A THEORY OF EDUCATION: MEANINGFUL LEARNING UNDERLIES …

Aprendizagem Significativa em Revista/Meaningful Learning Review – V1(2), pp. 1-14 , 2011

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A THEORY OF EDUCATION: MEANINGFUL LEARNING UNDERLIES THE CONSTRUCTIVE INTEGRATION OF THINKING, FEELING, AND ACTING LEADING TO

EMPOWERMENT FOR COMMITMENT AND RESPONSIBILITY (Uma teoria de educação: aprendizagem significativa subjacente à integração construtiva de

pensamentos, sentimentos e ações levando ao empoderamento para compromisso e responsabilidade)

Joseph D. Novak

Professor Emeritus, Cornell University Senior Research Scientist

Florida Institute for Human and Machine Cognition www.ihmc.us/groups/jnovak/

Abstract

This paper traces the history of research and practice that led to the development of my Theory

of Education. Based on Ausubel’s assimilation theory of meaningful learning and constructivist epistemology, the theory includes five elements: teacher, learner, subject matter, context, and evaluation, each of which must be integrated constructively to effect high levels of meaningful learning. The development and use of concept mapping to facilitate meaningful learning is discussed and a New Model for Education is presented that builds upon the theory and associated practices. Keywords: meaningful learning; thinking; feeling; acting.

Resumo

Este artigo descreve a pesquisa e a prática que levaram ao desenvolvimento de minha Teoria de Educação. Baseada na teoria da aprendizagem significativa de Ausubel e na epistemologia construtivista, a teoria inclui cinco elementos: professor, aprendiz, matéria de ensino, contexto e avaliação, cada um dos quais deve ser integrado construtivamente para levar a altos níveis de aprendizagem significativa. O desenvolvimento e uso do mapeamento conceitual são discutidos e um Novo Modelo de Educação é apresentado a partir da teoria e práticas associadas. Palavras-chave: aprendizagem significativa; pensamentos; sentimentos; ações.

Introduction

From the time I was in elementary school in the 1930’s, I often wondered why some fellow

students had so much trouble understanding what I thought were relatively simple ideas. These same students might have been good at spelling and some other things, but they didn’t appear to understand why 2+2= 4 was the same as 2X2= 4 or why 8 pints or 4 quarts both equal one gallon. Surely some of the reason was due to heredity, but this did not appear to be an adequate explanation.

It seemed to me that some students were just learning differently, but I did not know what the differences were. I did not understand clear reasons for these differences until the 1960’s when I became familiar with David Ausubel’s Assimilation Theory of meaningful learning (Ausubel, 1963; 1968; 1978). Ausubel made the sharp distinction between learning by rote, where the learner makes little or no effort to integrate new concepts and propositions with relevant concepts and propositions already known, and meaningful learning where the learner seeks to integrate new knowledge with relevant existing knowledge. While teachers can arrange instruction and assessment to encourage either


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learning by rote of learning meaningfully, the primary responsibility for learning is the learner’s, and this responsibility cannot be shared.

Human beings think, feel, and act. Every learning event involves to a greater or lesser degree all three of these actions. In rote learning, there is often little emotional commitment other than to recall the information, and the extrinsic motivation that comes with getting the right answer. In meaningful learning the recognition of how the new information integrates with prior knowledge and “makes sense” provides much more rewarding intrinsic motivation. Moreover, when the learning is integral to some activity and helps to guide and clarify the activity, there is usually a higher level of positive affect resulting. Ausubel noted that meaningful learning has three requirements and the contrast with rote learning is shown in Figure 1.

In 1973, Joseph Schwab proposed that any educative event involves four commonplaces: the learner, teacher, subject matter, and context or social milieu. Schwab maintained that each of these was distinctly important and none could be reduce into any other. Partly for this reason, I have chosen to call these four distinct entities of education elements, analogous to elements in chemistry that are distinct structural units of matter. Moreover, I have chosen to add a fifth element in my 1977 Theory of Education, evaluation, since so much that affects learners, teachers, subject matter selected, and the social milieu of education depends on how we evaluate teaching and learning (Novak, 1998; 2010). Educating is further complicated by the fact that each of these elements is somewhat distinct for every student, and they may be substantially different for the teacher. Thus there is a need for negotiation of meanings between students and between students and teachers. Thus we see that education is in some ways relatively simple – involving just five elements – and concomitantly enormously difficult, since so many idiosyncratic teacher, subject matter, evaluation, and student elements must be orchestrated to operate supportively, perhaps in a variety of milieus. There are so man ways to go wrong. The natural world is also enormously complex, but we have made great progress in understanding and manipulating the natural events with the advance of theory-based science. For the past 50 years, I have argued that education will not be substantially improved until it is guided by better theory-based research and practices. This has been the path my students and I have pursued.

Figure 1. The rote-Meaningful learning continuum showing the requirements for meaningful learning. Creativity is seen as essentially very high levels of meaningful learning. This diagram should be part of metacognitive instruction.


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A Focus on Learning

Over the past 30 years there has been major advances in our understanding of human learning. Behavioral psychology that dominated education for more than half a century began its demise in the late 1970’s and pretty much collapsed in the 1980’s. Almost all competent educational psychologists have moved toward cognitive rather than behavioral models of human learning; Ausubel was simply much ahead of his time with this. Moreover, advances in neurobiology are also revolutionizing our thinking about human leaning capabilities and better understanding of how our brains work (see for example Medina’s Brain Rules). Inappropriate use of Piaget’s work led to untenable prescriptions for delayed introduction of instruction in abstract concepts and a mythology now thoroughly debunked by recent cognitive research with infants and young children (Cary, 1985; Gelman, 1999; Keil, 2011). Recent studies of human brain functions show the complexity of brain mechanism and the complex interrelationships in the functioning of various parts of the brain. Simplistic ideas of right-brain and left-brain thinking have been discarded as we learn more about the plasticity of the brain and immense interactions between various parts of the brain, including those regions that generate and store feelings of affective memories. Recent brain studies also lend support to the fundamental idea in Ausubel’s theory that knowledge stored during meaningful learning is fundamentally organized differently than knowledge learned by rote, and affective associations are also different. The simple distinctions shown in figure 1 underlie the profound differences in the outcomes from meaningful as contrasted to rote learning (Valadares & Moriera, 2009). It also explains why creativity is all to rare: too few people have been educated to reach high levels of meaningful learning in a given discipline, to say nothing about such achievement in related disciplines.

We are also learning more about the important role that social exchange plays in learning. Vygotsky’s (1926; 1962) ideas on the importance of language and dialogue between learners, largely ignored in western psychology and education until recently, are now seen as valuable for planning the context for educating. Vygotsky’s idea of the zone of proximal development (ZOD) that recognizes children’s learning is limited primarily by the ideas they have mastered at a given point in time, and development beyond this zone requires careful coaching and scaffolding of learning. There have also been important advances in research showing the “learning about learning” or metacognitive learning needs to be part of educational programs (Novak and Gowin, 1984. Novak, 1985; Bransford, et al, 1999)

In the epigraph to his 1968 book Ausubel stated:

If I had to reduce all of educational psychology to just one principle, I would say this: The most important single factor influencing learning is what the learner already knows. Ascertain this and teach him accordingly.

This idea is now widely accepted in education. Unfortunately, it is not easy to ascertain

precisely what a learner already knows, especially using conventional tests or even more probing interviews. This was a challenge our research group at Cornell University faced when we sought to follow children’s understanding of basic science concepts such as the nature of matter and energy. We found that the usual kinds of tests were simply not adequate for showing specific changes in 6 to 7 year-old’s conceptual understanding. My graduate students and I came up with the idea of translating interviews with children into what we call concept maps. Figure 2 shows an example of one of the concept maps made by one of my graduate students. It chows clearly and explicitly the concepts and propositions held by this student relevant to the topics studied. We have found subsequently that


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concept maps can be used to represent knowledge held by anyone from young children to research professors in any subject matter domain. Moreover, using concept maps help learners learn how to learn meaningfully, and help researchers design and interpret research more effectively (Novak & Gowin, 1984; Novak, 1998, 2010). Concept maps drawn by learners prior to instruction are very effective in revealing what learners already know, including their misconceptions, and then we can “teach them accordingly”. The development of excellent software for creating concept maps by the Florida Institute for Human and Machine Cognition in the 1990’s that permits addition of any kind of digital resource to any concept has added new dimensions to the value of this tool permitting the construction of what we call Knowledge Models. These are essentially digital portfolios constructed by individuals or collaborative groups. Further discussion of knowledge models is included below under A New Model for Education. The software is being used worldwide and is available at no cost at: http://cmap.ihmc.us

Figurer 2. A concept map drawn from an interview with a six-year old child who had audio-tutorial instruction in grades one and two, including lessons on the nature of matter. Students receiving audio-tutorial instruction in grades one and two had very significantly more valid and few invalid concepts regarding the nature of mater and energy than students who did not receive this instruction, and the difference increased from grades 2-12 (Novak and Musonda, 1991).

There is much more that can be said about ways to improve learning, but for this paper, suffice it to say that focusing on ways to encourage and enhance meaningful learning is the principal and most critical factor. What is knowledge?

My own thinking about nature of knowledge and knowledge creation (also referred to as epistemology) was initially stimulated by Conant’s (1947) book, On Understanding Science. A later book by one of Conant’s assistants was Kuhn’s, The Structure of Scientific Revolutions (1962). That book and Toulmin’s (1972), Human Understanding: The collective use and evolution of concepts, helped to shape my thinking. The latter book was especially helpful not only in explaining the origins of concepts but also how they are used and how they evolve over time both for an individual and within


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a discipline. Toulmin’s ideas nicely complemented Ausubel’s assimilation theory of learning and I later fused these ideas into an epistemology I called human constructivism (Novak, 1987, 1998). From these works and other readings and seminars with Gowin, a colleague at Cornell University, a definition of concepts, the primary building blocks of knowledge, emerged as: perceived regularities or patterns in events or objects, or records of events of objects designated by a label, usually a word. Most of the words in any language are concept labels, and sometime the foreign word has a slightly different meaning, since it evolved in a different milieu. Two or more concepts are combined with “linking words” to form propositions, and these are descriptive statements about some event of object. As we learn new concepts and propositions, we are really learning the meanings of the concepts and the relationships between them. Through the process of meaningful learning, concepts and propositions are organized into the cognitive structure of our brains. Since this learning process is to some extent idiosyncratic for every, individual, concepts and propositions have slightly different meanings for each person. This is one reason why good educational practices offer much opportunity for interpersonal interactions during learning.

As noted earlier, meaningful learning is also accompanied by some degree of affective experience and this affective connotation colors to some extent the meaning of or concepts. This is why I see knowledge creation as a human construction, and is not the same as what might be compiled by a computer. The ideas associated with Human Constructivism are on the surface simple enough, but understanding the interrelationships can be challenging. Figure 3. Shows the web of relationships that need to be considered.

Figure 3. A concept map showing the key foundations and relationship of ideas involved in Human Constructivism (Novak, 1993).


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Subject Matter

So much of school learning entails with memorization of a sea of information with little differentiation of the “big ideas” that can be so powerful in helping students build powerful knowledge structures. I recall my fascination with history after reading Muller’s (1958) The Loom of History where he presents a series of big ideas that help to make sense out of all the details commonly taught in history classes. Similarly, Bonner’s (1962) The Ideas of Biology showed haw several major ideas help to understand all of biology. These and similar works helped me to see that any discipline is not just an ocean of facts and figures but rather a framework of big ideas, what Ausubel called superordinate concepts, and when these were understood, all the subordinate concepts made much more sense. In other words, subject matter in all disciplines needs to be organized to first present the major ideas of a discipline and then these will help to learn and understand the usual kinds of detailed information thrown at students who may otherwise have little recourse other than to memorize the information. Of course, coming to understand a big idea such as evolution, the Renaissance, or derivative in mathematics also requires learning subordinate concepts and thus sequencing instruction around big ideas is not an easy task. This is where curriculum workers expert in the field could help, but they seldom do. We attempted to do this in 1963 for K-12 science, but the idea was far ahead of its time and never took hold (Novak, 1964; Hurd, 1964). Perhaps if we had had the concept map tool we could have better illustrated what we were proposing and made it easier for teachers and students to implement this kind of curriculum plan. Science curriculum planners would do well to revisit the plan we proposed in 1964. The curriculum proposals by AAAS (1993) and NRC (1996) failed to do this, as is also the case with the most recent NAS proposal (2010). Building concept maps as study in any field progresses can be enormously helpful to the learners and to the teachers. Thousands of examples of concept maps can be found on the web site for CmapTools (http://cmap.ihmc.us) and hundreds of research studies showing the value of concept maps can be found at: http://cmc.ihmc.us

In any discipline we mist consider more that just information in that discipline; we must also consider how new knowledge is created in the discipline, the social context in which the discipline operates and advances, and the kind of attitudes we hope to foster. We attempted to include some of this in our 1964 report and presented 5 “Processes of Science” along with a description of the 7 “Conceptual Schemes” of science. Instruction should include laboratory, library and field studies, with work guided by the conceptual schemes being illustrated. So much of the “science inquiry” activity currently so popular in the science education literature proceeds largely with students who lack the conceptual ideas needed to understand what they are doing so they can do it creatively. Without this conceptual clarity, science inquiry activities may be little better that rote learning the subject matter. More will be said about appropriate instruction as we illustrate our New Model for Education (Novak and Cañas, 2004; Novak, 2010). The Teacher

Typically the teacher’s role has been to stand before her class and dispense information to her students for them to memorize. Evaluation typically consists of multiple-choice tests that require little more than recall of information, and no evidence that the meaning of this information is understood. This model has survived for years, even though it should have collapsed after the invention of the printing press in 1440 and certainly after the development of cheap computers and the Internet in the 1990’s. For most school and university classes, the traditional model of teaching remains dominant today, albeit there are some noteworthy exceptions.


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It is important to recognize that ideally, teachers are also learners and they “negotiate

meanings” with their students. This is a complex interaction where all five elements of education should be involved. This is illustrated in Figure 4. Concept maps when used with CmapTools facilitate a much richer exchange between teachers and learners, as well as a wide range of other educational resources. With this model, the teacher is more of a learning coach than a diseminater of information. This will be discussed later as part of our New Model for Education

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Figure 4. Highly successful teaching requires that all five elements of education operate in learning negotiations between teachers and learners. A similar relationship should operate in business settings (Novak, 2010). The Context for Educating

In the past two decades, we have witnessed and explosive growth in globalization. China and India have grown exponentially in economic development and their economies will surpass the US in as little as two decades. These economic realities combined with enormous growth in the number and quality of their universities mean that American workers and corporations will face even more global competition than they do today. Add to this the rapid growth of the Brazilian and Australian economies, plus a number of other countries, and we shall see a much fiercer world economic competition. This changing global context will necessitate creating much more effective educational programs for any country that seeks to maintain or raise it’s living standards. For the last century, education has been the principal driver for upward mobility of individuals and countries and this is even more likely to be the case in the future. This reality is both a challenge and an opportunity. For the USA, we must seek to raise graduation rates for minorities and other groups who now suffer lost opportunities. The saying, “necessity is the mother of invention” may come to apply to education, and


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the kind of education that could be achieved by the kind of science of education I am advocating here may begin to flourish in the years ahead. Progress has been slow, as is often the case when new ideas are introduced, but it is also true that subsequent changes can occur at an accelerating rate. The “egg crate” school model with rooms for a teacher and her 20 to 40 students needs to be replaced by different kinds of schools. The growth of home schooling and on-line learning already point to some of the kind of changes that need to occur Education in the future will make far greater use of technology than we see typically in today’s schools.

I believe the theory of education I am sketching in this paper can help to propel the kind of changes that are needed in education. Schooling has to become more efficient. We currently spend about $10,000 per pupil per year on education in the USA, and most of the world cannot afford even a fraction of these costs. But leveraging new ideas and vastly improved use of technology, even relatively poor countries can catapult into good 21st Century education. We saw in Panama how teachers in grades 5-6 from 1000 schools could be trained to use CmapTools and the Internet in a two-week training program. Students in these schools were eager to embrace the new educational tools as shown in Figure 5. While we were a long way from implementing what we call a New Model for education, these schools were making a good beginning in moving in this direction.

Figure 5. A 6th grade Panamanian student showing his concept map to Alberto Cañas who led the team that created CmapTools.

There have been efforts in the past to change the context for school learning such as “open classroom” configuration in the 1960’s where large carpeted spaces were designed for 100 or so students to work in these common areas, but instructional materials and practices remained essentially traditional and soon these schools were demolished or walled up with book cases and cabinets or new walls. Another foolish effort was to place TV sets in every room and/or a large central facility with 50 or more TV carrel units all controlled from a central TV center. Few or no special materials were made available and by 1970, most of these efforts were abandoned.


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Changing the physical context without changing all the other elements required for good education leads to costly failures. All of these elements are interdependent.

Evaluation

The fifth element in my theory of education is evaluation, and in some ways this is the most crucial element. For example, in the USA the No Child Left Behind program enacted in 2001 requires all schools to show higher passing scores on tests of information studied in math and English. The effect has been that most teachers and schools focus almost exclusively on raising test scores primarily by drilling on information I, and when it comes to understanding and being enthusiastic about learning, most students have been left behind. Here we see the tail “wagging the dog”, where testing is driving how teaching and learning occur, but with negative consequences.

Unfortunately, it is much easier to test for rote learning using ”objective” tests than to test for understanding to evaluate the gains in student understanding of subject matter, and accompanying positive attitudinal changes in their appreciation of the discipline and understanding of the ways knowledge is created in the discipline.

These issues are discussed at length elsewhere (Novak, 2010).

When instruction focuses on meaningful learning, using the kinds of tools and ideas suggested above, we not only see high achievement on standardized achievement tests but other positive outcomes. For example, Principal Otto Silesky and his teachers elected to use concept maps in every course and to stress meaningful learning and collaborative instruction. Figure 6 shows that after a year of struggle to adapt to the new strategies in 2002, achievement scores on state exams advanced to 100% passing. Perhaps even better, acceptance into tertiary education programs rose from zero percent to 20% by 2009. Both teachers and students report much more positive attitudes toward learning and toward the new educational practices.

It is not easy to change the school milieu from teacher dominated information presentation and testing for rote recall to a school where collaborative learning aimed at learning for understanding is dominant. Moreover, this kind of quality education does not cost more, it simply requires the right kind of leadership to effect the changes needed.

Figure 6. Approval percentages for high school graduates from Otto Silesky’s school in Costa Rica before (hashed bars) and after the use of concept mapping and emphasis on meaningful learning.


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A New Model for Education

With further refinement of learning theory and instructional practices that facilitate meaningful learning, the further development of CmapTools to facilitate use of Internet and other resources, and the explosive development of the WWW, it is now possible to effect what we call a New Model for Education. This New Model has three principal components:

1. The use of expert “skeleton concept” maps to scaffold learning. “Expert skeleton” concept

maps are small (10-15) concepts arranged hierarchically by and expert in the knowledge domain for learners to use as a starting point to “scaffold” their learning. Possible additional concepts may be suggested in a “Parking Lot” to be integrated into the skeleton cmap.

2. Use of all features of CmapTools, including collaboration tools, to build over a span of days or weeks a personal “knowledge model” for the domain studied. This may be preserved on DVD’s or hard drives and used in future years to accelerate further learning in this domain.

3. Explicit instruction in metacognitive learning and the use of metacognitive tools.

The New Model makes use of the whole palette of instructional strategies including selected readings, small group research projects, report preparation and presentation, and others as shown in Figure 7. Of course, not all of the 15 activities would be used in every study unit, but all should be used over a school year. During the course of a week to three weeks of work, 2-4 student teams work collaboratively to build a knowledge model, prepare written and or oral reports, and reflect on the work of classmates. Reports or school fairs with parents and or community groups should also follow at least once per year to engage the whole community in the learning efforts.

Figure 7. A schematic showing the features of a New Model for Education


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While I know of no school that has fully implemented this New Model, there are all over the

world many efforts at innovation that are moving in this direction. As computer prices continue to fall and capabilities of computers and Internet access continue to increase, it will be increasingly practicable to fully implement the New Model. For example, in Panama, a relatively poor country, we have worked with grade 4 to 6 teachers and principals in 1000 schools to help them begin to use the New Model. Figure 8 shows a knowledge model dealing with the Kuna Indians of Panama developed by a team of students. Figure 9 shows the chicken cages, built with help from the community, where students raised chickens to learn about animal nutrition needs, chickens that they later ate. In Italy, Professor Valitutti and his colleagues are moving towed full implementation of the New Model. Such project activities necessarily combine learning in reading, writing, oral presentation, mathematics tools and their application and the best kind of interdisciplinary study – all committed to the best form of meaningful learning. All of these projects approach the best possible implementation of all five elements of education needed for excellent meaningful learning experiences. Such implementation is not only possible in affluent countries but in all countries with stable democracies. Our only obstacle to widespread implementation of the New Model is the eternal resistance to change so common in most societies.

Figure 8. A digital knowledge model produced by 6th grade students in Panama, Shown are some of the resources that can be accessed by clicking on icons on concepts when connected to the server where the files are stored.


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Figure 9. Chick enclosure in schoolyard where children of this rural school in Panama raise chicks. Parents helped to build the enclosures and eagerly followed the children’s work.

Figure 10. 2nd grade children in Urbino, Italy study types and functions of plant leaves. Later they made concept maps to organize their knowledge.


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References American Association for the Advancement of Science. (1993;). Benchmarks for science literacy. New York: Oxford University Press.

Ausubel, D. P. (1963). The Psychology of Meaningful Verbal Learning. New York: Grune and Stratton.

Ausubel, D. P. (1968). Educational Psychology: A Cognitive View. New York: Holt, Rinehart and Winston.

Ausubel, D. P., Novak, J. D., & Hanesian, H. (1978). Educational Psychology: A Cognitive View (2nd ed.). New York: Holt, Rinehart and Winston.

Bonner, J. T. (1962). The Ideas of Biology. New York: Harper.

Bransford, J., Brown, A. L., & Cocking, R. R. (Eds.). (1999). How People Learn: Brain, Mind, Experience, and School. Washington, D.C.:

Carey, S. (1985). Conceptual Change in Childhood. Cambridge. MA: MIT Press

Conant, J. B. (1947). On Understanding Science: An Historical Approach. New Haven, CT: Yale University Press.

Gelman, S.A., (1999). Dialog on early childhood science, mathematics and technology education: A context for learning. Concept Development in Pre-school Children. (http://www.project2061.org/publications/earlychild/online/context/gelman.htm)

Hurd, P. de (1964). Theory Into Practice. Washington, D.C.: National Science Teachers Assn.

Keil, F.C. (2011). Science Starts Early. Science, 331:1021-1022.

Kuhn, T. S. (1962). The Structure of Scientific Revolutions. Chicago: Univ. of Chicago Press.

Muller, H. J. (1958). The Loom of History. New York: Harper.

National Research Council (NRC). (1996) National Science Education Standards. Washington D.C.: National Academy Press.

Novak, J. D. (1964). Importance of conceptual schemes for science teaching. The Science Teacher 31(6):10.

Novak, J. D. (1977). A Theory of Education. Ithaca, NY: Cornell University Press.

Novak, J.D. (1987). Human constructivism: Toward a unity of psychological and epistemological meaning making. In Joseph D. Novak (ed.), Proceedings of the Second International Seminar on Misconceptions and Educational Strategies in University

Novak, J. D. (1990). Concept maps and Vee diagrams: Two metacognitive tools for science and mathematics education. Instructional Science, 19, 29-52.

Novak, J. D. (1993). Human constructivism: A nification of psychological and epistemological phenomena in meaning making. International Journal of Personal Construct Psychology, 6, 167-193.

Novak, J. D. (1998). Learning, Creating, and Using Knowledge: Concept Maps as Facilitative tools in Schools and Corporations. Mahwah, NJ: Lawrence Erlbaum & Associates. Spanish, 1998, Madrid: Alianza Editorial. Portuguese, 2000, Lisboa: Platano Edicoes Tecnicas. Italian, 2001, Trento: Edizioni Erickson. Finnish, 2003, Jyuvaskyla, Finland: PS-kustannus.

Novak, J.D. (2010). Learning, Creating, and Using Knowledge: Concept Maps as Facilitative tools in Schools and Corporations (2nd Ed.). New York: Routledge, Taylor-Francis.


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Novak, J. D., & Gowin, D. B. (1984). Learning How to Learn. New York: Cambridge University Press.Novak, J. D., & Musonda, D. (1991). A twelve-year longitudinal study of science concept learning. American Educational Research Journal, 28(1), 117-153.

Novak, J.D.(1985). Metalearning and metaknowledge strategies to help students learn how to learn. In Leo H. T. West & A. Leon Pines (eds.), Cognitive Structure and Conceptual Change (pp. 189-209). (In the Educational Psychology Series.) Orlando, FL: Academic Press.

Novak, J. D., & Cañas, A. J. (2004). Building on Constructivist Ideas and CmapTools to Create a New Model for Education. In A. J. Cañas, J. D. Novak & F. M. González (Eds.), Concept Maps: Theory, Methodology, Technology. Proceedings of the 1st International Conference on Concept Mapping. Pamplona, Spain: Universidad Pública de Navarra.

Schwab, J. J. (1973). The practical 3: Translation into curriculum. School Review, 81(4), 501-522. [See Chapter 1, p. 1.3]

Toulmin, S. (1972). Human Understanding. volume 1: The Collective Use and Evolution of Concepts. Princeton, NJ: Princeton University Press.

Valadares, J.A & Moreira, M.A. (2009). A teoria da aprendizagem significativa: sua fundamentação e implementação. Coimbra: Edições Almedina S.A.

Vygotsky, L. S. (1926; 1962). Thought and Language. Cambridge, MA: MIT Press (edited and translated by Eugenia Hanfmann and Gertrude Vakar).

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CHAPTER 2: LEARNING THEORIES

Overview of Learning Theories

Over the past century, educational psychologists and researchers have posited many theories to explain how individuals acquire, organize and deploy skills and knowledge. To help readers organize and apply this extensive body of literature, various authors have classified these theories in different ways. For this summary, learning theories are grouped into three basic categories: • Behaviorist learning theories • Cognitive-information processing learning

theories • Cognitive-constructivist learning theories

The summary ends with a brief discussion of epistemological perspectives that serve as foundations for the various theories. Only a brief overview of extensive literature is provided to help you make informed decisions about your personal educational philosophy. If you have good working knowledge of one or more areas underlined above, feel free to scan over those sections and concentrate your attention on the areas you feel less certain. For further detail, readers are also encouraged to search for the corresponding topics in literature. As you look over the information contained in this document, keep in mind the purpose of your reading. The immediate purpose is to generate an educational philosophy statement (that is, stating what you believe in terms of how and why people learn and what educators should do to facilitate such learning). Your goal is to define a set of quality design standards. As such, you should note concepts and statements that you believe are important for promoting learning and for designing and delivering effective instruction.

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Behaviorist Learning Theories

The origins of behaviorist learning theories may be traced backed to the late 1800's and early 1900's with the formulation of "associationistic" principles of learning. The general goal was to derive elementary laws of learning and behavior that may then be extended to explain more complex situations. Inferences were tied closely to observed behavior in "lower organisms" with the belief that the laws of learning were universal and that work with laboratory animals could be extrapolated to humans. It was believed that a fundamental set of principles derived from the

study of learning in a basic or "pure" form could then be applied to the broader context of learning in schools. Three experimental approaches are related to the study of associationistic learning including:

1. The use of nonsense syllables and individual words to study the association of ideas

2. The use of animals to study the association between sensations and impulses

3. The use of animals to study association and reflexology

The Association of Ideas Following a tradition begun by Ebbinghaus (1885), researchers studied learning in terms of memory for individual items, most commonly nonsense syllables and individual words. It was assumed that understanding simpler forms of learning would lead to understanding of more complex phenomena. During this time, the predominate research methods were those of serial list learning and paired associate learning. These methods allowed researchers to study, predict, calculate and calibrate "associations" or the degree/ likelihood that a nonsense syllable or word could elicit a particular response from learners. In short, the basic premise underlying associationistic views of learning was that ideas become connected, or associated, through experience. Furthermore, the more frequently a particular association is encountered, the stronger the associative bond is assumed to be. For example, the stimulus "bread" is likely to elicit the response "butter" more often and more rapidly than the response "milk," because the association between bread and butter has been frequently experienced and thus has become well learned.

The Association between Sensations and Impulses Like Ebbinghaus, Thorndike was also interested in studying learning in terms of associations, but in terms of actions, rather than ideas. For his research, Thorndike used animals (e.g., cats and chickens) which were placed in "puzzle boxes" and measured learning in terms of the amount of time it took for the animal to operate a latch and escape. The results led Thorndike to believe that animals learned to associate a sensation with an impulse when its action had a satisfying consequence. For instance, an animal may form an association between a sense (the interior of a box) and an impulse (operating a latch) because the action led to a satisfying result--namely, escaping the box. This principle, termed the Law of Effect, helped modify the classical principle of association and later held significant implications

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for behaviorism. One of the clearest formulation of associationistic learning principles were made by Hull (1934, 1952) and Spence (1936-1956). Like Thorndike, Hull and Spence based their propositions on data from numerous experiments with laboratory animals. However, unlike Thorndike, Hull and Spence derived equations to explain different actions such as habits, drive and inhibitions. Hull (1952) was able to demonstrate that the elementary laws of learning captured in these equations could account for a number of behaviors such as trial-and-error learning and simple discrimination learning in animals.

Associationism and Reflexology A third approach to the study of associations, led by Pavlov, brought together the principles of associationism and reflexology. Pavlov noticed that dogs salivated not only to food, but often to a variety of other stimuli, such as the sight of a trainer who brought the food. He called this response a learned reflex that is established through the association between an appropriate stimulus (food) and an inappropriate one (the trainer). In other words, a relatively neutral stimulus is associated with something that causes a response until the neutral stimulus also causes the response. This lead to an extended research program now known as classical conditioning. According to the principle of classical conditioning, an unconditioned stimulus (UCS) biologically and involuntarily elicits an unconditioned response (UCR). For example, the site of food (UCS) elicits salivation (UCR). Then, as a conditioned stimulus (trainer) becomes associated with the unconditioned stimulus (food), it (the trainer) acquires the ability to elicit the same response (salivation). Because the response is now conditioned to a new stimulus, it becomes a conditioned response (Figure 2.1).

Figure 2.1: Illustration of Classical Conditioning

A significant problem became apparent as associationistic research continued. As experi-mental psychologists made finer and finer distinctions to their research on "trial and error" learning in animals and their studies of rote memory, their results appeared to be less and less relevant for education. The search for general laws that crossed all species and settings was failing. As methods were refined and experiments became more valid internally, they were becoming less

valid externally. The "laws of learning" were becoming known as the "laws of animal learning," "the laws of animals learning to make choices in mazes," or the "laws of human rote memory" rather than the universal principles sought after by early associationists. However, not all associa-tionist psychologies resulted in theoretical or applied dead-ends. The so-called radical behaviorists, led by Skinner (1938, 1953), have had a strong impact on both psychology and education. Like early works by Watson (1924), Skinner rejected the idea that the purpose of psychology was to study consciousness, rather the goal was to predict and control observable behavior. Learners were seen as coming to learning situations tabula rasa, subject to conditioning by their environment. It was believed that by controlling the environ-mental antecedents and consequences for behavior, people could predict and control that behavior. In addition, by providing positive consequences for behavior and by controlling the schedule by which these consequences were delivered, behavior could be further controlled and shaped. In his research, Skinner demonstrated that laboratory animals were sensitive to manipulation of both antecedents and conse-quences of their actions and that simple responses, such as bar pressing and pecking, could be predicted with high confidence. Based on these observations, Skinner proposed a basic stimulus-response-stimulus (S-R-S) relationship as depicted below (Figure 2.2).

Figure 2.2: Basic S-R-S Relationship

In brief, the nature of the contingent stimulus is believed to determine what happens to the response, whether it is reinforced or lost. In other words, behavior is more likely to reoccur if it has been rewarded or reinforced. Similarly, a response is less likely to occur again if its consequences have been aversive. These principles are referred to as the contingencies of reinforcement which suggest that to understand learning, one must look for the change in behavior that occurred and determine what consequences were responsible for the change (Skinner, 1969). The basic S-R-S relationship provides the framework from which most behavioral learning principles and their applications for instruction and education are derived. Behavioral learning theories have contributed to instruction and education in several significant ways. The three applications summarized here include: 1. Behavior Modification 2. Classroom Management

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3. The Management of Instruction

Behavioral Modification Also known as behavior therapy or contingency management, behavior modification is typically used to treat behavior problems in social, personal, or school situations. Some clinical applications include treatments for phobias, obsessions or eating disorders. Educational applications involve the treatment of school-related problems such as the lack of attention, hyperactivity, temper tantrums, or other behaviors that interfere with the regular workings of a classroom. Special education teachers are typically well trained in behavioral modification. In each of these instances, the S-R-S model and its resulting principles are used to shape, modify and otherwise control behavior.

Classroom Management While behavioral therapists and special education teachers apply behavioral learning principles to address individuals, teachers in regular classrooms may use the same principles to help manage the behavior of twenty to thirty children. For instance, teachers may set up group contingencies (a standard reinforcement given to a group) for following certain rules of conduct. A kindergarten teacher, for example, may take his/her students out to the playground 10-15 minutes early if they all pick up their things. One common means of applying group contingencies that some teachers find useful is the token economy (Ayllon & Azrin, 1968). In this system, tokens serve as conditioned reinforcers that can later be exchanged for objects or privileges. Tokens are earned for good conduct--whatever behaviors have been selected by the teacher for strengthening. Since tokens operate like money, students may also be fined for breaking the rules or engaging in undesirable behavior.

Management of Instruction Behavioral principles have proved useful, not only for managing student behavior, but also for managing the way instruction is delivered. The most prominent examples of how behavioral learning theories have been applied to the management of instruction include the develop-ment of behavioral objectives, contingency contracts, and personalized systems of instruction (PSI). Behaviorists, as well as others, argue that the only evidence of learning comes from the study of overt behaviors. How can one be sure that a student acquired knowledge or a skill unless we can see them actually do something with that knowledge or skill? Thus, to assess the

degree to which a student achieved an objective, it is important to specify desired instructional outcomes in terms of clear, observable behaviors (behavioral, instructional, learning, or performa-nce objectives). An instructional application that often makes use of both behavioral modification and instructional objectives is the contingency contract. Used with individual students, the contract sets out the terminal behavior the student is to achieve, along with the conditions for achievement and the consequences for completion (or noncompletion) of assigned tasks. Keller (1968) proposed a whole new approach to college instruction based on behavioral principles known as the personalized system of instruction (PSI). PSI calls for course materials to be broken up into units, each with a set of behavioral objectives. Students tackle course materials on their own, often aided by study guides which provide practice on unit objectives. To proceed, students are required to demonstrate mastery by taking a unit quiz. Students receive feedback immediately and if they pass, they can go on to the next unit. If they fail, they must remediate and take the quiz again, but with no penalty.

_____________________________________Cognitive-Information Processing Theories No single point in time signaled the end to the associationistic or behavioral era, and the beginning of the cognitive revolution. Early on, the cognitive revolution was a quiet one. However, as psychologists became increasingly frustrated with the limitations of behavioral theory and methods, and persuasive arguments against radical behaviorist theories were being put forth by linguists studying language development, the "time was right" for the emergence of cognitivism. Another prominent factor was the development of computers (Baars, 1986), which provided both a credible metaphor for human information processing, and a significant tool for modeling and exploring human cognitive processes. One major group of cognitive theories may be classified as cognitive-information processing learning theories. According to the cognitive information processing (CIP) view, the human learner is conceived to be a processor of inform-ation, in much the same way a computer is. When learning occurs, information is input from the environment, processed and stored in memory, and output in the form of a learned capability. Proponents of the CIP model, like behaviorists, seek to explain how the environment modifies human behavior. However, unlike behaviorists, they assume an intervening variable between the environment and behavior. That variable is the information processing system of the learner.

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Most models of information processing can be traced to Atkinson and Shiffrin (1968) who proposed a multistage theory of memory in which information received by the processing system undergoes a series of transformations before it can be permanently stored in memory. This flow of information, as it is generally conceived, is depicted in Figure 2.3. Displayed in the figure are three basic components of memory (i.e., sensory memory, short-term memory, and long-term memory) along with the processes assumed to be responsible for transferring information from one stage to the next. This system provides the basic framework for all learning theories classified under the cognitive-information processing category.

Figure 2.3: Information Processing Model of Human Learning

The following is a brief summary of each major component of the information-processing system and their implications for instruction.

Sensory Memory Sensory memory represents the first stage of information processing. Associated with the senses (vision, hearing, etc.), it functions to hold information in memory very briefly, just long enough for the information to be further processed. It is believed that there is a separate sensory memory corresponding to each of the five senses, but all are assumed to operate in the same way.

Selection Attention Selective attention refers to the learners' ability to select and process certain information while simultaneously ignoring other information. The degree to which an individual can spread their attention across two or more tasks (or sources of information) or focus on selected information within a single task depends on four factors:

1. The meaning of the task or information to the individual

2. The similarity between competing tasks or sources of information

3. Task complexity or difficulty 4. The individuals ability to control attention

Pattern Recognition Just attending to information is not enough to ensure its further processing. Attention is believed to be necessary but not sufficient; information must be analyzed, and familiar patterns must be identified to provide a basis for further processing. Pattern recognition refers to the process whereby environmental stimuli are recognized as exemplars of concepts and principles already in sensory memory.

Short-Term Memory Short-Term Memory (STM) functions as a temporary working memory where further processing is carried out to make information ready for long term storage or a response. At this stage, concepts from long-term memory (LTM) are also activated for making sense of the incoming information. STM or working memory has been likened to consciousness. When we actively think about ideas and are therefore conscious of them, they are said to be in working memory. STM, however, only holds a certain amount of information for a limited amount of time.

Rehearsal & Chunking Rehearsal and chunking are two processes that may help individuals encode information into long-term memory. When you repeat a phone number to yourself over and over again, you are engaged in rehearsal. Chunking is the grouping of ideas, letters, phrases, etc. into bits of information to facilitate the encoding process. Take for example, the following span of letters: JFKFBIAIDSNASAMIT. As individual letters, they more than exceed the capacity of working memory. However, as five chunks--JFK, FBI, AIDS, NASA, and MIT--they are easily processed.

Encoding Encoding refers to the process of relating incoming information to concepts and ideas already in long-term memory in such a way that the new material is more memorable. Encoding serves to move information from STM to LTM. There are too many studies and methods for facilitating encoding to review here in any meaningful way. In short, it is believed that individuals impose their own subjective organization to materials in order to learn them. However, techniques such as outlining, hierarchies, concept trees, mnemonics, mediation and imagery have all been shown to aid the encoding process.

Long-Term Memory Long-term memory (LTM) represents our permanent storehouse of information. Anything that is to be remembered for a long time must be

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transferred from STM to LTM. Although forgetting is a phenomenon we have all experienced, it is assumed that once information has been processed into LTM, it is never truly lost. As far as we know, LTM is capable of retaining an unlimited amount and variety of information. It has limitations in our retrieval process, that are believed to constrain our ability to remember. There are a number of different views of how information is stored in LTM including, but not limited to, schemas and mental models.

Retrieval The process of retrieval from long-term memory is relatively simple to understand. Previously learned information is brought back to mind, either for the purposes of understanding some new input or for making a response. Using previous knowledge to understand and learn new material has already been discussed as encoding. Using previous knowledge to make a response is known as retrieval. There are a number of alternative cognitive theories, including, but are not limited to: Levels or Depth of Processing, Meaningful Learning, Schema Theory, and Mental Models. These all relate learning with information processing, which is why they are grouped here. However, they do not necessary adhere to the CIP model as the method used by individuals to process information, or they focus on only one or a few components of the CIP model.

_____________________________________Cognitive-Constructivist Learning Theories Since space limitations prevent an extensive discussion of constructivism, in addition to those cited in the following paragraphs, interested readers are referred to the works of von Glasersfeld (1989, 1981), Jonassen (1991), Marra and Jonassen (1993) and Rorty (1991). In brief, there is no single constructivist theory. Constructivist approaches to teaching and learning is grounded in several research traditions (Perkins, 1991; Paris & Byrnes, 1989). The roots of constructivism may be traced back to a little known Latin treatise, De antiquissima Italorum sapientia, written in 1710 by Giambattista Vico (as cited in von Glasersfeld, 1991). Vico suggested that knowledge is knowing what parts something is made of, as well as knowing how they are related. "Objective, ontological reality, therefore, may be known to God, who constructed it, but not to a human being who has access only to subjective experience" (p. 31, von Glasersfeld, 1991). A second, related path to constructivism comes from Gesalt theories of perception (Kohler, 1924)

that focus on the ideas of closure, organization and continuity (Bower & Hilgard, 1981). Like Vico, Gesalt psychologists suggest that people do not interpret pieces of information separately and that cognition imposes organization on the world. Theories of intellectual development provide a third research tradition contributing to the notion of cognitive construction (e.g. Piaget, 1952, 1969, 1971; Baldwin, 1902, 1906-1911; Bruner, 1974). Developmentalists believe that learning results from adaptations to the environment which are characterized by increasingly sophisticated methods of representing and organizing information. Developmental scientists also forward the notion that children progress through different levels or stages which allow children to construct novel representations and rules. A fourth line of research depicts learning as a socially mediated experience where individuals construct knowledge based on interactions with their social and cultural environment. Like Piaget and Bruner, Vygotsky (1962, 1978) believed that the formation of intellect could be understood by studying the developmental process. However, like Bruner, Vygotsky felt that intellectual development could only be fully understood within the sociocultural context in which the development was occurring. Current conceptualizations of constructivist learning focus on the 3rd (developmental) or 4th (social) line of research. The two lines of research do not represent opposing perspectives, but rather differences in focus. Where developmental-constructivist tend to focus on the individual and how he or she constructs meaning of the world around him or her, social-constructivists emphasize the group and how social interactions mediate the construction of knowledge. The following tables, created by Bonk and Cunningham (1998) contrasts key concepts associated with developmental-constructivist and social-constructivist views of learning. These sources provide the groundwork for constructivism applied to education. The common belief that knowledge is constructed within a social context is the foundation for this group of learning theories. No discussion of learning theories, however, is complete without examining their epistemological foundations.

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Mind: The mind is in the head; hence, the learning focus in on active cognitive reorganization. Raw Materials: Use raw or primary data sources, manipulatives, and interactive materials. Student Autonomy: Ask students for personal theories and understandings before any instruction. Allow student thinking to drive lessons and alter instruction based on responses. Place thinking and learning responsibility in students’ hands to foster ownership. Meaningfulness and Personal Motivation: Make learning a personally relevant and meaningful behaviour. Relate learning to practical ideas and personal experiences. Adapt content based on student responses to capitalize on personal interests and motivation. Conceptual Organization/Cognitive Framing: Organize information around concepts, problems, questions, themes, and interrelationships, while framing activities using thinking-related terminology (e.g., classify, summarize, predict). Prior Knowledge and Misconceptions: Adapt the cognitive demands of instructional tasks to students’ cognitive schemes, while building on prior knowledge. Design lessons to address students’ previous misconceptions, for instance, by posing contradictions to original hypotheses and then inviting responses. Questioning: Promote student inquiry and conjecture with open-ended questions. Also, encourage student question-asking behaviour and peer questioning (Individual Exploration). Generating Connections: Provide time for the selection of instructional materials and the discovery of information, ideas, and relationships. Also, includes encouraging students to generate knowledge connections, metaphors, personal insights, and build their own learning products. Self-Regulated Learning: Foster opportunity for reflection on skills used to manage and control one’s learning. Help students understand and become self-aware of all aspects of one’s learning, from planning to learning performance evaluation. Given the focus on individual mental activity, the importance of cooperative learning or peer interaction is in the modelling of and support for new individual metacognitive skill. Assessment: Focus of assessment is on individual cognitive development within predefined stages. Use of authentic portfolio and performance-based measures with higher order thinking skill evaluation criteria or scoring rubrics.

Table 1: Developmental Constructivist Practices and Principles

Mind: The mind is located in the social interaction setting and emerges from acculturation into an established community of practice. Authentic Problems: Learning environments should reflect real-world complexities. Allow students to explore specializations and solve real-world problems as they develop clearer interests and deeper knowledge and skills. Team Choice and Common Interest: Build not just on individual student prior knowledge, but on common interests and experiences. Make group learning activities relevant, meaningful, and both process and product oriented. Give students and student teams choice in learning activities. Foster student and group autonomy, initiative, leadership, and active learning. Social Dialogue and Elaboration: Use activities with multiple solutions, novelty, uncertainty, and personal interest to promote student-student and student-teacher dialogue, idea sharing and articulation of views. Seek student elaboration on and justification of their responses with discussion, interactive questioning, and group presentations. Group Processing and Reflection: Encourage team as well as individual reflection and group processing on experiences. Teacher Explanations, Support, and Demonstrations: Demonstrate problems steps and provide hints, prompts, and cues for successful problem completion. Provide explanations, elaborations, and clarifications where requested. Multiple Viewpoints: Foster explanations, examples, and multiple ways of understanding a problem or difficult material. Build in a broad community of audiences beyond the instructor. Collaboration and Negotiation: Foster student collaboration and negotiation of meaning, consensus building, joint proposals, prosocial behaviors, conflict resolution, and general social interaction. Learning Communities: Create a classroom ethos or atmosphere wherein there is joint responsibility for learning, students are experts and have learning ownership, meaning is negotiated, and participation structures are understood and ritualized. Technology and other resource explorations might be used to facilitate idea generation and knowledge building within this community of peers. Interdisciplinary problem-based learning and thematic instruction in incorporated wherever possible. Assessment: Focus of assessment is on team as well as individual participation in socially organized practices and interactions. Educational standards are socially negotiated. Embed assessment in authentic, real-world tasks and problems with challenges and options. Focus on collaboration, group processing, teamwork, and sharing of findings. Assessment is continual, less, formal, subjective, collaborative, and cumulative.

Table 2: Social Constructivistic Practices and Principles

____________________________________________

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Summary of Epistomological Beliefs

Over the past century, social psychologists have taken a number of alternative approaches to explain how the mind acquires knowledge. One extreme is characterized by an objectivist (positivist, logical empiricism) epistomology that suggests that reality is external to individuals and is based on natural laws, physical properties and their relationships. Objectivists believe that the mind processes symbols and mirrors reality, and that thought is governed by, and reflects external reality. Objectivists believe that meaning is external to and independent of the understanding of individuals. The polar opposite of objectivism is interpretivism (constructivist, subjectivist). Interpretists believe that knowledge is constructed. The mind interprets sensory data and organizes it through active and dynamic processes according to innate perceptual categories such as numerosity and animacy. Furthermore, interpretists emphasize concepts, such as perceptual relations (Gibson, 1966) and the structure of language (Chomsky, 1965) that are imposed upon the world by individuals. Interpretists believe that reality is internal to the organism and that meaning is dependent on individual understanding. An alternative to both objectivism and interpretivism is pragmatism (Driscoll, 1994). Pragmatists also believe that reality is "constructed." However, the meaning derived by individuals are believed to be negotiated within a social context. Unlike interpretists, pragmatists believe that individuals' reality is mediated by their prior knowledge structures and their interactions with the environment and with others. They believe that the mind builds symbols and interprets nature, and that thought is governed by individuals' perceptions that reflect their internal reality. Pragmatists believe that meaning is constructed by individuals based on their interpretation and understanding of reality. A further comparison of objectivism, pragmatism and interpretivism (Driscoll, 1994) is given here (Table 3). For more extensive discussions of epistemological beliefs, interested readers are referred to the works of von Glasersfeld (1989, 1981), House (1980, 1983a, 1983b), Jonassen (1994, 1991), Lebow (1993) and Rorty (1991) among others.

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What is the relation with Design Standards?

Distance education programs are grounded on research and theory in four basic foundations: 1. Psychological Foundation

2. Instructional Foundation 3. Technological Foundation 4. General Systems Foundation Based on findings and key concepts associated with each of the four foundations, various sets of design standards that can be used to help guide the development of on-line coursework and distance education programs, have been generated.

Psychological Foundations Psychological foundations reflect views on how individuals acquire, organize and deploy skills and knowledge. Some of the conclusions are:

1. DE students need to be active participants in the planning and evaluation of their learning

2. Experiences (including mistakes) form the bases for knowledge construction

3. DE students are most interested in learning subjects that have immediate relevance to their job or personal life

4. DE students’ learning is problem centered rather than content-oriented

5. DE students prefer to build on prior learning and experience

Moreover, it is generally believed, in contrast to constructivist or positivist epistemologies, that there is a true reality that is external to, but can not be known directly by humans. Knowledge, however, is thought to be provisional, not absolute. It is considered that reality is "constructed" and that meaning derived by individuals is negotiated within a social context. An individual's reality is mediated by his or her prior knowledge structures and interactions with the environment and with others. It is believed that the mind builds symbols and interprets nature, and that thought is governed by an individual's perception that reflects his or her internal reality. Therefore, DE students are presented with a range of learner-centered and teacher-directed methods based on the desired learning outcomes, learners' prior knowledge and experience, and each faculty member's strengths and beliefs. Learner-centered methods are best suited for facilitating learning of higher-order thinking skills and when students have prior knowledge of and/or experience with the subject matter. In comparison, teacher-directed methods are appropriate when teaching well defined sets of procedures and when dealing with novice learners who are totally unfamiliar with the content or instructional strategies. Furthermore, in several cases, a combination of these strategies may be

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applied successfully within a course, as well as across courses within a program of study. However, some argue against the use of an eclectic approach (e.g., designing instruction from multiple theoretical perspectives) because it is said to strip concepts, strategies and tools of meaning and utility. "Problems arise ... when tools developing in the service of one epistemology are integrated within instructional systems designed to promote learning goals inconsistent with it" (Bonk & Cunningham, 1998, p. 25). In contrary, others feel that an eclectic approach based on a pragmatist epistemology is appropriate for the design and delivery of on-line instruction for a number of reasons: • Different theories have their own strengths

and weaknesses, and continue to evolve. We should not totally discard one just because something new is trendy. For example, behaviorist theories of human learning are not necessary wrong, but rather fail to explain certain phenomenon. Thus, cognitive information processing and cognitive constructivist theories have developed to explain a greater degree of variance. Behaviorist theories, however, still clarify certain behaviors quite well. Thus, one could utilize the strengths of different approaches when appropriate.

• Instructional strategies should be based on desired learning outcome and learners' prior knowledge, experience and interests. For example, when teaching basic procedures (simple algorithms), direct instruction may be more efficient and appropriate than social-constructivist approaches to teaching and learning. In contrast, when the primary learning objective is to have students' critically analyze, interpret and apply an ill defined body of research, constructivist approaches may be more appropriate than teacher-directed methods.

• Students differ in terms of learning requirements and preferred learning methods. To reach and satisfy as many students as possible, different approaches should be utilized when appropriate.

• Like students, instructors have their own strengths, weaknesses and beliefs. Therefore, instructors should also be able to apply what is best suited to their teaching and management style.

• Things change. We must stay abreast of changes, remain flexible and adapt our approaches accordingly to accommodate these changes.

• There is no one set of principles or single theory that explains everything related to

human learning and behavior. In other words, there is no panacea. Therefore, we should not limit ourselves to one particular theory, model or approach. Rather, we should be working toward understanding the context and conditions for which different methods and means are best suited.

• The real proof, that either validates or refutes arguments for or against eclectic models of instruction, lie in student test scores and work samples, as well as attitude, motivation and satisfaction measures. Theoretical and conceptual arguments are interesting, particularly for academics and theoreticians, but for students, if multiple methods and means from differing theoretical perspectives results in positive attitudes, motivation, learning and performance it would be difficult to argue against an eclectic approach from a pragmatic standpoint.

Instructional Foundation Instruction emphasizes how information is conveyed to learners and focus on the activities, methods, and structures of the environment that are designed to facilitated learning. Principles associated with student-centered learning, situated cognition and performance assessments form the basic instructional foundations. Student-Centered Learning

Figure 2.4: Student centered learning environment.

Figure 2.5: Teacher centered learning environment.

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Figures 2.4 and 2.5 illustrate both student-centered and teacher-centered models of instruction. Under the traditional teacher-centered approach, teachers serve as the center for epistemological authority, directing the learning process and controlling students' access to information. This model evolved to increase the number of students receiving instruction from an instructor; a necessity during the agricultural and industrial eras. Under this paradigm, students are treated as "empty vessels" and learning is viewed as an additive process with new information that is geared to the "average" students is simply added on top of existing knowledge and everyone is forced to progress at the same pace. Family and community members may contribute to student learning, but rarely in any systematic fashion. Research, however, indicates that students are not empty vessels. They come to class with their own perceptual frameworks and learn in different ways. Learning is no longer viewed as a passive process where static bodies of facts and formulas are passed along to the uninitiated. Rather, learning is an active, dynamic process in which connections are constantly changing and the structure is continually reformatted. In short, students construct their own meaning by talking, listening, writing, reading, and reflecting on content, ideas, issues and concerns. In student-centered environments, learners are given direct access to the knowledge base and work individually and in small groups to solve authentic problems. In such environments, parents and community members also have direct access to teachers and the knowledge base, playing an integral role in schooling process. Situated Cognition

Situated cognition suggests that learning is determined by both contextual and human factors. For knowledge to be useful, it is believed that learning must be situated in authentic tasks to enable transfer to similar situations. In short, instruction should be embedded in real-life contexts, and address issues that are familiar to students, and are relevant to their needs and interests. DE providers "situate" learning by asking students to apply learned skills and knowledge, develop work samples and complete course assignments that are based on problems confronted at their work setting. Performance Assessment Concepts associated with performance assessment represent the final basic instructional foundation. Performance assessments differ from conventional paper and pencil tests in two key respects. First, unlike conventional measures that tend to evaluate students' possession of knowledge, performance assessments judge students' ability to apply knowledge. Second, performance assessments are used as an integral part of learning. Rather than sorting students, such assessments tell students and their instructors how well they are developing their skills and knowledge and what they need to do to develop them further. This provides students with profiles of their emerging skills to help them become increasingly independent learners. The majority of DE courses are project-based, asking students to demonstrate their achievement of course standards by applying learned skills and knowledge. Efforts are also concentrated in place to design, implement and evaluate a program wide portfolio assessment system.

Objectivism Pragmatism Interpretivism Assumptions about reality

Reality is objective, singular, fragmentable

Reality is interpreted, negotiated, consensual

Reality is constructed, multiple, holistic

Nature of truth statements

Generalizations, laws, focus on similarities

Working hypotheses, focus on similarities and differences

Working hypotheses, focus on differences

Sources of knowledge Experience Experience and reason Reason

Types of research designs Experimental, a priori

Any design may be useful for illuminating different aspects of reality

Naturalistic, emergent

Associated learning and instructional theories

Behaviorism, Cognitive information processing, Gagne instructional theory

Educational semiotics, Bruner's and Vygotsky's views of learning and development

Piaget's developmental theory, radical constructivism

Table 3: Comparisons of Objectivism, Pragmatism and Interpretivism

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