Behaviorism, cognitivism, and constructivism are the three broad learning theories most often utilized in the creation of instructional environments. These theories, however, were developed in a time when learning was not impacted through technology. Over the last twenty years, technology has reorganized how we live, how we communicate, and how we learn. Learning needs and theories that describe learning principles and processes, should be reflective of underlying social environments. Vaill emphasizes that “learning must be a way of being – an ongoing set of attitudes and actions by individuals and groups that they employ to try to keep abreast of the surprising, novel, messy, obtrusive, recurring events…” (1996, p.42).

Learners as little as forty years ago would complete the required schooling and enter a career that would often last a lifetime. Information development was slow. The life of knowledge was measured in decades. Today, these foundational principles have been altered. Knowledge is growing exponentially. In many fields the life of knowledge is now measured in months and years. Gonzalez (2004) describes the challenges of rapidly diminishing knowledge life:

“One of the most persuasive factors is the shrinking half-life of knowledge. The “half-life of knowledge” is the time span from when knowledge is gained to when it becomes obsolete. Half of what is known today was not known 10 years ago. The amount of knowledge in the world has doubled in the past 10 years and is doubling every 18 months according to the American Society of Training and Documentation (ASTD). To combat the shrinking half-life of knowledge, organizations have been forced to develop new methods of deploying instruction.”

Some significant trends in learning:

• Many learners will move into a variety of different, possibly unrelated fields over the course of their lifetime.
• Informal learning is a significant aspect of our learning experience. Formal education no longer comprises the majority of our learning. Learning now occurs in a variety of ways – through communities of practice, personal networks, and through completion of work-related tasks.
• Learning is a continual process, lasting for a lifetime. Learning and work related activities are no longer separate. In many situations, they are the same.
• Technology is altering (rewiring) our brains. The tools we use define and shape our thinking.
• The organization and the individual are both learning organisms. Increased attention to knowledge management highlights the need for a theory that attempts to explain the link between individual and organizational learning.
• Many of the processes previously handled by learning theories (especially in cognitive information processing) can now be off-loaded to, or supported by, technology.
• Know-how and know-what is being supplemented with know-where (the understanding of where to find knowledge needed).

Background

Driscoll (2000) defines learning as “a persisting change in human performance or performance potential…[which] must come about as a result of the learner’s experience and interaction with the world” (p.11). This definition encompasses many of the attributes commonly associated with behaviorism, cognitivism, and constructivism – namely, learning as a lasting changed state (emotional, mental, physiological (i.e. skills)) brought about as a result of experiences and interactions with content or other people.

Driscoll (2000, p14-17) explores some of the complexities of defining learning. Debate centers on:

• Valid sources of knowledge – Do we gain knowledge through experiences? Is it innate (present at birth)? Do we acquire it through thinking and reasoning?
• Content of knowledge – Is knowledge actually knowable? Is it directly knowable through human experience?
• The final consideration focuses on three epistemological traditions in relation to learning: Objectivism, Pragmatism, and Interpretivism
  • Objectivism (similar to behaviorism) states that reality is external and is objective, and knowledge is gained through experiences.
  • Pragmatism (similar to cognitivism) states that reality is interpreted, and knowledge is negotiated through experience and thinking.
  • Interpretivism (similar to constructivism) states that reality is internal, and knowledge is constructed.

All of these learning theories hold the notion that knowledge is an objective (or a state) that is attainable (if not already innate) through either reasoning or experiences. Behaviorism, cognitivism, and constructivism (built on the epistemological traditions) attempt to address how it is that a person learns.

Behaviorism states that learning is largely unknowable, that is, we can’t possibly understand what goes on inside a person (the “black box theory”). Gredler (2001) expresses behaviorism as being comprised of several theories that make three assumptions about learning:

1. Observable behaviour is more important than understanding internal activities
2. Behaviour should be focused on simple elements: specific stimuli and responses
3. Learning is about behaviour change

Cognitivism often takes a computer information processing model. Learning is viewed as a process of inputs, managed in short term memory, and coded for long-term recall. Cindy Buell details this process: “In cognitive theories, knowledge is viewed as symbolic mental constructs in the learner’s mind, and the learning process is the means by which these symbolic representations are committed to memory.”

Constructivism suggests that learners create knowledge as they attempt to understand their experiences (Driscoll, 2000, p. 376). Behaviorism and cognitivism view knowledge as external to the learner and the learning process as the act of internalizing knowledge. Constructivism assumes that learners are not empty vessels to be filled with knowledge. Instead, learners are actively attempting to create meaning. Learners often select and pursue their own learning. Constructivist principles acknowledge that real-life learning is messy and complex. Classrooms which emulate the “fuzziness” of this learning will be more effective in preparing learners for life-long learning.

Limitations of Behaviorism, Cognitivism, and Constructivism

A central tenet of most learning theories is that learning occurs inside a person. Even social constructivist views, which hold that learning is a socially enacted process, promotes the principality of the individual (and her/his physical presence – i.e. brain-based) in learning. These theories do not address learning that occurs outside of people (i.e. learning that is stored and manipulated by technology). They also fail to describe how learning happens within organizations

Learning theories are concerned with the actual process of learning, not with the value of what is being learned. In a networked world, the very manner of information that we acquire is worth exploring. The need to evaluate the worthiness of learning something is a meta-skill that is applied before learning itself begins. When knowledge is subject to paucity, the process of assessing worthiness is assumed to be intrinsic to learning. When knowledge is abundant, the rapid evaluation of knowledge is important. Additional concerns arise from the rapid increase in information. In today’s environment, action is often needed without personal learning – that is, we need to act by drawing information outside of our primary knowledge. The ability to synthesize and recognize connections and patterns is a valuable skill.

Many important questions are raised when established learning theories are seen through technology. The natural attempt of theorists is to continue to revise and evolve theories as conditions change. At some point, however, the underlying conditions have altered so significantly, that further modification is no longer sensible. An entirely new approach is needed.

Some questions to explore in relation to learning theories and the impact of technology and new sciences (chaos and networks) on learning:

• How are learning theories impacted when knowledge is no longer acquired in the linear manner?
• What adjustments need to made with learning theories when technology performs many of the cognitive operations previously performed by learners (information storage and retrieval).
• How can we continue to stay current in a rapidly evolving information ecology?
• How do learning theories address moments where performance is needed in the absence of complete understanding?
• What is the impact of networks and complexity theories on learning?
• What is the impact of chaos as a complex pattern recognition process on learning?
• With increased recognition of interconnections in differing fields of knowledge, how are systems and ecology theories perceived in light of learning tasks?

An Alternative Theory

Including technology and connection making as learning activities begins to move learning theories into a digital age. We can no longer personally experience and acquire learning that we need to act. We derive our competence from forming connections. Karen Stephenson states:

“Experience has long been considered the best teacher of knowledge. Since we cannot experience everything, other people’s experiences, and hence other people, become the surrogate for knowledge. ‘I store my knowledge in my friends’ is an axiom for collecting knowledge through collecting people (undated).”

Chaos is a new reality for knowledge workers. ScienceWeek (2004) quotes Nigel Calder’s definition that chaos is “a cryptic form of order”. Chaos is the breakdown of predictability, evidenced in complicated arrangements that initially defy order. Unlike constructivism, which states that learners attempt to foster understanding by meaning making tasks, chaos states that the meaning exists – the learner’s challenge is to recognize the patterns which appear to be hidden. Meaning-making and forming connections between specialized communities are important activities.

Chaos, as a science, recognizes the connection of everything to everything. Gleick (1987) states: “In weather, for example, this translates into what is only half-jokingly known as the Butterfly Effect – the notion that a butterfly stirring the air today in Peking can transform storm systems next month in New York” (p. 8). This analogy highlights a real challenge: “sensitive dependence on initial conditions” profoundly impacts what we learn and how we act based on our learning. Decision making is indicative of this. If the underlying conditions used to make decisions change, the decision itself is no longer as correct as it was at the time it was made. The ability to recognize and adjust to pattern shifts is a key learning task.

Luis Mateus Rocha (1998) defines self-organization as the “spontaneous formation of well organized structures, patterns, or behaviors, from random initial conditions.” (p.3). Learning, as a self-organizing process requires that the system (personal or organizational learning systems) “be informationally open, that is, for it to be able to classify its own interaction with an environment, it must be able to change its structure…” (p.4). Wiley and Edwards acknowledge the importance of self-organization as a learning process: “Jacobs argues that communities self-organize is a manner similar to social insects: instead of thousands of ants crossing each other’s pheromone trails and changing their behavior accordingly, thousands of humans pass each other on the sidewalk and change their behavior accordingly.”. Self-organization on a personal level is a micro-process of the larger self-organizing knowledge constructs created within corporate or institutional environments. The capacity to form connections between sources of information, and thereby create useful information patterns, is required to learn in our knowledge economy.

Networks, Small Worlds, Weak Ties

A network can simply be defined as connections between entities. Computer networks, power grids, and social networks all function on the simple principle that people, groups, systems, nodes, entities can be connected to create an integrated whole. Alterations within the network have ripple effects on the whole.

Albert-László Barabási states that “nodes always compete for connections because links represent survival in an interconnected world” (2002, p.106). This competition is largely dulled within a personal learning network, but the placing of value on certain nodes over others is a reality. Nodes that successfully acquire greater profile will be more successful at acquiring additional connections. In a learning sense, the likelihood that a concept of learning will be linked depends on how well it is currently linked. Nodes (can be fields, ideas, communities) that specialize and gain recognition for their expertise have greater chances of recognition, thus resulting in cross-pollination of learning communities.

Weak ties are links or bridges that allow short connections between information. Our small world networks are generally populated with people whose interests and knowledge are similar to ours. Finding a new job, as an example, often occurs through weak ties. This principle has great merit in the notion of serendipity, innovation, and creativity. Connections between disparate ideas and fields can create new innovations.

Connectivism

Connectivism is the integration of principles explored by chaos, network, and complexity and self-organization theories. Learning is a process that occurs within nebulous environments of shifting core elements – not entirely under the control of the individual. Learning (defined as actionable knowledge) can reside outside of ourselves (within an organization or a database), is focused on connecting specialized information sets, and the connections that enable us to learn more are more important than our current state of knowing.

Connectivism is driven by the understanding that decisions are based on rapidly altering foundations. New information is continually being acquired. The ability to draw distinctions between important and unimportant information is vital. The ability to recognize when new information alters the landscape based on decisions made yesterday is also critical.

Principles of connectivism:

• Learning and knowledge rests in diversity of opinions.
• Learning is a process of connecting specialized nodes or information sources.
• Learning may reside in non-human appliances.
• Capacity to know more is more critical than what is currently known
• Nurturing and maintaining connections is needed to facilitate continual learning.
• Ability to see connections between fields, ideas, and concepts is a core skill.
• Currency (accurate, up-to-date knowledge) is the intent of all connectivist learning activities.
• Decision-making is itself a learning process. Choosing what to learn and the meaning of incoming information is seen through the lens of a shifting reality. While there is a right answer now, it may be wrong tomorrow due to alterations in the information climate affecting the decision.

Connectivism also addresses the challenges that many corporations face in knowledge management activities. Knowledge that resides in a database needs to be connected with the right people in the right context in order to be classified as learning. Behaviorism, cognitivism, and constructivism do not attempt to address the challenges of organizational knowledge and transference.

Information flow within an organization is an important element in organizational effectiveness. In a knowledge economy, the flow of information is the equivalent of the oil pipe in an industrial economy. Creating, preserving, and utilizing information flow should be a key organizational activity. Knowledge flow can be likened to a river that meanders through the ecology of an organization. In certain areas, the river pools and in other areas it ebbs. The health of the learning ecology of the organization depends on effective nurturing of information flow.

Social network analysis is an additional element in understanding learning models in a digital era. Art Kleiner (2002) explores Karen Stephenson’s “quantum theory of trust” which “explains not just how to recognize the collective cognitive capability of an organization, but how to cultivate and increase it”. Within social networks, hubs are well-connected people who are able to foster and maintain knowledge flow. Their interdependence results in effective knowledge flow, enabling the personal understanding of the state of activities organizationally.

The starting point of connectivism is the individual. Personal knowledge is comprised of a network, which feeds into organizations and institutions, which in turn feed back into the network, and then continue to provide learning to individual. This cycle of knowledge development (personal to network to organization) allows learners to remain current in their field through the connections they have formed.

Landauer and Dumais (1997) explore the phenomenon that “people have much more knowledge than appears to be present in the information to which they have been exposed”. They provide a connectivist focus in stating “the simple notion that some domains of knowledge contain vast numbers of weak interrelations that, if properly exploited, can greatly amplify learning by a process of inference”. The value of pattern recognition and connecting our own “small worlds of knowledge” are apparent in the exponential impact provided to our personal learning.

John Seely Brown presents an interesting notion that the internet leverages the small efforts of many with the large efforts of few. The central premise is that connections created with unusual nodes supports and intensifies existing large effort activities. Brown provides the example of a Maricopa County Community College system project that links senior citizens with elementary school students in a mentor program. The children “listen to these “grandparents” better than they do their own parents, the mentoring really helps the teachers…the small efforts of the many- the seniors – complement the large efforts of the few – the teachers.” (2002). This amplification of learning, knowledge and understanding through the extension of a personal network is the epitome of connectivism.

Implications

The notion of connectivism has implications in all aspects of life. This paper largely focuses on its impact on learning, but the following aspects are also impacted:

• Management and leadership. The management and marshalling of resources to achieve desired outcomes is a significant challenge. Realizing that complete knowledge cannot exist in the mind of one person requires a different approach to creating an overview of the situation. Diverse teams of varying viewpoints are a critical structure for completely exploring ideas. Innovation is also an additional challenge. Most of the revolutionary ideas of today at one time existed as a fringe element. An organizations ability to foster, nurture, and synthesize the impacts of varying views of information is critical to knowledge economy survival. Speed of “idea to implementation” is also improved in a systems view of learning.
• Media, news, information. This trend is well under way. Mainstream media organizations are being challenged by the open, real-time, two-way information flow of blogging.
• Personal knowledge management in relation to organizational knowledge management
• Design of learning environments
  

Conclusion:

The pipe is more important than the content within the pipe. Our ability to learn what we need for tomorrow is more important than what we know today. A real challenge for any learning theory is to actuate known knowledge at the point of application. When knowledge, however, is needed, but not known, the ability to plug into sources to meet the requirements becomes a vital skill. As knowledge continues to grow and evolve, access to what is needed is more important than what the learner currently possesses.

Connectivism presents a model of learning that acknowledges the tectonic shifts in society where learning is no longer an internal, individualistic activity. How people work and function is altered when new tools are utilized. The field of education has been slow to recognize both the impact of new learning tools and the environmental changes in what it means to learn. Connectivism provides insight into learning skills and tasks needed for learners to flourish in a digital era.

References

Barabási, A. L., (2002) Linked: The New Science of Networks, Cambridge, MA, Perseus Publishing.

Buell, C. (undated). Cognitivism. Retrieved December 10, 2004 fromhttp://web.cocc.edu/cbuell/theories/cognitivism.htm.

Brown, J. S., (2002). Growing Up Digital: How the Web Changes Work, Education, and the Ways People Learn. United States Distance Learning Association. Retrieved on December 10, 2004, from http://www.usdla.org/html/journal/FEB02_Issue/article01.html

Driscoll, M. (2000). Psychology of Learning for Instruction. Needham Heights, MA, Allyn & Bacon.

Gleick, J., (1987). Chaos: The Making of a New Science. New York, NY, Penguin Books.

Gonzalez, C., (2004). The Role of Blended Learning in the World of Technology. Retrieved December 10, 2004 from http://www.unt.edu/benchmarks/archives/2004/september04/eis.htm.

Gredler, M. E., (2005) Learning and Instruction: Theory into Practice – 5th Edition, Upper Saddle River, NJ, Pearson Education.

Kleiner, A. (2002). Karen Stephenson’s Quantum Theory of Trust. Retrieved December 10, 2004 fromhttp://www.netform.com/html/s+b%20article.pdf.

Landauer, T. K., Dumais, S. T. (1997). A Solution to Plato’s Problem: The Latent Semantic Analysis Theory of Acquisition, Induction and Representation of Knowledge. Retrieved December 10, 2004 from http://lsa.colorado.edu/papers/plato/plato.annote.html.

Rocha, L. M. (1998). Selected Self-Organization and the Semiotics of Evolutionary Systems. Retrieved December 10, 2004 from http://informatics.indiana.edu/rocha/ises.html.

ScienceWeek (2004) Mathematics: Catastrophe Theory, Strange Attractors, Chaos. Retrieved December 10, 2004 from http://scienceweek.com/2003/sc031226-2.htm.

Stephenson, K., (Internal Communication, no. 36) What Knowledge Tears Apart, Networks Make Whole. Retrieved December 10, 2004 from http://www.netform.com/html/icf.pdf.

Vaill, P. B., (1996). Learning as a Way of Being. San Francisco, CA, Jossey-Blass Inc.

Wiley, D. A and Edwards, E. K. (2002). Online self-organizing social systems: The decentralized future of online learning. Retrieved December 10, 2004 from http://wiley.ed.usu.edu/docs/ososs.pdf.

This work is licensed under a Creative Commons License

 About the Author

Université de Paris 8

Well, could he? If you trawl the net, you will find that the majority of material on language acquisition – whether of a first or a second language – is strongly Nativist and often simply takes it for granted that Chomsky and Fodor have, between them, swept away all possibility of opposition. In the English-speaking world – the French, for example, are far more skeptical – the Universal Grammar or the language module rules supreme.

This page is simply an attempt to redress the balance; you will find a set of links to pages that offer alternatives to what appears to be the reigning paradigm. I add material as I find it, trying to give some indication of the arguments. Although I am neither a linguist nor a neuroscientist, I have tried to exercise some judgement over what to include, but you may find you disagree with a number of my calls. So if anyone has any comments – other than to pooh-pooh the whole idea of questioning Chomsky – I’d be glad to hear them.

 Chomsky does not believe that human language evolved from any previous animal communication system, but sprang into being from nowhere. Pinker disagrees with this, but there are problems for the Nativist view in admitting a Darwinian origin for human speech. One of the foremost critics of the Chomskian model from the evolutionary perspective is Terrence Deacon, whose book ‘The Symbolic Species’ is an attempt to show how language must have evolved gradually, and how the underlying rules, far from being too complex to be learned without the aid of a special module, are, in fact, child’s play. You may wish to read William Calvin’s review of the book. James Hurford, reviewing it for the Times Literary Supplement, is critical but ultimately favourable. Deacon himself has a paper on ‘meme theory’ from the Semiotic Review of Books on the web which does expand on one of his arguments.

 While Calvin, particularly in his conversations with Bickerton, seems sometimes sympathetic to the idea that language is an innate and specific competence, other neurologists are less charitable ; Ralph-Axel Mueller, in a paper entitled ‘Innateness, Autonomy, Universality?’ makes a point that is often put by brain-specialists – the specialization of certain regions of the brain for language processing is an end-result of development rather than being programed into the genome, and, in general, it cannot be said that any of the higher brain functions are innate.

It may be that the burgeoning interest in the evolution of language will prove to be one of the most crucial proving grounds of Nativism ; see the Paris Conference on Language Evolution web-site, where you will find abstracts of interventions by both Nativists and anti-Nativists (You will also find a link to the most recent Conference which is to be held at Leipzig). The Nativist position on modularity is strongly questioned by one strand in evolutionary thinking, which claims that our ancestors’ first symbolic communication system was gestural, and that language is intimately related to kinesis : they make the point that Broca’s area is concerned both with linguistic and with bodily sequencing.

For an intriguing piece on evolution and meme theory which takes on Chomsky, see Vaneechoutte and Skoyles ‘The memetic origin of language ; Modern humans as musical primates’. They cite Robin Allott, who has written a critique of Pinker’s “The Language Instinct”, arguing that one cannot construct an evolutionary account of language from within the Chomskyan tradition. Allot has other interesting papers on this page.

 For a Whorfian perspective on the evolution of language see Daniel Moonhawk Alford’s pages, hosted by Don Watson. Alford provides a line-by-line critique of Chomsky’s rebuttal of Whorf. For another critical view from within anthropology, see Chris Knight’s “Noam Chomsky ; Politics or Science?” , who argues that that Chomsky’s anarchist politics are inversely related to his linguistics paradigm, which was moulded initially by military and other corporate requirements..

 Quartz and Sejnowski, in their ‘Constructivist Manifesto’, argue that Chomsky and Pinker have avoided confronting the problems inherent in modeling a ‘non-stationary mechanism’. They argue that :

Two themes emerge from finding a structural measure of representational complexity: (1) development is a progressive increase in the structures underlying representational complexity, and (2) this increase depends on interaction with a structured environment to guide development. These form the basis of neural constructivism, the developmental theory we present.

(N.B. Ash Asudeh, of the University of Edinburgh, disagrees that they have refuted Fodor and Chomsky).

A group of British cognitive scientists ran a ‘Constructivist Workshop’ for a while. An outline of their meetings, with useful bibliographical indications, is still on the web, although they announce a little ruefully that ‘the workshop has been temporarily discontinued’. However, the webmaster, Gert Westermann, has several of his own papers on the constructivist approach to language available from his home page.

 Deacon sees language as being basically simple in structure – simple enough to be learned by a two-year-old ; Nativists often claim that it is, in fact, far too complex to be learned without help. One of the critical moments in any argument with a Generative Grammarian is when s/he will draw up a list of ‘difficult’ sentences and then lean back with a satisfied smile, asking you to show how a child could have worked them out for herself. I always feel a little nonplussed at this, for often I am unable to see why it is that the sentences are supposed to be so obscure ; their difficulty seems to be a product of the linguist’s analytical tools, rather than of the sentence-structures themselves. Richard Hudson, who is Professor of Linguistics at University College London, seems to be on to something ; he believes that if you look to the words, the meaning will out. Have a look at The difficulty of (so-called) self-embedded structures – you’ll have to scroll down the page and then load it down. Hudson has developed a theory of language structure which he calls ‘Word Grammar’, and also provides a list of links to sites representing other approaches.

 In a similar vein, Thomas Schoenneman (pdf file) argues that the structural similarities found across languages are of a very simple nature, and that they are best explained as arising from the fact that “all languages attempt to communicate the same sorts of semantic information”.

Addressing the same argument, some linguists have been working with computers to see whether they can write programs that will learn rules of grammar without needing the specialist module that the Nativists believe in. Although no-one has yet produced a machine that can learn a fully-fledged language, some specialists do claim to have trained their computers to deduce rules in such a way as to falsify the Chomskian impossibility arguments. For one example, have a look at Gerry Wolff’s ‘Language Learning as Compression’ page. (References to other, similar attempts will be found on other pages to which I shall be pointing).

A model of learning which its authors see as applying to all forms of knowledge – language as much as any other – is Anderson’s ACT*. At one time, you could access an on-line tutorial from the page, but this seems to be difficult to reach at the moment.

Martin Redington and Nick Chater are working in a similar way – although they are not specifically interested in falsifying the Nativist view. Geoffrey Sampson, one of the participants in the Paris Conference, working in the same area, is far more dismissive of ‘the Language Instinct’, and claims to have countered every one of the Nativist arguments in his book ‘Educating Eve’. His paper “There is No Language Instinct”, given at the Conference, is on-line.

Both Wolff and Hudson see language as consisting of a network or set of networks. One of the most powerful of anti-modularity voices is that of Elizabeth Bates ; several of her works can be loaded down from her home page. She also takes a network view of language – you can look at “On the inseparability of grammar and the lexicon: Evidence from acquisition, aphasia and real-time processing”, for example (a PDF file) – and she is particularly good at picking holes in the Nativist arguments advanced on the basis of the characteristics of ‘language-savants’ such as Williams syndrome children : see On language savants and the structure of the mind, another PDF file. In fact, everything on this site is worth looking at, and a happy life-time could be spent following up the bibliographical leads. You will also want to look at Annette Karmiloff-Smith’s “Precis of Beyond modularity: A developmental perspective on cognitive science”. (Behavioral and Brain Sciences – an unedited preprint).

Bates and Karmiloff-Smith’s colleague (or coconspirator?) Jeff Elman also has a number of papers on-line, including “The emergence of language: A conspiracy theory” (in PDF format). Elman can be described as a ‘connectionist’.

Another important source in this tradition is Brian MacWhinney’s page, where several of his articles – in PDF format again – are available for down-load. MacWhinney sees linguistics in general, and Chomskyan linguistics in particular, as having neglected competitive models of grammar. They fail to see that “Language structure and processing are completely intertwined: There is no distinction between competence and performance”(Competition and Teachability, 1986).

 (The Stanford Encyclopedia of Philosophy provides a short article on connectionism, with some useful links). Also well worth looking at is David Plaut’s page, which has a number of links to his essays, including “Language acquisition in the absence of explicit negative evidence: how important is starting small?”, written with Douglas Rohde, in which the authors argue that “under a statistical model of the language environment, Gold s theorem and the possible lack of explicit negative evidence do not implicate innate, linguistic-specific mechanisms”. (I found their discussion of Gold particularly useful).

One of the main voices in connectionism is that of William Bechtel ; his article entitled : “What knowledge must be in the head in order to acquire language” (available as pdf file from his Publications page) , in which he argues against Fodor that language cannot be uniquely situated in the mind, is available in PostScript format. Grounds for skepticism concerning connectivist claims are voiced by the philosopher István S. N. Berkeley in his essay “Some Myths of Connectionism”.)

 Fodor sees Connectionism as a born-again Behaviourism. This characterization is enthusiastically embraced by Ullin T. Place, in ‘Behaviourism as a Standpoint in Linguistics’, published in the net review ‘Connexions’. In another article, ‘The Role of the Hand in the Evolution of Language’, elsewhere on the web, Place puts forward a Behaviourist model of the evolution of language, which he sees as developing first through gesture.

 Two further critiques of Chomsky’s position from a behaviourist perspective can be downloaded in pdf form from the site of the Review ‘Analysis of Verbal Behavior : David Palmer’s ‘Chomsky’s Nativism; a Critical Review’ and ‘Chomsky’s Nativism Reconsidered’ can be downloaded from the ‘Select Electronic Reprints’ page. Palmer sees Chomsky’s review of Skinner’s ‘Verbal Behavior’ as having been ‘harmful to linguistics’.

 Bates and Karmiloff-Smith do not deny modularity as such – but see it as developing through learning. William O’Grady, at the University of Hawaii, on the other hand, may be described as taking a ‘nativist’ position, but with no need for such a thing as a grammar module. He writes “the phenomena that lie at the core of traditional work on syntax (the architecture of phrase structure, pronominal co-reference, control, agreement, constraints on extraction, and the like) can be explained by the right theory of sentence processing-a theory that does not make reference to a grammar at all.” You can access several of his papers from the site, including ‘The Radical Middle’ (scroll down and load as pdf).

 Another model of interest is Ezra Van Everbroeck’s CLASPnet. This is a web-version of his M.Sc dissertation, which presents, he says “an original model illustrating the relevance of connectionist simulations to theoretical linguistic research.”

Another well-known critic of Chomsky’s is Roger Schank, a specialist in Artificial Intelligence. In the page indexed here, he is interviewed for John Brockman’s ‘The Third Culture ; Beyond the Scientific Revolution’ (Simon and Schuster, 1995). Chomsky, he says, represents everything that’s bad about academics, (at one point he could not talk about what he considers to be Chomsky’s charlatanism without getting angry). In particular, he was enraged by Chomsky’s deliberate exclusion of meaning. John F. Sowa, in a detailed comparison of Schank’s work with that of Richard Montague, looks at how a lexicon might be constructed.

 Chomsky’s distinction between competence and performance, and his dismissal of the latter as good evidence for linguists has, it seems, condemned those interested in language to spend the rest of their lives contemplating such phrases as ‘John promised Bill to leave’ or ‘John asked Bill for permission to leave’ (both from Chomsky’s ‘Language and Mind’, Harcourt, Brace Jovanovich, 1972). Given the richness of human expression, this is a self-denial of awesome proportions. Is it justified? Robert de Beaugrande argues that it is not – he also directs his argument at Stephen Krashen’s attempt to build on Chomsky’s work to elaborate a theory of second-language acquisition. In another paper, de Beaugrande holds that Chomsky has lead a ‘performative campaign to replace real language with ideal language’ which allows him to avoid confronting the social nature of linguistic processes.

Ngoni Chipere of the University of Cambridge, in his paper, ‘Real Language Users’, suggests that if we look at the way people really use language, instead of exchanging phrases between linguists, we discover that there are wide variations in the degree to which people can handle grammatical complexity (and see Huttenlocher, below). He compared skill in the comprehension of grammatically challenging sentences on the part of educated and non-educated subjects, finding that that highly educated non-native speakers may show a greater ability to understand challenging sentences than less educated native speakers. (It has to be said that the educated speakers were, in fact, linguists – which may account for their being good at the kind of games that linguists play).

 Another linguist whose main work is constructing grammar with the aid of the computer is Remko Scha of the Institute for Logic, Language and Computation of the University of Amsterdam. In “Language theory and language technology; competence and performance”, first published in Dutch in 1990, he argues that : “The current generation of language processing systems is based on linguistically motivated competence models of natural languages. The problems encountered with these systems suggest the need for performance-models of language processing, which take into account the statistical properties of actual lanuage use”.

 While some construct computer programs to learn languages, others are actually looking at the ways in which real flesh-and-blood children do it. (For an idea of what is going on in this field, see the Standford “Child Language Research Forum” page. You can also go to the ‘Language Science Research Group” page, which has a rich set of resources, including several articles and some recordings – with transcripts – of children’s speech).

 Although a number of workers in this field – such as Boysson-Bardies, whose excellent ‘Comment la langue vient aux enfants’ has now been translated into English – take a Nativist stance, not all of them do. One interesting approach is that of Janellen Huttenlocher, whose investigations indicate that mothers’ verbal behaviour towards their children is crucial not only to their acquisition of vocabulary, but also to their mastery of grammar – children whose mothers talk to them a lot are better able to decode and encode complex sentences than are those who receive little stimulation (“With respect to syntax”, she writes, “the studies show that the mastery of complex syntax forms (recursive devices) is highly related to the proportion of complex speech used by their parents”. Chomsky believes that there are no significant differences in the degree to which individual native-speakers acquire competence). Although her work is not web-available, a journalistic summary may be found here – scroll down the page a little to find that part of the article.

 Other researchers into language acquisition – Melissa Bowerman, for example – can be found from the Max Planck Institute for Psycholinguistics home page.

 Michael Gasser and Eliana Colunga, in “Playpen : Towards an Architecture for Modeling the Development of Spatial Cognition”, argue that “the study of the acquisition of word meaning requires taking seriously non-linguistic cognition, in particular human vision and the pre-linguistic development of concepts”. “Playpen” itself is a connectionist modeling of what they characterize as a radical grounded conception of language acquisition.

 On vocabulary acquisition, Paul Bloom, a psychologist at Yale, has recently published ‘How Children Learn the Meanings of Words’. Bloom, who takes neither a nativist nor a connectivist position, argues that “word learning is the product of a set of cognitive and linguistic abilities that include the ability to acquire concepts, an appreciation of syntactic cues to meaning, and a rich understanding of the mental states of other people”. An article setting out the main arguments of the book will be found at the BBS Online site.

 Another specialist in child-development (and primatology), Michael Tomasello, is also skeptical of nativist claims ; he says that children are great imitators, compared with our chimpanzee cousins. They model their behaviour on that of the adults around them – they learn – for language as well as for other behaviours. Some indications of Tomasello’s thought can be found in an article written with Christophe Boesch, published in Current Anthropology, although this is more concerned with primates in general than the human animal in particular. (My thanks to Tim Dougherty for the reference).

 One beef I have with Chomsky is the way he airily declares out of court any work which is not within his own domain. Working from first principles, he is able to dismiss investigations – such as those of Basil Bernstein – without having taken the trouble to read the papers. To the extent that American linguistics has come under the thrall of Nativism, other ways of working have been marginalised – so that, for example, we hear little of Functionalist Linguistics (or of its French equivalent ‘enunciative grammar’). Could it be that the victory of the Nativist perspective is largely a matter of rhetorical power – both Chomsky and Fodor are formidable in argument, as Piaget discovered to his cost – rather than of substance? Konrad Koerner, of the University of Ottowa, sees the Chomskian revolution as being due to factors other than its adequacy, elegance or economy ; he offers a socio-historical outline of the rise of Generative Linguistics. Rather more gently, Keith Allan, of Monash University, argues that while Chomsky’s work has value, it is not the only way of going about things.

The most often cited alternative to Chomsky’s model is that of Halliday’s Systemic Functional approach. The Systemic Meaning Modelling Group at MaquarieUniversity is the primary web resource for this. Then you could see The Systemic Functional Room, maintained by Noboru Yamaguchi, of Tohoku University, Japan, (but beware of some very aggressive graphics) and you can also consult Carol Chapelle’s page on Functional linguistics. (Ms. Chapelle asks her students in applied linguistics to compare Chomsky and Halliday).

Some of Chomsky’s own students became dissatisfied with certain features of his model, and were instrumental in forging a distinct brand of language study – Cognitive Linguistics. An interview with George Lakoff by John Brockman gives some background into how the break came about.The International Cognitive Linguistics Association has its own site, which is not, at the moment, particularly exciting. An introductory bibliography for the field has been compiled by Dick Hudson.

There are several papers written in this tradition that you can load down from Zouhair Maalej’s page – such as “Metaphoric Discourse in the Age of Cognitive Linguistics (with special reference to Tunisian Arabic).

Out of the same stable is Fillmore’s Construction Grammar. There is a page dedicated to this on the Berkeley server, put together by Charles Fillmore and Paul Kay. Although it hasn’t been updated since 1997, it still offers a couple of chapters of a book they were writing on the grammar, and a number of lectures. A bibliography on Construction grammars is also available. (Richard Hudson compares his own Word Grammar with Fillmore’s in a recent document available from his home site).

In a similar vein, a web-page put up from Kazan University, under the aegis of the Russian Association of Artificial Intelligence, offers The Web Journal of Formal, Computational and Cognitive Linguistics. Several interesting papers are available for down-loading – including Two Paradigms of Linguistics : The Semiotic Versus Non-Semiotic Paradigm by Sebastian Shaumyan (I can’t load the original page any more – this link takes you to the paper via another site – load down as pdf)). Shaumyan argues that Chomsky has ignored the relationship between sound and meaning, and advances what he calls the semiotic paradigm, continuing the tradition in linguistics as rooted in the work of Saussure. Shaumyan also posted a short statement of his case against Chomsky’s psychologism to the Linguist list.

Other dissatisfied Chomskans can be found at the HPSG Server at Stanford. This has a good set of links, and interviews with Ivan Sag, Bob Carpenter, Dan Flickinger and Hans Uszkoreit (Note – this has been shifted from the University of Ohio server, losing some items in the process, it seems).

There is also a ‘Beyond Chomsky’ Website run by Bernard Paul Sypniewski out of Rowan University, hosting a number of papers by linguists such as Victor Yngve (‘Human Linguistics – the Hard Science Alternative’). Its ‘statement of purpose’, written by Bruce Richman opens :”The main obstacle that we have today to clearly understanding the nature and origin of language is the overly formalistic, anti-empirical, anti-historical influence of Chomsky’s paradigm for doing linguistics.” Bernard Sypniewski encourages contributions. (I can’t seem to access this site using Mozilla on OSX. Bruce Richman tells me that there is a site menu on the left hand side, but I can’t see it. I can’t see it using Opera on Linux either. Perhaps it’s just for Windows people).

For an early call to go “Beyond Chomsky”, see Leon James’ “Prolegomena to a Theory of Communicative Competence”, written in 1969, and which takes on the task of evaluating the Chomsky revolution ‘ten years after’.

On a rather different tack, James Cooke Brown, who has spent many years teaching people to speak the logical language Loglan, argues that it is his experience that the extension and change in world-view and thinking-patterns his students report is good evidence for the Sapir/Whorf hypothesis – the latter is, of course, decried by Nativists, such as Pinker, who see in it a denial of the unicity of language. (There seem to be at least two languages going by the name of Loglan, one of which is a programming language developed by a Polish group of computer specialists in the late 70s (now supported by the University of Pau), while the other, developed by Cooke Brown, was, it seems, specially devised to test the Sapir/Whorf hypothesis. A breakaway version called Lobjan, also exists, and may even have more support than Brown’s original version. (This page is sometimes difficult to access, but does seem to be still active).

M. Craig Edwards

Gary E. Briers

Texas A&M University

Abstract

Historically, one of the most constant features of America’s high schools is the structure of the school day. However, in the 1990s, “The most visible and perhaps significant change in the organization of the high school is the block schedule” (Cawelti, 1997, p. 41). There have been conflicting results regarding the effects of block scheduling on student achievement. Researchers in agricultural education have supported instructional practices that improve student learning at higher levels of cognition. Yet, little is known about the effects of scheduling on agricultural education and its influence on student cognition. This study compared higher- and lower-order thinking skills (HOTS and LOTS) achievement of students enrolled in animal science on a Traditional schedule to the achievement of students on a Block schedule. The responding sample

included 45 teachers representing 23 Traditional scheduled schools with 341 students and 22 Block scheduled schools with 325 students. Student achievement was measured by two

examinations/scales based on an extension of Newcomb and Trefz’ (1987) “levels of learning” model. The scales consisted of 33 HOTS and 23 LOTS items. Teachers answered a questionnaire describing themselves and their schools. Student achievement was slightly more than half of the “conventional” 70 % passing standard. T-tests revealed that neither HOTS nor LOTS performance of students on a Traditional schedule was significantly different than that of the Block scheduled students. Multiple regression analyses with hierarchical order of entry were performed. The moderator variables student length of FFA membership and teacher tenure significantly explained student variability for both levels of achievement (longer FFA membership and longer teacher tenure resulted in greater achievement); the scheduling variable Traditional

versus Block did not explain additional student variability in achievement. One could not

conclude that one schedule was superior to the other in improving student achievement.

Introduction/Theoretical Framework

Elmore (1995) stated, “Over the past decade the United States has been engaged in the most sustained period of educational reform since the Progressive Era” (p. 356). Evidence of impetus for reform has been well documented by reports such as Prisoners of Time (National Education Commission on Time and Learning, 1994) and Breaking

Ranks: Changing an American Institution (NASSP, 1996). These reports called for a restructuring of the American educational system, and frequently targeted “time” and its use in school-day scheduling patterns as a basic element to be altered. Moreover, learning theorists (Bloom, 1974; Carroll, 1989) have stated that time and its use is a

significant and essential component of student learning. Karweit and Slavin (1981) maintained “the ambiguity of the research studies to date, make the continuation of studies of time and learning important” (p. 158).

Researchers (Carroll, 1990; Kirby, Moore, & Becton, 1996) have maintained that one of the most constant features of America’s high schools is the structure of the school day. In support, Carroll (1990) contended, “For threequarters of a century–a period characterized by immense social, political, economic, and technological changes–the

high school has not changed its basic form of organization” (p. 360). Moreover, investigators have said, “The way time is organized in schools may have contributed to the educational deficiencies in American education identified in such reports as A Nation at Risk” (Wortman, Moore, & Flowers, 1997, p. 440). This “basic” or “traditional”

school-day schedule is one in which students attend between six and eight classes each school day, with a class lasting approximately 50 or so minutes (York, 1997).

However, Cawelti (1997) concluded, “The most visible and perhaps significant change in the organization of the high school is the block schedule” (p. 41). DiRocco (1998/1999) asserted, “Intensive schedules [i.e., block scheduling] can be a powerful catalyst for change and for improved instruction in our secondary schools when implemented properly” (p. 83). Although many “variations” of block scheduling exist (Canady & Rettig, 1995), two of the more common are the Modified A/B (Alternating Day) Block Schedule and the Nine-Week Accelerated (4X4) Semester Block Schedule. On the Modified A/B Block Schedule, the school day is divided into four instructional blocks of approximately 90 minutes each. Students alternate class attendance between “A” day classes

and “B” day classes, and may be simultaneously enrolled for as many as eight different courses. On this schedule, most courses meet every other day for an 18-week semester. On the Nine-Week (4X4) Block Schedule, the school day is also divided into four instructional blocks of about 90 minutes each, but students attend the same four classes

each day for the nine-week period.

Watson (1998) asserted, “In a block schedule, the [learning] tasks can be designed to take more time, be of greater depth, [and] require more inductive or higher-order thinking skills” (p. 97). Torres and Cano (1995) stated, “The use of thinking skills in problem situations is universally recognized as a prominent objective for all educational

academies” (p. 46), including agriculture. Moreover, researchers Cano and Newcomb (1990) concluded that agriculture teachers “should purposefully create learning situations which assist in the development of higher cognitive abilities in students” (p. 51).

Bloom, Engelhart, Furst, Hill, and Krathwohl (1956) described six levels of cognition, that is, levels of thinking often referred to as Bloom’s Taxonomy. This approach to describing thinking behaviors delineated cognition into lower- and higher-order thinking skills and conceptualized them in a hierarchical fashion (Bloom et al., 1956;

Newcomb & Trefz, 1987; Torres & Cano, 1995; Whittington, Stup, Bish, & Allen, 1997).

Using Bloom’s model as a framework, Newcomb and Trefz (1987) developed a similar model for classifying cognitive behaviors into “four levels of learning”: remembering, processing, creating, and evaluating (Figure 1). Whittington et al. (1997) stated,

“Research supports the theory that thinking at higher levels of cognition (thinking critically) is an indispensable skill and must be reinforced in schools” (p. 47). Cano and Martinez (1989) recommended, “Students of vocational agriculture should be challenged to develop stronger cognitive abilities and critical thinking abilities at higher levels

through the instruction they receive” (p. 364). However, Cano (1990) stated that there was “a paucity of findings regarding vocational education students’ level of cognitive performance. Specifically, research in determining the level of cognitive performance of vocational agriculture students was lacking” (p. 74). Whittington (1995)

recommended that additional research was needed to investigate non-teacher variables that may be influencing the level of cognition obtained during instruction.

Block scheduling has been accompanied by conflicting results regarding its effect on student thinking skills and achievement (Wortman et al., 1997). Kirby et al. (1996) found agriculture teachers to be “neutral or undecided” (p.357) when responding to the statement “Student achievement has improved with block scheduling” (p. 358).

However, Brannon, Baker, Morgan, Bowman, and Schmidt (1999) concluded, “Agriculture teachers agreed that as a result of block scheduling learning is more meaningful for all students” (p. 197). Yet, little is known about the effects of scheduling on secondary-level agricultural education and its potential for influencing the cognitive development of students (Kirby et al., 1996; Wortman et al., 1997). Is there a difference in achievement for students enrolled in an agriscience course, depending on the school-day scheduling pattern?

Purpose/Research Questions

The purpose of this study was to compare the higher- and lower-order thinking skills achievement of students enrolled for a secondary-level course in animal science on a Traditional school-day schedule to the achievement of students enrolled for the course on a Block schedule basis (i.e., Modified A/B (Alternating Day) and Nine-Week

Accelerated (4X4) Semester Block schedules). These research questions guided this study:

1. What are selected characteristics of students enrolled in and instructors teaching a secondary-level course in animal science?

2. What is the level of achievement for HOTS, as described by Newcomb and Trefz (1987), for students enrolled in animal science? a) Does level of achievement for HOTS of students on a Traditional schedule differ from that of students on a Block schedule?

3. What is the level of achievement for LOTS, as described by Newcomb and Trefz (1987), of students enrolled in animal science? a) Does level of achievement for LOTS of students on a Traditional schedule differ from that of students on a Block schedule?

4. Do moderator variables, e.g., student and teacher variables, explain variation in student achievement, and does scheduling pattern significantly explain variation in student achievement after effects of moderator variables have been removed?

Methods/Procedures

This was a descriptive study that employed the causal-comparative method to describe and explore possible cause and-effect relationships between school-day schedules and the achievement of intact groups. Gall, Borg, and Gall

(1996) stated that “the major advantage of the causal comparative method is that it allows us to study cause-and effect relationships under conditions where experimental manipulation is difficult or impossible” (p. 383).

The target population (Gall et al., 1996) consisted of students enrolled in and the instructors teaching the agriscience course Animal Science (AGSC 332) in Texas public schools during the fall of 1998. Schools that had offered/taught this course for the school years 1996-97 and 1997-98 (n = 388) were obtained from the Texas Education Agency and served as the sampling frame. The “experimental units” for this study were individual agriscience classes and teachers, but individual students were the sampling units within an agriscience class. This was a form of cluster sampling, which, according to Gall et al. (1996), “is used when it is more feasible to select groups of individuals

rather than individuals from a defined population” (p. 227). The responding sample consisted of 45 “volunteer” teachers and schools, representing 23 Traditional scheduled schools with 341 students and 22 Block scheduled schools with 325 students. Because the data for this study were provided by a volunteer sample, the results are

generalizable only to subsequent similar volunteer samples.

The students completed a two-part instrument. Part one consisted of selected demographic items, e.g., length of FFA membership. The second part of the instrument was an end-of-course achievement examination. Glaser (1963) maintained that achievement tests were appropriate for determining “the degree to which the student has attained criterion performance” (p. 519). The examination was developed from recommended curriculum materials for the agriscience course Animal Science (AGSC 332) (Instructional Materials Service, n.d.; Instructional Materials Service, 1998). It included 56 multiple-choice items selected for content validity in the areas of nutrition,

reproduction, health, and management of domestic animals. Three agricultural educators—a curriculum specialist, a classroom teacher, and a measurement specialist—reviewed the items for clarity and content.

The examination was divided into two scales based on an extension of Newcomb and Trefz’ (1987) “levels of learning” model (Figure 1). The two scales consisted of 33 higher- and 23 lower-order thinking skills items, respectively.

The LOTS portion of the examination was made up of remembering and processing items; the HOTS scale contained items at the creating and evaluating levels of learning (Newcomb & Trefz, 1987). The Cronbach’s coefficient alpha reliability estimate for the LOTS scale was .79, while the HOTS scale had a reliability estimate of

.78. Finally, teachers responded to a questionnaire that included selected multiple-choice items describing themselves and their schools.

A researcher-developed packet consisting of student questionnaires/examinations, teacher questionnaires, pre-coded scan sheets, and postage-paid return envelopes were mailed to the participating teachers. Due to varying end-ofcourse dates, two mailings were necessary. Teachers administered the student questionnaires/examinations and

completed their questionnaires at or about the same time. The student scan sheets were coded so that they could be identified with their teacher and school-day schedule. The returned scan sheets were inspected to ensure the number codes were still intact. Following scanning, the data were entered into a Microsoft Excel 97 spreadsheet file and

then imported into an SPSS 7.5 data file. T-tests were performed to compare means and explore differences for research questions two and three, with an a priori alpha of .05. Multiple regression analyses with hierarchical order of entry of predictor variables were performed to answer question four.

Results/Findings

As seen in Table 1, slightly more than one-half of the participating students were male and nearly 44 percent were female. Almost 70 percent of the students were Anglo, while three-in-ten identified themselves as “People of Color.” Slightly more than three-in-ten had never been an FFA member, and approximately seven-in-ten had been members for one or more years. Nearly three-fourths indicated at least “some experience” with domesticated animals, while slightly more than one-fourth said they had “little” or no experience (Table 1).

Nearly 90 percent of the teachers were male while slightly more than one-in-ten were female (Table 1). Concerning their education, the teachers were nearly evenly divided, that is, slightly less than half held only a bachelor’s degree while a slight majority had earned a master’s degree. Years of experience as an agriscience teacher was also nearly

evenly split with slightly less than half of the teachers having taught 12 or fewer years, and slightly more than half indicating 13 or more years of service. When asked about years of service at their current school, a slight majority replied that they had taught at their current school for 10 or fewer years, while slightly less than half indicated 11 or more years of service (Table 1).

1 8

Table 1.

Table 2.

Table 3.

To determine if school-day scheduling patterns significantly explain variability in student achievement after the effects of selected student and teacher variables were removed, multiple regression analyses with hierarchical order of entry of variables were performed. These procedures were done to control initial non-equivalence in the two

research groups. Correlation analysis revealed that there was a statistically significant relationship between the student variable length of FFA membership and end-of-course higher- and lower-order thinking skills achievement,

r = .53 and r = 46 (p < .01), respectively. That is, the greater the length of time the student had been a member of the FFA, the better they performed on the higher- and lower-order thinking skills achievement examination items.

Moreover, similar analysis demonstrated that there was a statistically significant relationship between the teacher variable teacher tenure and HOTS achievement (r = .34, p < .05). As a teacher’s length of tenure increased, the HOTS achievement of their students increased. (The variable “teacher tenure” combined an instructor’s years of experience as an agriscience teacher and their tenure at their current school. The resulting scale had a reliability coefficient estimate of .86.) Therefore, because of positive associations with student achievement, these two moderator variables were entered into a multiple regression analysis equation as step one in a hierarchical order of

entry procedure. Then, to determine if school-day schedules significantly explained additional student variability for end-of-course achievement, the scheduling pattern variable was entered in step two of the procedure. Thus, step two included the variable Traditional versus Block.

In Table 4, step one portrays regression of the variable HOTS achievement on the variables student FFA membership and teacher tenure. A statistically significant amount of student variability for HOTS achievement was explained by this entry: R2 = .324, F = 10.046, p = .000. But, when the variable Traditional versus Block schedule

was entered, there was not a significant contribution to the explanation of variance, R2 Change = .000, F = .020, p =.888. Further, when the dependent variable LOTS achievement was regressed on the independent variables entered in step one, i.e., student FFA membership and teacher tenure, the amount of variance explained was R2 = .231, F = 6.324, p = .004 (Table 4), which was significant at an alpha level of .05. The variable Traditional versus Block schedule was entered into the regression equation in step two; it did not explain additional student variability for LOTS achievement, R2 Change = .002,

F = .103, p = .750 (Table 4).

Table 4.

Conclusions/Implications/Recommendations

Glaser (1963) contended “achievement tests are employed to discriminate among treatments, that is, among different instructional procedures [e.g., scheduling patterns] by an analysis of group differences” (p. 520). This study compared the higher- and lower-order thinking skills achievement of students enrolled for a secondary-level course

in animal science on a Traditional school-day schedule to the achievement of students enrolled on a Block schedule.

The end-of-course HOTS achievement for all students was only very slightly more than half of the “conventional” 70 % passing standard, while their LOTS achievement was only slightly better (Table 2). Webster and Miller (1998) found similar results for an animal science examination administered to high school seniors in 12 Midwestern States. They concluded that the students were not strongly intrinsically motivated to excel on the test, and that “this factor most likely explains why the students did not perform better on the exam” (p. 318). Moreover, was there a significant lack of “alignment” or “congruence” between the curriculum these students were taught and the course content on which they were assessed? Hoyle, Steffy, and English (1994) suggested, “the result of incongruence is normally lower test performance on the part of the students, particularly if the test has been selected because it was congruent with the written curriculum” (p. 98). The examination used in this study was based solely

on the recommended curriculum materials for the course Animal Science (AGSC 332).

Was this a valid procedure if the requisite “alignment” did not exist?

The higher- and lower-order thinking skill performance of students on a Traditional schedule was not statistically significantly greater than that of the Block schedule students (Table 3). Moreover, when multiple regression analyses with hierarchical order of entry were performed, and the moderator variables student length of FFA

membership and teacher tenure were entered in step one, variability in HOTS achievement was significantly explained (Table 4). However, in step two, when the scheduling pattern variable Traditional versus Block was entered, there was no additional significant explanation of student variability (Table 4). Further, in the case of LOTS achievement, when the moderator variables student length of FFA membership and teacher tenure were entered in step one, variability in LOTS achievement was significantly explained (Table 4). Similar to HOTS achievement, in step two, when the scheduling pattern variable Traditional versus Block was entered, there was no

additional significant explanation of student variability in LOTS achievement (Table 4).

Based on these findings, one could not conclude that one school-day schedule was significantly superior to the other for the purpose of improving end-of-course achievement of students. Recommendations for future practice and research include the

following:

1. This study suggests that there may be an “incongruence” between the actual curriculum materials that teachers used to teach animal science and the recommended instructional materials. Hoyle et al. (1994) stated,

“curriculum mapping can reveal what was taught, in what order, and for how long . . .” (p. 90). So, a form of “curriculum mapping” should be used to identify the curriculum materials used by the instructors for this course. It might also be useful to examine the relationship between this study’s teachers’ use of the recommended materials and the performance of their students.

2. This study should be “replicated” using quasi- or experimental design procedures that will control potential extraneous variables (i.e., student length of FFA membership and teacher tenure), and thereby improve the generalizability of future results.

3. This study did not find a significant difference in the performance of learners depending on which school-day schedule pattern they received instruction. Would this result have been similar for other agriscience courses?

Mindful of this, it is recommended that this study be replicated for other agriscience courses.

4. Are there other moderator variables that significantly explain student variability in end-of-course achievement for the secondary-level course Animal Science? Further research should be performed to determine if additional variables do exist.

5. As a component to the study that yielded these findings, Edwards and Briers (in press) found that there was a significant difference in the achievement of students, when two different “block” scheduling patterns (i.e., Modified A/B and Nine-Week (4X4) Semester) were compared. In addition to the two patterns investigated by

that study, it appears that there are numerous “variations” of block scheduling regimens (Canady & Rettig,1995). Therefore, it is recommended that a two-part study be conducted. The purpose of the first part would be to identify and describe these varied block-scheduling patterns. Then, in part two one might conduct additional

comparative studies to determine if there are significant differences in student achievement depending on the learner’s school-day schedule.

6. Although the relationship between the use of school-day time (i.e., scheduling patterns) and student performance remains ambiguous, researchers (Canady & Rettig, 1995; Carroll, 1994) have suggested that there is a causal relationship between the use of block scheduling and an improvement in school climate (i.e., classroom environment), and further, the important role that “climate” can play in the behaviors of students and

teachers (Bloom, 1974; Hoyle et al.,1994; Kruse & Kruse, 1995). So, research should be undertaken to investigate how changes in school-day scheduling patterns may positively influence factors that comprise a school’s “climate,” and, subsequently, create learning environments that are more conducive to improved student achievement. For example, instructors teaching on different scheduling patterns may be exhibiting different teaching behaviors that are related to their students’ performance. Case studies or other qualitative techniques could be conducted profiling the teaching behaviors of these instructors.

1. After reading the material on topic-based learning  (chapter 9 what’s your topic), I would like you to compare both and say what similarities and/or differences you could find between them.
2. Analyse the lesson and identify the different steps of it: warm up, presentation, etc. ;  and from the point of view of the structure of a lesson (chapter 6 from reflecting teaching)
3. Can you also identify the steps of the learning cycle? what would be the exposure, recognition and production stages?
4. Analyse the activities used in the lesson following chapter 7 (Can we do ‘poker face’ again, Miss?)
5. Analyse the decisions involved in the planning of this lesson following chapter 8 (discussed last class)
6. Identify theoretical concepts studied during the first term in relation to the psychological theories of learning.
7. Analyse the use of the native language in the classroom and students’ interlanguage.
8. Give your opinion on this lesson: Did you like it? would you have done the same? would you change anything? why/ why not? account for your comments.

I’ll be looking forward to your comments and remember that the idea of the blog is for you to interact with your partners as well!!

I’ll be posting more material later on.

http://www.teachingenglish.org.uk/try/resources/britlit

http://www.teachingenglish.org.uk/try/resources/britlit/primary-learners

La evaluación de los aprendizajes es y ha sido uno de los temas que mayor preocupación acarrea a los docentes. En cualquiera de los niveles de la enseñanza y en cualquiera de las asignaturas las propuestas de evaluación señalan un camino de difícil resolución. La búsqueda de objetividad, por una parte y la necesidad de encontrar una estrategia que asegure que los estudiantes puedan expresar de la mejor manera todos sus conocimientos, por otra, consisten en los desafíos diarios de las tareas del docente. Si pudiéramos caracterizar a las buenas prácticas que recorren los diferentes niveles del sistema educativo elegiríamos las que satisfacen las siguientes condiciones: prácticas sin sorpresas, enmarcadas en la enseñanza, sin desprenderse del clima, ritmo y tipo de actividad de la clase en la que los desafíos cognitivos no son temas de las evaluaciones sino de la vida cotidiana del aula. Atractivas para los estudiantes y con consecuencias positivas respecto de los aprendizajes. Seguramente no hay una sola manera de evaluar correcta y esa es parte de su condición. Es la expresión de las prácticas cotidianas del trabajo en el aula y, por lo tanto, tiene el sello del tipo de trabajo diario que llevan a cabo docentes y estudiantes. En los procesos de calificación el requerimiento de las prácticas docentes consiste en posibilitar el mejor rendimiento de cada uno de los estudiantes. En el marco de los estudios obligatorios el máximo rendimiento consiste en posibilitar la máxima calificación, que es la que da cuenta de que se poseen, aprecian y satisfacen los conocimientos necesarios para el ciclo subsiguiente. Las apreciaciones respecto de las correcciones y las valoraciones de cada uno de los trabajos se deberán explicitar pero, son apreciaciones cualitativas y deben sostenerse como tales. El valor de cada uno de los trabajos debe ser apreciado por los docentes y comunicado a los estudiantes. Los límites, las posibilidades, las resoluciones originales, se constituyen en nuevas instancias de aprendizaje. No debieran confundirse con la acreditación de los conocimientos básicos de cada ciclo o nivel. El valor de los análisis cualitativos y el surco que dejarán en los aprendizajes de los estudiantes dependerá del valor de las sugerencias, los estímulos que provoque y el acierto con que se perciban los lugares potentes para el mejoramiento de esas producciones. Los docentes sostienen que una buena evaluación debe recuperar la comodidad de la enseñanza. Estar cómodo en un momento de alta tensión, como suelen ser los espacios de las evaluaciones, es todo un desafío. La comodidad se da en un clima de confianza, sin poner a prueba la salud del estudiante, tomando en cuenta el tiempo que utilizó para aprender, respetando sus intereses y posibilidades. La confección de los instrumentos o dispositivos de evaluación juega un lugar central en las evaluaciones. Analizar su validez y confiabilidad consiste en su segundo momento. La validez implica que midan lo que pretenden medir. La confiabilidad da cuenta de que aplicados en diferentes circunstancias obtienen similares resultados. Esta tarea se completa al construir los criterios con los que se va a evaluar y se la comunica a los estudiantes. Se trata de tres momentos de la evaluación: confeccionar los instrumentos, analizar su confiabilidad y validez y construir los criterios de evaluación para comunicárselos a los estudiantes. Estos momentos entraman diferentes y complejas tareas que contemplan propuestas creativas, rigor en el análisis y compromiso al transformar la evaluación en un nuevo acto de aprendizaje cuando los estudiantes comprenden y comparten el sentido de los criterios de evaluación con que se los evaluará. El análisis de la pertinencia de los criterios también nos provoca nuevas comodidades, aspiraciones que compartimos los docentes en el acto de evaluar. Al someter los criterios de la evaluación a los alumnos logramos también dar cuenta de nuestro compromiso por generar evaluaciones que transparenten las aspiraciones o expectativas del trabajo docente. Ejemplos de criterios pueden ser: la remisión a la bibliografía, la pulcritud en el análisis, el reconocimiento de las dimensiones de análisis más importantes, el recuerdo de datos, etc. Los criterios que seleccionamos dependerán del campo de conocimientos, de las concepciones de enseñanza y de las de aprendizaje. Los estudiantes sostienen que en una buena evaluación se cumple con lo que se prometió y el profesor valora el esfuerzo y la dedicación. Los docentes esperan, en el mejor de los sentidos, que las evaluaciones permitan dar cuenta de apreciaciones y fracasos justos. Diseñar y llevar a cabo buenas evaluaciones implica también tender un puente entre lo que esperan los estudiantes y lo que pretenden los profesores. Una manera posible de orientar la evaluación consiste en proponer a los estudiantes la confección de un portafolio. Los portafolios forman parte de las actividades de los estudiantes y pueden ser el instrumento elegido para la evaluación.

¿Qué es un portafolio?

 Los portafolios constituyen una manera de presentar los trabajos de los estudiantes con el objeto de favorecer la evaluación. Se trata de un registro de los aprendizajes en tanto reúne materiales que se elaboran en el proceso de aprender. También constituyen una colección ordenada de evidencias que presenta las producciones que se fueron organizando para los diferentes proyectos llevados a cabo en el aula. En los portafolios se incluyen las mejores producciones de los estudiantes luego de sus procesos de elaboración. Los portafolios no son iguales. Algunos docentes los utilizan sólo para el registro evaluativo y otros, los utilizan como parte de la estrategia de enseñanza aún cuando puedan, finalmente, utilizarlos también para la evaluación.

¿Cuál es la diferencia entre un portafolio y una carpeta de registro de trabajos?

Los estudiantes seleccionan sus mejores trabajos. Cada trabajo conforma un folio que no necesariamente puede ser guardado en una carpeta o caja. Estos trabajos son el resultado de trabajos en proyectos y no de las actividades diarias que se realizan. Cada folio ha sido confeccionado, revisado, actualizado, corregido, recoge sugerencias para el mejoramiento por parte de los docentes y da cuenta del progreso que puede realizar el estudiante a partir de numerosas intervenciones por parte de los docentes. Por último, hay folios que pueden ser el fruto de una producción colectiva mientras que las carpetas de trabajos siempre dan cuenta de producciones individuales.

¿Cuáles son los beneficios de trabajar con portafolios?

 Los portafolios pueden ser construidos en torno a problemas, grandes ideas, temas en controversia, biografías, propuestas de acción, entre otros. Dependerá de la asignatura y de la concepción de aprendizaje que se contemple el diseño de las propuestas o los ejes que posibilitarán la organización del portafolio. Los docentes que trabajen con portafolios ponen especial cuidado en la producción de los estudiantes entendiendo que los procesos de comprensión seguramente implican diferentes propuestas de elaboración de proyectos, de solución de problemas, trabajos en relación con temas, ideas o preguntas relevantes. Cada una de las propuestas demanda tiempo. Los folios se hacen y rehacen o se completan paulatinamente. No se trata de evaluaciones que se pueden realizar en un plazo breve sino de trabajos que demandan un tiempo considerable. Los beneficios tienen que ver con las producciones sostenidas que favorecen mejores procesos de construcción del conocimiento.

¿Cuál puede ser el contenido de un portafolio?

Dependerá de la asignatura y las características de los proyectos que se elaboren. En ciencias sociales un portafolio puede contener folios que se integren por un trabajo que reconstruye una biografía, una carta, un ensayo, el análisis de una fuente o de una representación pictórica de la época estudiada, la confección de un guión, entrevistas y su correspondiente análisis, etc. Los portafolios contienen más de un producto y cada producto, a su vez, demanda un trabajo diferente de producción y sistematización. Se confecciona durante un cierto tiempo y probablemente necesita de borradores y propuestas parciales. Requiere la crítica del docente y finaliza mediante un acuerdo entre el docente y el estudiante acerca del estado final de la producción que permite que pase a integrar uno de los folios del portafolio.

¿Es la mejor propuesta para la evaluación?

Difícilmente encontremos una propuesta para la evaluación que nos asegure que su implementación sea siempre provechosa y productiva. La evaluación no puede reducirse a la búsqueda del instrumento más certero. Las buenas prácticas posibilitan evaluaciones en circunstancias no previstas en donde una respuesta oportuna del estudiante o una buena pregunta que da cuenta de su proceso de pensamiento permite reconocer mejor los conocimientos adquiridos o el nivel de reflexión poseído. De esa manera no sólo se evalúan productos sino que se analizan o se reconocen procesos y se adoptan tanto los procedimientos formalizados como los que no lo son para obtener no sólo indicios sino apreciaciones valiosas en torno al conocimiento adquirido. En las escuelas no se debería actuar pensando que hay una sola manera posible de evaluar. Una buena evaluación debe otorgar confianza y generar, por el contrario al clima habitual que se instala con las evaluaciones, un espacio para permitir que los aprendizajes fluyan y se expresen con naturalidad. Posibilitar reconocer nuestros límites, reírnos de nuestras propias exigencias y recuperar el sentido de lo humano en el acto de aprender. Desde esta perspectiva, la evaluación recupera y es proveedora de seguridad y protege a los estudiantes y por tanto, se imbrica en la buena enseñanza.

Edith Litwin

The interlanguage continuum
L1 L2
X__________________________________¬¬¬¬¬¬¬¬¬¬________________X

Ss need to move along this continuum if they are to learn the L2. How can they do this? Let’s imagine, S makes this error:
John cans go. His T may correct him, or he may read the sentence or hear it. After a while he’ll start to realise his rule isn’t entirely accurate + needs refining. So, one way S can learn this is to make an error, i.e. try out a hypothesis + see if it is correct or not according to the feedback.

This happens continually – interlanguage gradually improves (gets nearer to L2 set of rules)!

‘By a gradual process of trial & error + hypothesis testing, learners slowly and tediously succeed in establishing closer & closer appropriateness to the system used by Native Speakers of the language.
P.168–Principles of Language Learning & Teaching, Brown

Sources of errors:
• overgeneralization
• language transfer ( or interlingual transfer)
• fossilization

STAGES OF INTERLANGUAGE DEVELOPMENT
It has been suggested there are some distinct stages along this continuum
1. RANDOM ERRORS
E.g. L is only vaguely aware there might be some systems, but has got little idea.
2. EMERGENT STAGE
L has begun to identify a system + to internalize the rules. These rules may not be correct (i.e. not the
same as the L2 rules) but they are the best the L has at this stage. At this stage we got a lot of backsliding,
they seem to have got the rule, then forget it. (e.g. present continuous instead of present simple) Can’t self-
correct very often.
3. SYSTEMATIC STAGE
Stronger system of rules + nearer to L2. More mistakes than actual errors.
4. STABILIZATION STAGE
Very few errors, mostly just mistakes of anything. One problem here – fossilization.

Where do you think you are?
NB You can be in different places for different parts of L2
e.g. stabilization – pres.simple
systematic – 2nd conditional
emergent – more complex conditional
And, NB you don’t always perform at the level you’ve reached (i.e. you make mistakes which you could easily correct) – WHEN? – nervous, upset, thinking about meaning not form: compare doing grammar in class to talking to NSs on train

CONCLUSION
Opportunities to make errors are crucial: NO GAIN WITHOUT PAIN.
• errors give Ss data on which to base improvements of their interlanguage rules
BUT, to much correction can lead to a lack of motivation:
• we need to be encouraging + not over correct, but not let too many important errors go uncorrected
• we need to point out important errors, but not so much that the L is discouraged from speaking at all.

Another cognitive style variable the one on which the most research has been performed is filed dependence/field independence (Witkin, Moore, Goodenough, & Cox, 1977). Sometimes called global vs. analytical thinking this variable reflects on how learners think and process information. The field dependent learner is one who processes information globally. This learner is less analytical, not attentive to detail, and sees the perceptual field as a whole. This whole resists analysis or decomposition. The field independent person on the other hand can easily break the field down into its component parts. He/she is typically not influenced by the existing structure and can make choices independent of the perceptual field. Field dependent persons are more socially oriented and therefore they respond more to reward and punishment (Ferrell, 1971). They also need more explicit instructions when material to be learned is disorganized. They also are less able to synthesize and analyze (Frank & Davis, 1982).

Psychological Differentiation and Dependence/Independence: Field independence/field dependence deals with the amount of psychological differentiation experienced. Differentiated systems are more complexly organized. The relationships between the system and the environment are more elaborate. Witkin and Goodenough (1981) describe the differentiation process as one of the creation of inner boundaries between the inner core of the self and the environment. Psychological activities also have boundaries and are separated from each other and the environment. Differentiation creates a hierarchical structure forming an articulated system. Field independence requires a restructuring of the perceptual or psychological field and therefore is a more differentiated process. According to Witkin and Goodenough (1981) field dependent learners are more socially oriented than filed independent learners. They pay more attention to social cues, they like to be with others and they seek learning and vocational experiences that put them in contact with people. Field dependent children perform less well on formal operations tasks than do field independent children, Brodzinsky (1985). Other researchers support this. For example: children, according to Witkin and Goodenough (1981), are more field dependent than are adults. There is a general movement toward field independence across development, but there are also great individual differences. Those who develop more rapidly toward field independence also develop greater competence in cognitive restructuring. Interestingly evidence is presented (from primitive agricultural and nomadic herding societies) which indicates that there is genetic selection of field independent subjects in primitive settings and that more are field dependent as the culture grows and becomes more modem.

Characteristics of Field Dependent/Independent Learners: Garger and Guild (1987) have summarized the characteristics of field independent and field dependent learners. These are reported below. From this table it is clear that, at least in the extremes, the two styles are very different.

Independence/Dependence Descriptions.

Learning Styles
FIELD-DEPENDENT	FIELD-INDEPENDENT
Perceives globally	Perceives analytically
Experiences in a global fashion, adheres to structures as given	Experiences in an articulate fashion, imposes structures of restrictions
Makes broad general distinctions among concepts, sees relationships	Makes specific concept distinctions, little overlap
Social orientation	Impersonal orientation
Learns material with social content best	Learns social material only as an intentional task
Attends best to material relevant to own experience	Interested in new concepts for their own sake
Requires externally defined goals and reinforcements	Has self-defined goals and reinforcements
Needs organization provided	Can self-structure situations
More affected by criticism	Less affected by criticism
Uses spectator approach for concept attainment	Uses hypothesis-testing approach to attain concepts
Teaching Styles
FIELD-DEPENDENT	FIELD-INDEPENDENT
Prefers teaching situations that allow interaction and discussion with students	Prefers impersonal teaching situations such as lectures. Emphasizes cognitive aspects of instruction.
Uses questions to check on student learning following instruction	Uses questions to introduce topics and following student answers
Uses student-centered activities	Uses a teacher-organized learning situation
Viewed by students as teaching facts	Viewed by students as encouraging to apply principles
Provides less feedback, avoids negative evaluation	Gives corrective feedback, uses negative evaluation
Strong in establishing a warm and personal learning environment	Strong in organizing and guiding student learning
How to Motivate Students
FIELD-DEPENDENT	FIELD-INDEPENDENT
Through verbal praise	Through grades
Through helping the teacher	Through competition
Through external rewards (stars, stickers, prizes)	Through choice of activities, personal goal chart
Through showing the task’s value to other people	Through showing how the task is valuable to them
Through providing outlines and structure	Through freedom to design their own structure

Garger and Guild (1987)

Below are two tables reproduced from Whitefield (1995) which expand on the field dependent/field independent styles labeled global and analytic. Whitefield shows how learners with these styles learn and suggests ways in which they should be taught.

Whitefield (1995) at http://cleo.murdoch.edu.au/asu/pubs/tlf/tlf95/whit271.html

Here is an excerpt from a modern ESL textbook (“Workout Advanced” by Paul Radley and Kathy Burke, published by Nelson English Language Teaching). The textbook was used in an English class Tom attended at a language school in England.

The textbook presents a grammar rule for ordering adjectives (“size/age/shape/colour/nationality/material”). Then it gives only two examples. After that, you are expected to do an exercise.

Obviously, you cannot do the exercise using your intuition (what intuition can you get from seeing only two examples?). The textbook wants you to use the grammar rule. You are supposed to classify the adjectives into one of the groups (“size”, “age”, etc.), and then put them in order according to the rule. In other words, you are supposed to:

1. recall the rule (“size – age – shape – color – nationality – material”)
2. for every adjective, answer the question “Is it an adjective of size, age, shape, color, nationality, or material?”
3. order the adjectives according to the rule

Now imagine doing all these things whenever you’re writing or saying a sentence with 2 or more adjectives. Can you guess how much time it would take you to build the sentence?

Is there another way? Yes, there is. You can learn by input. You can read a lot of sentences with adjectives and get a natural, intuitive knowledge of adjective order. Instead of memorizing the rule and using it to build sentences, you can get correct sentences into your head and your brain will imitate them. The “input way” is easier and it lets you speak and write faster.

Of course, learning by input is not effortless. You have to spend a lot of time reading and listening to English. However, if you learn e.g. by reading a book that you like, it can give you pleasure and motivation.

Grammar rules vs. input — summary

Learning with grammar rules has two important disadvantages:

• Memory effort. It is difficult to memorize a grammar rule. The process is highly artificial; it is like memorizing a poem. It is much easier to read some example sentences and let your brain do the rest.
• Time. You need a lot of time to use a grammar rule. You have to remember it, you have to see if it can be used in your sentence, then you have to build the sentence according to the rule. Writing a sentence with grammar rules is like solving a mathematical equation. If you use grammar rules often, you can’t speak or write in English fluently.

Can grammar rules be useful?

Yes, they can. For example, if you don’t hear (or read) some word or grammar pattern frequently, it may be hard to acquire a natural, intuitive knowledge of it. For example, it may be hard to acquire an intuitive knowledge of the future perfect tense (a grammar structure used e.g. in this sentence: “By 2050, life in Europe will have changed.”) just by reading books in English, because the future perfect occurs relatively rarely in books.

If you want to use the future perfect in your own sentences, you can memorize a rule for it. The rule will tell you when to use the future perfect and how to use it correctly. In a similar way, you can memorize other rules or definitions of words which are used rarely.

So you could substitute grammar rules for intuition. The problem with this method is that you can’t remember too many rules (memory limit). Also, it would slow you down if you had to use many rules when speaking or writing (time limit). Therefore, most of your knowledge must be intuitive (based on input).

Grammar rules may be useful for using rare words and grammar patterns, but we think there is a better way. You can build your intuition “the input way” for every rare grammar pattern. How? You can artificially increase the frequency with which you see that grammar pattern. For example, if you don’t see the future perfect often, you can add 20 example sentences with the future perfect to your SuperMemo collection. SuperMemo will make you repeat the sentences regularly, and so will help you to build an intuitive knowledge of the future perfect.

Stop asking people to tell you grammar rules

Many learners have a strange habit. When somebody (e.g. a teacher) tells them the correct way to say something in English (“We say big red car.”) or corrects their mistake (“You can’t say red big car“), they like to ask “why?”.

However, the question “why?” has no real answer. When asking the question, learners want to hear a grammar rule (e.g. “We say big red car because adjectives of size come before adjectives of color”). But the rule is not the reason why we don’t say “red big car”. The rule is only a description of native speakers’ habits. It was invented by some linguist who simply noticed that native speakers never say “red big car” or “white small house”.

In other words, it is not true that native speakers say “big red car” because they know the rule and follow it. It’s the other way around. The size-color rule exists because native speakers say “big red car”. Native speakers are the ones who create the language. Grammar rules only follow native speakers’ habits.

We think that it doesn’t make much sense to ask the question “why is that sentence correct, and not the other one?”. The only good answer to that question would be “Because native speakers say that sentence, and not the other one.”. Instead of wondering “why?”, simply learn the correct way. You don’t have to care that a linguist wrote a rule for it. Follow native speakers, not grammar rules.

What do you think about it? Do you agree? Leave your comments!