Editorial

Decision Sciences

Donald G. Perrin

By exploring decision sciences and teaching a course in management science in a local MBA program, I have come to realize that we make scores, even hundreds of decisions, every day of our lives. It is a skill we take for granted, and a task filled with hazards because of erroneous, distorted, or incomplete information.

Edward Deming identified the importance of accurate data – lots of data – to facilitate making good decisions. He was a force for change in the Japanese automobile industry after his transformative ideas were rejected by U.S. automobile manufacturers. He became part of a quality movement that has continued under many names to this day.

Decision science identifies variables we can control – decision variables. Logical decisions based on decision variables are deterministic – thei fixed answers not usually controversial. There are also variables we cannot control, called environmental variables. To the extent we can identify and measure uncontrollable variables, they can be factored into the decision process. Most variables have a range, like daily temperature or hours of sunlight. Decision sciences allow for variability and come up with a range of probable answers. This also is what education is about.

In primary and secondary education in the United States, designated as grades K through 12,. learners are of different age, sex, height, weight, intelligence, experience, aptitudes, learning style, maturation, personality, curiosity, motivation, focus, and attention span. Some data is specific for each individual learner, such as age and sex; some data are changing, like height and weight; some data is in a measurable range, like intelligence and attention span; and some are difficult to measure or subject to random influences. Our industrial model of education attempts to put similar students into groups, the way we grade oranges in a packing shed, so that all oranges in the box are uniform in size and color.

So called homogenous grouping was used in schools until the 1960s. It includes students of similar age, intelligence, aptitudes, and academic experience. These controllable variables – our decision variables. Uncontrolled variables include life experience, learning styles, maturation, personality, curiosity, motivation, focus, attention span, and aspects of physical coordination and social behavior. Variability in controlled variables is easily managed, but variability and random behavior in the uncontrolled variables challenges the industrial concepts of batch processing and “one-size-fits-all” instruction. We grade students by what they have learned and reject students (human capital) that do not succeed under current educational theories and practices.

In the latter part of the 20^th century, integration replaced homogenous grouping and the classroom became a microcosm of U.S. society. This added variables of race, culture, language, social class, and intelligence. In this same period, students with disabilities were “mainstreamed” into regular classrooms. Variability among students was greater than ever before, but teachers – and teacher training institutions – continued to use the same teaching techniques and batch training methods designed to produce a uniform product based on old educational theories and practices.

Technology, immigrant labor, and outsourced manufacturing have changed requirements for high school and college graduates. The traditional compliant and literate graduates are ill-matched to creative, entrepreneurial needs of high tech, high finance, information age society, and flattened global economies. In this new and complex world, graduates must be creative problem solvers able to find and assemble data to make measured decisions. Creativity and decision science tools are just one aspect of the knowledge, skills, and aptitudes required for the graduate of the future. Are we ready to change our schools and teacher training programs for the twenty-first century?