a significant role in his thinking process.

3. Produce! A distinguishing characteristic of genius is productivity.

Thomas Edison held 1,093 patents. He guaranteed productivity by giving himself and his assistants idea quotas. In a study of 2,036 scientists throughout history, Dean Keith Simonton of the University of California at Davis found that the most respected scientists produced not only great works, but also many “bad” ones. They weren’t afraid to fail, or to produce mediocre in order to arrive at excellence.

(And Edison stole many ideas and patented them as his and didn’t pay his genius assistants)

4. Make novel combinations. Combine, and recombine, ideas, images, and thoughts into different combinations no matter how unusual.

The laws of heredity on which the modern science of genetics is based came from the Austrian monk Grego Mendel, who combined mathematics and biology to create a new science.

5. Form relationships; make connections between dissimilar subjects.

Da Vinci forced a relationship between the sound of a bell and a stone hitting water. This enabled him to make the connection that sound travels in waves. Samuel Morse invented relay stations for telegraphic signals when observing relay stations for horses.

6. Think in opposites.

Physicist Niels Bohr believed that if you held opposites together, you suspend your thought, and your mind moves to a new level. His ability to imagine light as both a particle and a wave led to his conception of the principle of complementarity. Suspending thought (logic) may allow your mind to create a new form.

7. Think metaphorically.

Aristotle considered metaphor a sign of genius, and believed that the individual who had the capacity to perceive resemblances between two separate areas of existence and link them together was a person of special gifts.

8. Prepare yourself for chance.

Whenever we attempt to do something and fail, we end up doing something else. That is the first principle of creative accident.

Failure can be productive only if we do not focus on it as an unproductive result. Instead: analyze the process, its components, and how you can change them, to arrive at other results. Do not ask the question “Why have I failed?”, but rather “What have I done?”

————————————————————————————

Addendum: The items below are characteristics I have gathered from Einstein biographical resources.

————————————————————————————

9. Study philosophy.

  • Einstein studied philosophy and it influenced the way he thought.

10. Remain skeptical of your professors and other experts

  • Einstein sometimes showed a high degree of skepticism towards processed knowledge

11. Slow down your thinking process.

  • Einstein said that he was not smarter but that he stayed with problems longer. He has been described by himself and others as a slow thinker.

12. Imagine yourself as being part the problem you want to solve.

  • Einstein sometimes imagined himself being part of the dynamics he was trying to understand. He came to some great insights about time by imagining that he was riding a beam of light through space.

13. Not all innovative ideas are necessarily 100% “good”.

Any idea or innovation that can be used for the benefit of people may also be used against them.

  • Einstein’s breakthroughs in energy could be used to power cities and benefit society. But as we learned, the same ideas could also be used to kill thousands of people. It’s important to understand multiple possible applications of your ideas before you make them public. Einstein understood this. But it’s unclear if he understood this when he first proposed his theories or later, when others began talking about how to create a bomb.
  • (Einstein understood the impact of atomic bomb before he proposed his atomic bomb to Roosevelt at the beginning of the war: He suspected that Nazi Germany was working diligently on this mass destructive weapon).

14. Discuss ideas with other bright people to gain a more robust insight.

  • Einstein would discuss his ideas with colleagues and friends who were also experts in the subjects he cared about. His first wife studied along with Einstein early on and she was well versed in the same subjects. She is very likely to have contributed insights that moved his ideas forward.

15. Immerse yourself in the newest ideas from others.

  • Einstein worked as a patent clerk. He was one of the fist people to read many of the newest ideas submitted for patent protection by the brightest minds of his time.

Note 1: https://adonis49.wordpress.com/2009/05/24/%E2%80%9Cwhat-kind-of-methods-will-i-have-to-manipulate-in-human-factors%E2%80%9D/

Note 2: From Wikipedia:

Abductive reasoning, Abstract thinking, Analogy, Attitude, Calculation, Categorization,Cognition, Cognitive restructuring, Cognitive space, Cognitive style, Common sense,

Concept, Conjecture, Concrete concepts, Critical thinking, Deductive reasoning,

Definition, Estimation, Evaluation, Explanation, Gestalt psychology, Heuristics,

Historical thinking, Hypothesis, Idea, Identification (information), Inductive reasoning,

Inference, Instinct, Intelligence, Intelligence amplification, Intentionality,

Introspection, Knowledge management, Language, Lateral thinking,

Linguistics, Logic, Logical argument, Logical assertion, Meaning (linguistics),

Meaning (non-linguistic), Meaning (semiotics), Mental calculation, Mental function,

Metacognition, Mind’s eye, Mindset, Multiple intelligences, Multitasking,

Pattern matching, Personality, Picture thinking, Prediction, Premise,

Problem finding, Problem shaping, Problem solving, Proposition,

Rationality, Reason, Reasoning, Reasoning event, Self-reflection, Sapience,

Semantic network, Semantics, Semiosis, Semiotics, Six Thinking Hats,

Speech act, Stream of consciousness, Syllogism, Synectics, Systems intelligence,

Systems thinking, Thinking, Thought act, Thinking maps, Thinking process,

Thought experiment, TRIZ, Visual thinking, Working memory, Writing

 “An exercise for taxonomy of methods”

Article #14 in Human Factors

I am going to let you have a hand at classifying methods by providing a list of various methods that could be used in Industrial engineering, Human Factors, Ergonomics, and Industrial Psychology.

This first list of methods is organized in the sequence used to analyzing part of a system or a mission;

The second list is not necessarily randomized though thrown in without much order; otherwise it will not be an excellent exercise.

First, let us agree that a method is a procedure or a set of step by step process that our for runners of geniuses and scholars have tested, found it good, agreed on it on consensus basis and offered it for you to use for the benefit of progress and science.

Many of you will still try hard to find short cuts to anything, including methods, for the petty argument that the best criterion to discriminating among clever people is who waste time on methods and who are nerds.

Actually, the main reason I don’t try to teach many new methods in this course is that students might smack run into a real occupational stress which they are not immune of, especially that methods in human factors are complex and time consuming.

Here is this famous list of a few methods and you are to decide which ones are still in the conceptual phases and which have been “operationalized“.

The first list contains the following methods: operational analysis, activity analysis, critical incidents, function flow, decision/action, action/information analyses, functional allocation, task, fault tree, failure modes and effects analyses, time line, link analyses, simulation, controlled experimentation  operational sequence analysis, and workload assessment.

The second list is constituted of methods that human factors are trained to utilize if need be such as: verbal protocol, neural network, utility theory, preference judgments, psycho-physical methods, operational research, prototyping, information theory, cost/benefit methods, various statistical modeling packages, and expert systems.

Just wait, let me resume.

There are those that are intrinsic to artificial intelligence methodology such as: fuzzy logic, robotics, discrimination nets, pattern matching, knowledge representation, frames, schemata, semantic network, relational databases, searching methods, zero-sum games theory, logical reasoning methods, probabilistic reasoning, learning methods, natural language understanding, image formation and acquisition, connectedness, cellular logic, problem solving techniques, means-end analysis, geometric reasoning system, algebraic reasoning system.

If your education is multidisciplinary you may catalog the above methods according to specialty disciplines such as: artificial intelligence, robotics, econometrics, marketing, human factors, industrial engineering, other engineering majors, psychology or mathematics.

The most logical grouping is along the purpose, input, process/procedure, and output/product of the method, otherwise it would be impossible to define and understand any method.

Methods could be used to analyze systems, provide heuristic data about human performance, make predictions, generate subjective data, discover the cause and effects of the main factors, or evaluate the human-machine performance of products or systems.

The inputs could be qualitative or quantitative such as declarative data, categorical, or numerical generated from structured observations, records, interviews, questionnaires, computer generated or outputs from prior methods.

The outputs could be point data, behavioral trends, graphical in nature, context specific, generic, or reduction in alternatives.

The process could be a creative graphical or pictorial model, logical hierarchy or in network alternative, operational, empirical, informal, or systematic.

You may also group these methods according to their mathematical branches such as algebraic, probabilistic, or geometric.

You may collect them as to their deterministic, statistical sampling methods and probabilistic characters.

You may differentiate the methods as belonging to categorical, ordinal, discrete or continuous measurements.

You may wish to investigate the methods as parametric, non parametric, distribution free population or normally distributed.

You may separate them on their representation forms such as verbal, graphical, pictorial, or in table.

You may discriminate them on heuristic, observational, or experimental scientific values.

You may bundle these methods on qualitative or quantitative values.

You may as well separate them on their historical values or modern techniques based on newer technologies.

You may select them as to their state of the art methods such as ancient methods that new information and new paradigms have refuted their validity or recently developed.

You may define the methods as those digitally or analytically amenable for solving problems.

You may choose to draw several lists of those methods that are economically sounds, esoteric, or just plainly fuzzy sounding.

You may opt to differentiate these methods on requiring high level of mathematical reasoning that are out of your capability and those that can be comprehended through persistent efforts.

You could as well sort them according to which ones fit nicely into the courses that you have already taken, but failed to recollect that they were indeed methods worth acquiring for your career.

You may use any of these taxonomies to answer an optional exam question with no guarantees that you might get a substantial grade.

It would be interesting to collect statistics on how often these methods are being used, by whom, for what rational and by which line of business and by which universities.

It would be interesting to translate these methods into Arabic, Chinese, Japanese, Hindu, or Russian.

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What are error taxonomies, and other taxonomies in Human Factors in Engineering?

Article #12, written in April 9, 2005)

May you allow me just a side explanation on experimentation, to set the foundations first?

Psychologists, sociologists and marketing graduates are trained to apply various experimentation methods and not just cause and effect designs.

There are many statistical packages oriented to providing dimensions and models to the set of data dumped into the experiment, so that a preliminary understanding of the system behavior is comprehended qualitatively.

Every applied science has gone through many qualitative models or schema, using various qualitative methods, before attempting to quantify their models.

Many chairmen of engineering departments, especially those who have no understanding of the disciple of Human Factors in engineering and would never touch this body of knowledge and methods with a long pole, ask me to concentrate my courses on the quantitative aspects.

That hint sends immediate shiver through my rebellious spirit and I am tempted to ask them “what taxonomy of methods are you using in teaching engineering courses?”

What taxonomies Human Factors have to conceive?  How about the classification of human errors when operating a system, their frequencies and consequences on the safety of operators and system performance?

Human Factors professionals attempted to establish various error taxonomies, some within a specific context, during their study and analysis of errors that might be committed in the operation of nuclear power plants for example, and other taxonomy that are out of any specific context.

One alternative classification of human errors is based on human behavior and the level of comprehension. Mainly, skill-based, or rule-based or knowledge-based behavioral patterns.

This taxonomy identifies 13 types of errors and discriminates among the stages and strength of controlled routines in the mind that precipitate the occurrence of an error, whether during execution of a task, omitting steps, changing the order of steps, sequence of steps, timing errors, inadequate analysis or decision-making.

With a strong knowledge of the behavior of a system, provided that the mental model is not deficient, applying the rules consistently most of the errors will be concentrated on the level of skill achieved in performing a job.

Another taxonomy rely on the theory of information processing and it is a literal transcription of the experimental processes; mainly, observation of a system status, choice of hypothesis, testing of hypothesis, choice of goal, choice of procedure and execution of procedure.  Basically, this taxonomy may answer the problems in the rule-based and knowledge–based behavior.

It is useful to specify in the final steps of taxonomy whether an error is of omission or of commission.  I suggest that the errors of commission be also fine tuned to differentiate among errors of sequence, the kind of sequence, and timing of the execution.

There are alternative strategies for reducing human errors by either training, selection of the appropriate applicants, or redesigning a system to fit the capabilities of end users and/or taking care of his limitations by preventive designs, exclusion designs, and fail-safe designs.


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