Naturalistic Misunderstanding of the Heisenberg Uncertainty Principle

McKerrow, Kelly; Mckerrow, Joan E.

doi:10.3102/0013189x020001017

Cited by 11 publications

(8 citation statements)

References 7 publications

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“…We cannot say with any certainty when a woman with stage IV breast cancer will succumb to her illness, but we can say with near certainty that her chance and duration of survival will be less than those of a woman with stage I cancer. Yes, it is true that quantum mechanical leaves are both on the tree and on the ground until observed, but this, like Heisenberg Uncertainty, applies only in the submicroscopic world of the quantum 7 . It does not mean we do not or cannot understand biosynthesis, nuclear decay or breast cancer.…”

Section: Understanding the Physicsmentioning

confidence: 99%

“…Conversely, although the theories of chaos and complexity are complex, non‐linear, interactive and indeterminate; the experimental methods are reductionist. As McKerrow and McKerrow 7 state with respect to the invocation of the Heisenberg Uncertainty Principle to support naturalistic research methods:…”

Section: Larger Lessons From Natural Sciencementioning

confidence: 99%

“…

‘The principle was discovered in the context of the rationalist paradigm. This fact would appear to support the rationalist paradigm, not call into question its adequacy… It is incorrect to use the principle, which was discovered in the context of the rationalist paradigm, to negate the same paradigm and then use it to support arguments in favour of the naturalist paradigm.’ 7 …”

Section: Larger Lessons From Natural Sciencementioning

confidence: 99%

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Chaos, complexity and complicatedness: lessons from rocket science

Norman

2011

Medical Education

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Medical Education 2011: 45: 549–559 Context Recently several authors have drawn parallels between educational research and some theories of natural science, in particular complexity theory and chaos theory. The central claim is that both the natural science theories are useful metaphors for education research in that they deal with phenomena that involve many variables interacting in complex, non‐linear and unstable ways, and leading to effects that are neither reproducible nor comprehensible. Methods This paper presents a counter‐argument. I begin by carefully examining the concepts of uncertainty, complexity and chaos, as described in physical science. I distinguish carefully between systems that are, respectively, complex, chaotic and complicated. I demonstrate that complex and chaotic systems have highly specific characteristics that are unlikely to be present in education systems. I then suggest that, in fact, there is ample evidence that human learning can be understood adequately with conventional linear models. Conclusions The implications of these opposing world views are substantial. If education science has the properties of complex or chaotic systems, we should abandon any attempt at control or understanding. However, as I point out, to do so would ignore a number of recent developments in our understanding of learning that hold promise to yield substantial improvements in effectiveness and efficiency of learning.

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Section: Understanding the Physicsmentioning

confidence: 99%

Section: Larger Lessons From Natural Sciencementioning

confidence: 99%

“…

Section: Larger Lessons From Natural Sciencementioning

confidence: 99%

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Chaos, complexity and complicatedness: lessons from rocket science

Norman

2011

Medical Education

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“…Nonetheless, I now find much to criticize in my original argument concerning quantum physics. While we may have to contend with an indeterminate (and to a certain degree unpredictable) subatomic world, that is not the world that is relevant to education (except, of course, in physics courses; see also McKerrow & McKerrow, 1991). My original argument-that if the behavior of the simplest entities of the universe are indeterminate and unpredictable, then we should not expect the behavior of human beings to be more determinate and predictable-simply does not stand.…”

Section: Quantum Physicsmentioning

confidence: 99%

Purposeful Behavior as the Control of Perception

Cziko

1992

Educational Researcher

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This article continues the discussion begun in my 1989 EducationalResearcher essay on the problems posed by the unpredictability and indeterminism of human behavior for educational research and responds to criticisms of these arguments offered by . In the course of this discussion, a theory of purposeful behavior known as perceptual control theory is presented which provides an explicit, working model of how in dividuals are able to produce repeatable outcomes via variable means. It is argued that the traditional "scientific" method of educational research, which attempts to find relationships between "independ ent" and "dependent" variables over groups of individuals, is in principle incapable of providing valid explanations of the how and ,ļ why of purposeful human behavior.

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“…This principle states, essentially, c.-lat observers, by their very presence, have an effect on the observed, thus rendering the observations invalid. It appears, though, that this principle has been erroneously borrowed from the physical sciences (McKerrow & McKerrow, 1991). It was developed from quantum mechanics, where statistical probability had to be employed when it was discovered that either the position or the momentum of a particle could be accurately measured, but not both simultaneously (Hawking, 1988 Traditional research, by following its'own rules, maintains the status quo (Argyris, 1980), whereas action research, for example, defines the boundaries of "new paradigm research" (Allender, 1986):solving problems through qualitative research in the relevant context.…”

Section: The Oualitative World Viewmentioning

confidence: 99%

1. Methodology of Statistical Analysis

Gottinger¹

1980

Elements of Statistical Analysis

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This paper addresses the conditions under which quantitative and qualitative research methods could be combined in special education. The paper asserts that qualitative designs have not had a significant effect on special education research and speculates that mixed-method research might be more acceptable to special education researchers or editors than studies 4sing only qualitative methods. The methodological characteristics of each approach to research are described. Strategies for mixing quantitative and qualitative research methods are discussed. The work of Robert Yin, who has described ways in which quantitative data can be embedded in case studies, is examined. The use of mixed-methods in educational evaluation is addressed, and the special case of single-subject research is analyzed. Several examples of the integration of qualitative and quantitative methods are then described. The paper concludes with a discussion of the need for and viability of mixed-method research in early intervention. This conclusion argues that mixed-method research is needed for both philosophical and pragmatic reasons, but that it is important not to let mixed-method designs excuse researchers from following the principles or rules of each method. Mixing methods generates the possibility of creating a higher standard of research which would take into account the complexity of the organism, the environment, and the intervention, as well as different perspectives on the purpose of research. (Contains approximately 65 references.) (JDD)

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Naturalistic Misunderstanding of the Heisenberg Uncertainty Principle

Cited by 11 publications

References 7 publications

Chaos, complexity and complicatedness: lessons from rocket science

Chaos, complexity and complicatedness: lessons from rocket science

Purposeful Behavior as the Control of Perception

1. Methodology of Statistical Analysis

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