Skip to main content

The Theory of Totally Integrated Education (TIE)

Learning, Design, and Technology

Abstract

The theory of totally integrated education (TIE) predicts that mental structures formed by learners are expected to be stronger when “knowing that one,” “knowing how,” and “knowing that” are integrated with learner emotions and intentions. Such whole, completely connected mental structures are expected to be less vulnerable to forgetting. TIE theory builds on seminal work of John Dewey, Charles Sanders Peirce, Maria Montessori, Elizabeth Steiner, George Maccia, Stanley Greenspan, Kenneth Thompson, Myrna Estep, Eric Kandel, David Merrill, and Jeroen van Merriënboer. Two unique extant cases of education systems are described which illustrate parts of TIE theory. A further strategy for improving curriculum is recommended, which is based on sequencing authentic, whole learning tasks from simple to complex. Most importantly, these learning tasks are expected to help students integrate nine kinds of cognition with emotions and intentions: recognitive, acquaintive, appreciative, protocolic, adaptive, creative, instantial, relational, and criterial. A variety of teaching methods can be used to implement such an improved curriculum. TIE theory does not prescribe specific instructional methods or practices; rather it provides a set of principles which can be used to evaluate curriculum itself. To the extent these principles are present in curriculum, TIE theory predicts that students are more likely to achieve curriculum goals.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Institutional subscriptions

References

  • Brandes, U., & Erlebach, T. (Eds.). (2005). Network analysis: Methodological foundations. Heidelberg, Germany: Springer.

    Google Scholar 

  • Dewey, J. (1916). Democracy and education. New York, NY: The Free Press.

    Google Scholar 

  • Eagleman, D. (2015). The brain. New York, NY: Pantheon Books.

    Google Scholar 

  • Educology. (2017). Knowledge of education. Retrieved January 27, 2017 from: http://educology.indiana.edu

  • Estep, M. (2006). Self-organizing natural intelligence: Issues of knowing, meaning and complexity. Dordrecht, The Netherlands: Springer.

    Google Scholar 

  • Feldman, J. (Ed.). (2016). SUNY Cobleskill magazine. Albany, NY: Fort Orange Press.

    Google Scholar 

  • Frick, T. W. (1991). Restructuring education through technology. Bloomington, IN: Phi Delta Kappa Education Foundation.

    Google Scholar 

  • Frick, T. W. (1997). Artificially intelligent tutoring systems: What computers can and can’t know. Journal of Educational Computing Research, 16(2), 107–124.

    Article  Google Scholar 

  • Greenspan, S. I., & Benderly, B. L. (1997). The growth of the mind and the endangered origins of intelligence. Reading, MA: Addison-Wesley.

    Google Scholar 

  • Greenspan, S. I., & Shanker, S. G. (2004). The first idea: How symbols, language, and intelligence evolved from our primate ancestors to modern humans. Cambridge, MA: Da Capo Press (Kindle edition).

    Google Scholar 

  • Kandel, E. R. (1989). Genes, nerve cells, and the remembrance of things past. Journal of Neuropsychiatry, 1(2), 103–125.

    Article  Google Scholar 

  • Kandel, E. R. (2001). The molecular biology of memory storage: A dialogue between genes and synapses. Science, 294, 1030–1038.

    Article  Google Scholar 

  • Keller, J. M. (1983). Motivational design of instruction. In C. M. Reigeluth (Ed.), Instructional-design theories and models: An overview of their current status (pp. 383–434). Mahwah, NJ: Lawrence Erlbaum Associates.

    Google Scholar 

  • Koh, J., & Frick, T. (2010). Implementing autonomy support: Insights from a Montessori classroom. International Journal of Education, 2(2:E3), 1–15.

    Google Scholar 

  • Lillard, A. S. (2008). Montessori: The science behind the genius. New York: Oxford University Press.

    Google Scholar 

  • Lillard, P. P. (1996). Montessori today: A comprehensive approach to education from birth to adulthood. New York, NY: Schocken Books.

    Google Scholar 

  • Maccia, E. S. & Maccia, G. S. (1966). Development of educational theory derived from three educational theory models. Washington, DC: Final Report, Project No. 5–0638, U.S. Department of Health, Education, and Welfare.

    Google Scholar 

  • Maccia, G. S. (1986). Right opinion and Peirce’s theory of signs. Paper presented at the Semiotic Studies Faculty Seminar. Retrieved January, 27, 2017 from: http://educology.indiana.edu/Maccia/RightOpinionAndPeircesTheoryOfSigns_GSMaccia1987.pdf

  • Maccia, G. S. (1987). Genetic epistemology of intelligent natural systems. Systems Research, 4(3), 213–218. Retrieved January, 27, 2017 from: http://educology.indiana.edu/Maccia/Correspondence_GeneticEpistemologyOfIntelligentNaturalSystems1987.pdf

  • Maccia, G. S. (1988). Genetic epistemology of intelligent natural systems: Propositional, procedural and performative intelligence. Paper presented at Hangzhou University, China. Retrieved January, 27, 2017 from: http://educology.indiana.edu/Maccia/GeneticEpistemologyOfIntelligentSystems_propositionalProceduralPerformativeIntelligence1988.pdf

  • Merrill, M. D., Barclay, M., & van Schaak, A. (2008). Prescriptive principles for instructional design. In J. M. Spector, M. D. Merrill, J. van Merriënboer, & M. F. Driscoll (Eds.), Handbook of research on educational communications and technology (3rd ed., pp. 173–184). New York: Lawrence Erlbaum Associates.

    Google Scholar 

  • Mendenhall, A., Buhanan, C. W., Suhaka, M., Mills, G., Gibson, G. V., & Merrill, M. D. (2006). A task-centered approach to entrepreneurship. Tech Trends, 50(4), 84–89.

    Article  Google Scholar 

  • Peirce, C. S. (1932). Collected papers, Vol. II, Elements of logic (C. Hartshorne & P. Weiss, Eds.). Cambridge, MA: Harvard University Press.

    Google Scholar 

  • Peirce, C. S. (1934). Collected papers, Vol. V, Pragmatism and pragmaticism (C. Hartshorne & P. Weiss, Eds.). Cambridge, MA: Harvard University Press.

    Google Scholar 

  • Squire, L. R., & Kandel, E. R. (1999). Memory: From mind to molecules. New York, NY: Henry Holt and Co.

    Google Scholar 

  • Steiner, E. (1988). Methodology of theory building. Sydney: Educology Research Associates.

    Google Scholar 

  • Thompson, K. R. (2006a). “General system” defined for predictive technologies of A-GSBT (Axiomatic General Systems Behavioral Theory). Scientific Inquiry Journal, 7(1), 1–11.

    Google Scholar 

  • Thompson, K. R. (2006b). Axiomatic theories of intentional systems: Methodology of theory construction. Scientific Inquiry Journal, 7(1), 13–24.

    Google Scholar 

  • Thompson, K. R. (2008a). ATIS glossary. Retrieved January, 27, 2017 from http://www.indiana.edu/~aptac/glossary/

  • Thompson, K. R. (2008b). ATIS graph theory. Columbus, OH: Systems Predictive Technologies. Retrieved January, 27, 2017 from: https://www.indiana.edu/~aptfrick/overview/reports/11ATISgraphtheory.pdf

  • van Merriënboer, J. J., Kirschner, P. A., & Kester, L. (2003). Taking the load off a learner’s mind: Instructional design for complex learning. Educational Psychologist, 38(1), 5–13.

    Article  Google Scholar 

  • van Merriënboer, J. J., & Kirschner, P. A. (2013). Ten steps to complex learning: A systematic approach to four-component instructional design. New York, NY: Routledge.

    Google Scholar 

  • Yazzie-Mintz, E. (2007). Voices of students on engagement: A report on the 2006 high school survey of student engagement. Retrieved January, 27, 2017 from http://www.eric.ed.gov/PDFS/ED495758.pdf

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Theodore W. Frick .

Editor information

Editors and Affiliations

Appendix A: Defined and Undefined Terms in TIE Theory

Appendix A: Defined and Undefined Terms in TIE Theory

Definitions of Basic Terms in TIE Theory

In order to explicate theory, it is necessary to define terms. Steiner (1988) states it this way:

… when one sets forth the terms of the theory and their definitions, descriptive metaphysics is presented…. Descriptive metaphysics is a division of the phenomena which are the object of theorizing – the system – so that a set of descriptors characterizing the system’s properties emerges. To do this, the metaphysician must provide a set of class terms for characterizing each and every component of the system…. Therefore, classification is basic to descriptive metaphysics.

However, classification always involves definition. A class term denotes all the particulars to which the term is applicable (the extension of the term) and connotes the characteristics that a particular must have in order for the term to be applicable to it (the intension of the term). (Steiner, 1988, p. 64)

Steiner provides criteria for evaluating descriptive theory: exactness, exclusivity, exhaustiveness, external coherence, extendibility, equivalence, chaining and substitution (pp. 64–74). Descriptive theory is necessary for building a foundation before explanatory theory can be explicated.

Fundamental to TIE theory are the following defined terms (“=Df ” is read as “is defined as”) (These and other terms are defined at http://educology.indiana.edu. This website provides definitions of these terms and more. It is easier to follow the chains of definitions on the website by clicking on the hyperlinks.):

  • Mental structures = Df affect-relations which constitute intelligence (Certain terms are defined elsewhere by Thompson (2008a). See http://www.indiana.edu/~aptac/glossary/. These terms, defined in Axiomatic Theories of Intentional Systems (ATIS) can also be viewed at http://educology.indiana.edu. (Those defined terms include: affect-relations, complexity, system environment, intentional system, and complete connectedness). Mental structures can be formed for right and wrong opinions, for effective, ineffective, ethical and unethical conduct, and for true or false beliefs. )

  • Learning = Df increasing of complexity of a person’s mental structure (for Types 1–12)

  • Learner = Df person whose volition is learning

  • Forgetting = Df decreasing of complexity of a person’s mental structure

I have been discussing ‘mental structure’ above, and now I must be more precise. I take some definitions here from general system theory, and in particular, Axiomatic Theories of Intentional Systems (Thompson, 2006a, b; 2008a, b). “Affect-relations” are the connections among components of a system, and “complexity” is the number of connections. Thus, learning is defined as increasing the number of connections in a one’s mental structure. This is consistent with what Kandel (1989) has concluded on a biological level, claiming that long-term memory is “associated with growth in synaptic connections [among neurons]” (p. 115) and that “learning produces enduring changes in structure and function of synapses” (p. 121).

The biological explanation of changes in the human nervous system is not part of TIE theory. TIE theory asserts that humans form mental structures as they learn. To use Steiner’s criterion, there is external coherence. This definition of learning in TIE theory has external coherence with biological knowledge.

Undefined Terms

Some terms in a theory must remain undefined (Steiner, 1988). Definitions could go on ad infinitum if there are no primitive terms. This is to avoid circularity in definitions, as well as infinite regress. Undefined terms in TIE theory follow: intelligence, think, feel, intend, believe, perceive, guide, person, good, object (thing), course of action (conduct), end (goal).

More Definitions of Terms in TIE Theory

The domain of human learning is shown as a Venn diagram in Fig. 1, which illustrates defined terms that include “intended learning,” “guided learning,” “education,” “effective education,” and “worthwhile education.” Figures 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, and 14 illustrate via shadings in the Venn diagram how these terms are related but yet distinct:

  • Accidental learning = Df learning which is neither guided nor intended (see Fig. 2)

  • Discovery learning = Df learning which is intended but unguided (see Fig. 9)

  • Compelled learning = Df learning which is not intended but guided (see Fig. 11)

  • Conducive learning = Df education = Df learning which is both intended and guided (see Fig. 5)

  • Student = Df a person who intends to learn content with a teacher

  • Teacher = Df a person who intends to guide another person’s learning

  • Teaching = Df a teacher guiding another person’s learning (see Fig. 3)

  • Sign = Df representamen = Df “something which stands to somebody for something in some respect or capacity…. every representamen being thus connected with three things, the ground, the object, and the interpretant” (see Peirce, 1932, 2.228)

    • Interpretant = Df a sign derived by a person as a mental construct that is a representamen of the equivalent external sign, which relates to an object

  • Content = Df objects and signs of objects selected for student learning

  • Context = Df system environment of teacher and student that contains content

  • Education system = Df intentional system consisting of at least one teacher and one student in a context

  • Knowing = Df mental structures which consist of warranted beliefs, right opinions, and capabilities for performance (C. S. Peirce (1877) discussed four methods of fixating belief: tenacity, authority, agreeableness to reason, and science. Scientific method (or more generally disciplined inquiry) means that any rational agent can repeat the same method and should come to the same conclusion. (see Figs. 15, 16 and 17). Other mental structures can result from learning, such as beliefs that are unwarranted by the method of science, such as authority or agreeableness to reason. Learning can also create mental structures for wrong opinion and for ineffective and unethical conduct.)

    • Knowing that one: mental structures for right opinion

      • Recognitive: select the unique Q (Q is the unique object of knowing.) from not-Q and not-Q from Q.

      • Acquaintive: identify relations determinate of the unique Q.

      • Appreciative: identify relations appropriate of the unique Q.

    • Knowing how: mental structures for effective performance

      • Protocolic: take one path to goal.

      • Adaptive: take alternative paths to goal, choosing or combining paths based on specific conditions.

      • Creative: innovate or invent a new way to reach an existing or new goal.

    • Knowing that: mental structures for beliefs warranted by disciplined inquiry

      • Instantial: classification of objects of the same kind.

      • Relational: rational explanation of relationships between kinds of objects.

      • Criterial: rational judgment of kinds of objects and their relations according to a norm.

  • Knowledge = Df record of knowing = Df preservation of signs that represent what is known in some medium external to knower.

  • Disciplined inquiry = Df rigorous research = Df learning which is regulated by criteria for creating scientific, praxiological, and philosophical knowledge. (Of course, persons who are called teachers can work together with students in disciplined inquiry. In this case they are both intending to learn something that is unknown to either. In this sense, the teacher is not acting as a guide because he or she does not know their destination. Rather they are exploring together – attempting and intending to learn something new. The process of disciplined inquiry is regulated by criteria. This is different from when a teacher is leading a student to a known outcome, such as repeating an experiment that has already been done – e.g., by dropping a feather and a golf ball in a vacuum, to “discover” that their acceleration is the same. The student might learn something new in this case, but not the teacher. Isaac Newton did not have a teacher to lead him to discover the laws of gravity. Rather, he did this through disciplined inquiry.) (See Fig. 10.)

  • Instrumentally good = Df means that are good for an end (goal).

    • Means = Df course of action, a way to reach an end (goal).

  • Intrinsically good = Df means or ends that are good in themselves, not with respect to their instrumental goodness.

  • Effective Education = Df education that is instrumentally good (Steiner, 1988, pp. 16–17) (See Fig. 6.).

  • Effective Bad Education = Df education that is instrumentally good but not intrinsically good (See Fig. 13.).

  • Worthwhile Education = Df education that is both instrumentally and intrinsically good (Steiner, 1988, p. 17) (See Fig. 8.).

  • Totally Integrated Education = Df education that results in student completely connected knowing, intention and feeling (See Fig. 18.).

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer International Publishing AG

About this entry

Cite this entry

Frick, T.W. (2018). The Theory of Totally Integrated Education (TIE). In: Spector, M., Lockee, B., Childress, M. (eds) Learning, Design, and Technology. Springer, Cham. https://doi.org/10.1007/978-3-319-17727-4_69-2

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-17727-4_69-2

  • Received:

  • Accepted:

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-17727-4

  • Online ISBN: 978-3-319-17727-4

  • eBook Packages: Springer Reference EducationReference Module Humanities and Social SciencesReference Module Education

Publish with us

Policies and ethics

Chapter history

  1. Latest

    The Theory of Totally Integrated Education (TIE)
    Published:
    28 June 2023

    DOI: https://doi.org/10.1007/978-3-319-17727-4_69-3

  2. The Theory of Totally Integrated Education (TIE)
    Published:
    20 October 2017

    DOI: https://doi.org/10.1007/978-3-319-17727-4_69-2

  3. Original

    The Theory of Totally Integrated Education (TIE)
    Published:
    21 June 2017

    DOI: https://doi.org/10.1007/978-3-319-17727-4_69-1