As we have seen in module 1, our educational systems are realizing the importance of providing opportunities for learners to acquire realistic employability skills. This suggests that learners must have an awareness of their own individual learning needs, and develop skills and habits that lead to life-long learning. Technology-based or enhanced learning environments are positioned to facilitate the processes required to meet these new educational objectives. How we learn may in fact be becoming more important than what we learn. The implications from research in educational psychology and learning theory and can provide educators and designers with the knowledge needed to create more effective interactive learning activities and environments. Much of this knowledge may already be implicit for most of you, but an understanding for and appreciation of multiple epistemological perspectives can provide you with more options for successfully integrating technology-based learning activities in your classroom. It takes approximately two hours to complete this module.

Simply put, learning theories are descriptive, in that they describe how learning actually takes place. Quite often, learning theories are confused with instructional design theories, which are more involved with events outside of the learner that facilitate learning. In this module, we are more concerned with what goes on inside of a learner's head when learning occurs. By understanding how individuals learn, you can select or design learning activites and environments that have a better chance of being successful.

Behaviourism deals with regulating or controlling behaviour through stimulus-response reinforcements. B.F. Skinner (1904-1990) is often associated with behaviourism theories. His work with conditioning rats in mazes during the early 1930’s actually led to theories designed to teach human beings based on operant conditioning, by offering a reward when a desired response is given. Ivan Ivan Pavlov (1849-1936) is known for demonstrating that a dog would reflexively salivate upon hearing a bell, after he came to associate the bell with feeding time. This is known as classic conditioning. Basically, behaviourism theory discounts higher mental activities and defines learning as nothing more than the acquisition of new behaviour. Teachers ask learners to practice a desired behavioural response to a chosen stimulus, and reward behavioural operants that they wish to be repeated.

One of the teaching models associated with behaviouristic practices is passive reception. Top-down, didactic teaching is the predominate mode of instruction here, where knowledge is ‘poured into’ the learner. Delayed gratification or reward-based motivation is associated with behaviourism, such as in receiving a star for completing piece of work or passing a test. In regards to a technology-based learning environment, we may consider that putting too much textual information online without enough interactivity could easily fit into the passive reception model, where knowledge is being passively transferred from the screen to the learner. 

It is logical to assume that a behaviourist approach to teaching and learning produces individuals highly suited to routine tasks and low-level processing, and putting too much emphasis on the hierarchical nature of prerequisites may limit the potential for some learners to develop more complex skills (Hannafin, 1997). Many technology-based training programs may use a more behaviourist approach, especially when designed to impart procedural knowledge, such as how to operate a piece of equipment, or perform a specific, mundane task. This may have its place, but for the most part, it is far removed from the independent thought that individuals require to be flexible and adaptable in today’s workforce. If we are to foster the epistemological curiosity that leads to the development of critical thinking skills in our youth (or even adults), then perhaps we shouldn’t be thinking about behaviourist views. 
 

Although we may consider behaviourist ideas somewhat antiquated, it is interesting to note that many computer-based training programs are rooted in behaviourism. In them we find classic examples of drill and practice activities, simulations, and tutorials. Reinforcers are typically applied using text, visual or audio components, and most often the program incorporates a scoring and/or monitoring system that indicates the status of progress. Many teachers that are introduced to a new learning technology immediately try to devise learning activities based in behaviourism. A technology-based or enhanced learning environment can and perhaps should offer much more.

The capacity to achieve an outcome is much different from the ability to draw upon understanding from personal experience and exploration (Lagner, 1997). In modern contexts, employers don’t need individuals who can simply pass tests, as real world tasks, environments and situations are typically much more complex. Modern society requires higher skills, so that proper decisions should be informed positions. For example, it’s fine to know that pressing a green button usually halts a nuclear melt down in a power plant, but what happens when it doesn’t? Obviously the individual’s mind in this situation must be well stocked with rules that are relevant to a variety of potential situations, and this may require critical thinking skills and a more diverse knowledge base to draw on. This suggests that the knowledge the individual must possess is deep knowledge, firmly anchored and stored in such a way that it is useful for application and task performance that requires understanding, critical judgement and evaluation (de Jong and Ferguson-Hessler, 1996). This type of knowledge-in-use involves more than procedural or practical knowledge, or ‘knowing-how’ (Ohlsson, 1995). It also requires depth and different levels of abstraction that we can refer to as declarative knowledge, or ‘knowing that”. Behaviourism may have its time and place for specific training, but there are perhaps more appropriate theories to underpin our learning environments.

Cognitivism is somewhat the converse of behaviourism, as it deals more with how an individual’s mind works, thinks, remembers and learns – being based more on what occurs from the inside-out, rather than the outside-in.  It is occasionally referred to an information processing type of learning model, where knowledge is moved from one location to another. It holds that learner-constructed, relevant knowledge that is built upon prior knowledge is more likely to be acquired and retained for practical use, and in time, the action that this knowledge produces may become an entirely automatic program within the learner. With this, we can realize a few distinctions between procedural and prepositional or declarative knowledge.

Cognitivism sees learning as an active and creative process, where learning transcends knowledge reception and involves individual meaning making. Although new information must often be learned through behaviouristic practices, the learner must be allowed and encouraged to experiment with the new knowledge... making interconnections, seeing patterns and building new understandings. 

In designing a totally open technology-based learning environment based on a true discovery model (unhindered exploration), intervention by the system may actually be viewed as detrimental to the learner’s experience and growth (Schell, 1992). Thus, setting parameters and creating interaction may prove difficult. Of course it depends on what the educational goals are. For example, exploration and discovery learning can be used to develop proficiencies in knowledge acquisition and application skills associated with learning how to become a self-directed learner. This can be considered a meta-cognitive process, where the learner thinks about his or her own learning. 

Born on the heels of cognitivism, constructivism suggests that knowledge and information are formed in mental structures, which are tested and elaborated upon until the structures become established. Learners are encouraged to construct their own understanding, and validate their new perspectives through social negotiation and collaboration.

Grades and standardized testing seem to pose a dilemma with constructivist methods. Assessment should actually become part of an individual’s learning process so that it plays a larger role in them judging their own progress. With so much focus on standardized testing these days in our schools, it is no wonder that our current methods appear to be ‘stuck’. Technology is opening up new opportunities for improved learning and political pressure is being applied for standardized assessment. 

Constructivism approaches work well in most domains, however, with areas of knowledge that are considered to be ill-structured, such as history and medicine, there may not be any clear-cut right or wrong answers, and quite often, simultaneous understanding of conflicting viewpoints is difficult to obtain. From a learner’s point of view, there may be a tendency to oversimplify concepts, which may lead to the cognitive restructuring of knowledge that is based in error. 

Although constructivist techniques can prepare a learner to problem solve in a variety of situations, advanced ill-structured domains may contain so much knowledge that the learner cannot possibly store or retrieve it on demand. This has lead to a relatively new theory based on constructivism known as cognitive flexibility theory (CFT), (Spiro, et al., 1992). By presenting knowledge in a variety of ways or situations, learners acquire the ability to develop cognitively flexible processing skills. Prior knowledge goes through a situation specific assembly to form new knowledge. In other words, new understanding is constructed from prior knowledge, and the prior knowledge itself may be constructed to suit the particular situation. It would be like looking at the same problem from many different angles, and there may be a different solution posed from each of them.

Guided instruction is a model of learning that conforms to cognitive and constructivist theories. The learner constructs his own knowledge, but has guidance that facilitates the learning process. Here we can imagine an interactive technology-based learning environment that incorporates somewhat of a discovery-based model, with guided instruction from an agent, being the system, a tutor or otherwise. 

Humanistic theories deal with guiding students towards physical, mental, emotional, and moral development, where self-actualization is the goal (A. Maslow). Similar to social constructivist theories, students discover their personal interests and develop their potential as happy, healthy individuals by relating within a community. The emphasis is on authentic human relationships and the development of self-esteem, creativity and critical thinking skills. Like constructivism, humanism is based on the premise that we all construct our own perspective of the world, through our individual experiences and schema. Higher levels of understanding are possible when an individual reacts in an environment to develop form and value in their personal world, rather than simply sliding into a niche in it (Lagner, 1997).

The human brain can only hold eidetic information for a few seconds, unless it is consciously rehearsed and stored into long-term memory. When learning is meaningful, information is automatically stored. Current theories of learning are concerned with the creation of some level of understanding, whether it is in the form of an internal representation, or certain performances that can be observed or assessed. If we are looking for true quality in learning, then obviously all factors are related, in that mental representations must be able to have some value in performance and vice versa. The degree of knowledge and information acquisition, transformation, assimilation and retention in a learner seems to be dependent upon the significance or meaningfulness of it all to the learner. Thus, meaningful learning is concerned more with creating true or deeper understandings, rather than behavioural changes alone. This also implies that motivational and affective factors are important for creating effective (technology-based) learning environments.

One can easily sense that meaningfulness, critical thinking and creativity are linked. When encouraged to think ‘outside of the box’, individuals can become better problem solvers by exploring new perspectives and seeking out new understandings and solutions, sometimes ones that are unique or innovative. Langer (1997) talks about breaking with traditional top-down or bottom-up teaching methods with something she refers to as ‘sideways learning’. This type of learning incorporates a high degree of cognitive activity in maintaining ‘mindfulness’, a term which relates to certain psychological states:
 

Photography has always been considered somewhat of a creative endeavour. With an arsenal of technical know-how and visual awareness, a photographer seeks to solve problems to produce a representation of what he or she perceives. Renowned Canadian photographer Freeman Patterson (1979) spoke of ‘sideways thinking’ in his book, “Photography and the Art of Seeing”. 
 

Many students are attracted to the field of photography because it offers a creative means of personal expression for them. Many become quickly disillusioned when they discover that learning photography deals with the acquisition of much technical knowledge, which is an arduous task. Before students can ‘get out there’ and be creative, they are subjected a barrage of technical information on focal length, exposure, reciprocity, light dynamics, mathematical calculations, sensitometry, depth-of-field, and much more. While working in the field, their technical know-how becomes somewhat transparent and automatically contributes to problem-solving situations. There is nothing wrong however, with applying new knowledge in the field through experimentation, but some grounding principles should be present. In discovery approaches, these basic grounding principles could be considered overly general rules, where students can specialize and act upon their development as a function of their perspective on what is inappropriate or incorrect (Ohlsson, 1995). 

Due to the nature of the evolving workforce, working as a group is becoming the norm. Fostering interaction and collaborative problem solving in learners will help prepare them for this new reality. Collaborative learning is based on a learner-centered model in which the learners are active participants. Sharing one’s ideas in a community setting can deepen understanding (but still may not appeal to everyone). In addition to creating academic benefits, collaborative learning promotes the spirit of learning and increases competence in working with others, self-assurance and personal insight (McConnell, 1994).

Vygotsky (1896-1934) may not always be considered a constructivist because of his emphasis on the social context of learning, but his emphasis on learners creating their own concepts are definitely from the constructivist camp. His work contributes to the appreciation and importance of various external and social interactions found in constructivism. Learners can connect with each other and summarize concepts by analyzing, predicting, justifying, and defending their ideas with each other.

Activities that trigger learning mechanisms are evident in both individuals and groups, but the interaction in groups can trigger extra cognitive mechanisms. There is no guarantee that that the mechanisms will occur in collaborative environments, but designing appropriate activities can increase the likelihood that they will.

Collaboration basically has 3 components, which we can apply to technology-based learning environments:

• Conversation – verbalizing through written responses and/or audio/video
• Multiple perspective – reading, reflecting, cognitive restructuring
• Argument – conceptual conflict resolution, establishing internalized concepts

We have seen that behaviourist and cognitive theories of learning are based in individualism. Collaborative learning extends beyond the individual and is associated with more situated learning. The gap between the learner’s actual developmental level and potential developmental level is narrowed through interaction with peers of greater capability.  This is referred to as the zone of proximal development  (Vygotsky, 1978). Real world communities of practice (COP’s) reflect this theory, as in apprenticeship training, where those that are less skilled work alongside a peer or mentor and learn through authentic practice. There is a distinction between authentic and educational activities. If we are to foster a society of collaborative workers, then the instruction in a technology-based learning environment should fit within a context that at least copies or represents professional or authentic practices, being the regular practices of the proper culture. Within a distributed or social learning situation, more exists that just shared mental models, as the culture itself can strongly influence what and how an individual thinks and learns. By integrating and emphasizing real world activities in learning, we not only create more authentic and situated learning environments, but may also instil cultural values as well (Hannafin & Land, 1997). 

Learning and cognition may be fundamentally situated, and this involves creating understanding from experiences and phenomenon in context, where the context is seen as a primary component, contributing to the way in which an individual views the world. The relationship between theory, the real world and the classroom (or learning environment) is in itself situated (Cobb & Bowers, 1999). 

So then, learning strategies that are in context can encourage the active building of knowledge according to a specific community’s practices. Both cognitive and situational theories involve the movement of knowledge, skills or information from one location to another, and this is not always dependent upon context (Cobb & Bowers, 1999). Not all skills need to be (or can be) attained in a social context, but it seems obvious that there may be certain benefits when they are. Even motivational impetus for the learner can be realized from the culture itself. However, knowledge or concepts acquired in one context can remain associated with that context, and may not be easily transferred to other problem solving situations (Crews et al., 1997). Flexible cognition approaches may help with this. 

Since individuals learn much through interaction, technology-based learning environments should be designed to emphasize interaction between learners and encourage realistic or practical learning tasks. An agent in the environment that continually adjusts the level of guidance in response to a learner’s level of performance provides for scaffolding. Scaffolding can help to instil the skills necessary for more independent problem solving in future activities. 

It is more or less implied that the development of learning theories has been chronological. Table 1 illustrates this, and the dates suggested reflect eras of popularity and usage of various theories in education, and not necessarily the time periods when they where first conceived of. 
 

A learning style approach to learning emphasizes the fact that individuals may perceive and process information in very different ways. In considering the manner in which a learner perceives, interacts, and responds in a technology-based (or any other) learning environment, we can think about how an individual learns (cognitive), what motivates the learning (affective), and how they respond to their environment (physiological). 

There are several generally accepted learning style theories.  To present a brief overview, here are two of the more popular ones typically applied in education:
 
 

In addition to the identifiable diversity between learning styles, other factors such as ethnic backgrounds, technological savvy, past experiences, age and gender can create even more specific types of learners. Obviously, no teacher or designer can expect to develop ways to reach each individual student according to these criteria. All of this information suggests that we begin to think about using various modes of delivery and other methods to include all learners in a group. What is required is a way to scale up to everyone. What we can do is synthesize a multi-modal approach by implementing the proper learning theories that will scale up to the needs of many learners and appeal to the dominant and auxiliary learning modalities present in everyone. Teaching and learning methods can connect with all learning styles by using various combinations of experience, reflection, conceptualization, and experimentation. In a technology-based learning environment, a wide variety of experiential elements can be incorporated, such as audio, visuals, movement and narratives.

The theory of multiple intelligences was developed in 1983 by Dr. Howard Gardner, professor of education at Harvard University. He proposes eight different intelligences to account for a wider range of human potential in individuals. These intelligences are:

• Linguistic intelligence ("word smart"):
• Logical-mathematical intelligence ("number/reasoning smart")
• Spatial intelligence ("picture smart") 
• Bodily-Kinesthetic intelligence ("body smart")
• Musical intelligence ("music smart") 
• Interpersonal intelligence ("people smart")
• Intrapersonal intelligence ("self smart") 
• Naturalist intelligence ("nature smart")

One of the most remarkable features of the theory of multiple intelligences is how it can provide eight different potential pathways to learning. Here is a real-life application of the theory taken from a course at California State University, Hayward. The Professor sets the criteria for  student projects with the following:
 

We can recognize that divergent thinking and multiple perspectives are preferred to any single perspective on learning. What is evident is that there should be an explicit focus on individual meaning for the learner. One way to further consider this is to provide multiple opportunities for multiple users, so that the users can select that which is most adaptable to them as an individual, which may or may not always be in a social context.

There is also an implicit belief that neuroscience and knowledge about the brain will lead to significant improvements in learning theories. At present, these ‘brain-based’ theories offer us nothing more than a few implications for learning. For example, one such theory known as the modal directionality principle, suggests that the teaching of new information or structures should follow a right-brain to left-brain mode flow (experiential to analytical). This suggests that experiential forms of instruction belong to the initial learning stages and should move progressively towards a more formal, analytical style in the later stages. We already know that an individual can be left or right-brain dominant, so how would this work for everyone? For the time being, this type of research can offer little perspective on how we learn and gain understanding, but this may change in a few decades or so. As we continue to understand and map the brain and develop artificial intelligences based on our findings, we may one day be able to place an electronic device upon our heads to receive the wisdom of the ages, and forget all about learning theories. 
 
 

Moving from a teacher-centered to a learner-centered environment has many implications for teachers, including the fear that technology will someday replace them. This is not true, as planning and facilitating good constructivist learning activities may require more work and skill than traditional teaching.
 

INSTRUCTIVIST  TEACHER -CENTERED APPROACHES CONSTRUCTIVIST LEARNER-CENTERED APPROACHES Top down, didactic teaching Stimulus-response reinforcements Predetermined sequenced steps Focus on individual achievement Delayed gratification Focus on linguistic and logical-mathematical intelligence Clear objectives matched with assessment criteria Work sheets, mastery activities and (standardized) tests   Problem solving, sharing, exploration and discovery Situated activities in context Focus on collaboration and group work Emphasis on significance and meaningfulness Instant gratification Focus on multiple intelligences Various learning and assessment criteria  Student constructed projects (multimedia, descriptive narratives, etc.)

What we really seem to be talking about is engaging learners in learning, so that it becomes meaningful, understood, shared, deeply processed, retained, built-upon and of course used. We can realize that exploration and discovery learning can help learners apply knowledge, develop higher-order relationships and even proficiency as independent learners, but we can also recognize that there may be a time and a place to use more procedural knowledge building strategies. Discovering how to integrate the appropriate behavioural, cognitive and motivational factors with scaffolding, anchored support or other techniques is the challenge. One approach may not be superior to another, as they may relate differently to specific learning goals and/or cultures. It may be important to note in all of this that even nature with all its complexities observes the rule of simplicity, in that nothing in our natural world is more complex than it has to be to fulfill a particular function or purpose (Patterson, 1979). 

We can now see how technology-based learning activities and environments can be underpinned by learning theory. In the next module, we will learn how vehicles such as networked asynchronous discussion, hypertext, hypermedia, agent-learner or learner-learner narratives, virtual reality, non-linear access and multimedia can facilitate more active, social and/or personalized learning opportunities.

Try the self test for this module.

Learning Theory and Research
Links concerning cognitive science, constructivism, experiential education, human computer interaction, learning theory and neuropsychology.

Cognitive ergonomy and co-operative work
Waern, Y. (1998) 

Learning to Learn
Thinking and Learning Skills is a free ongoing, online course, teaching learners a variety of learning and thinking skills through a large collection of mind expanding exercises and interactivity. 

Learning Styles
An index of Web sites that suggest ways of recognizing a variety of learning styles and making use of them in teaching and learning.

1. Answer the following for personal reflection only:

a)  Which category or theory do the majority of your teaching practices fall into, and how can you determine this?

b)  Did this module change the way you think about your teaching practices? If so, how?

c)  How are computers presently used in the courses that you teach?
 

Armstrong, Thomas. Multiple Intelligences in the Classroom. Alexandria, VA: Association for Supervision and Curriculum Development, 1994

Arrowsmith Young, B., Danesi, M. (2000). Studying how the Brain Learns: Are there any Useful Implications for Instruction?, Research Paper, University of Toronto, Canada, http://learn.utoronto.ca/scs/InterconnectionsNewsletter.pdf

Briggs-Myers, Isabel and McCaulley, Mary H. (1985). A Guide to the Development and Use of the Myers Briggs Type Indicator. Consulting Psychologists Press.

Cobb, P., and Bowers, J. (1999) Cognitive and Situated Learning Perspectives in Theory and Practice.Educational Researcher Vol 28 (2), 4 - 15.

Crews, T., Biswas, G., Goldman, S. & Bransford, J. (1997) Anchored Interactive Learning Environments. International Journal of Artificial Intelligence in Education.  Vol. 8, pp 142-178.

de Jong, T., & Ferguson-Hessler, M. (1996). Types and qualities of knowledge. Educational Psychologist, 31(2), 105-113.

Greeno, J., Collins, A., & Resnick, L. (1996). Cognition and learning. In D. Berliner & R. Calfee (Eds.), Handbook of Educational Psychology (pp. 15-46).  New York: Simon &  Schuster Macmillan.

Hannafin, M., & Land, S. (1997). The foundations and assumptions of technology-enhanced student centered learning environments. Instructional Science, 25(3), 167-202.

Koschmann, T. (1996). Paradigm shifts and instructional technology: an introduction. In T. Koschmann (Ed.), CSCL: theory and practice of an emerging paradigm (pp. 1-23). Mahwah New Jersey: Lawrence Erlbaum Associates.

Laurillard, D., Stratfold, M., Luckin, R., Plowman, L. & Taylor, J. (2000) Affordances for Learning in a Non-Linear Narrative Medium. Journal of Interactive Media in Education, 2000, (2). http://www-jime.open.ac.uk/00/2

Langer, E.J. (1997) The Power of Mindful Learning.  Addison-Wesley: Reading, MA.

McConell (1994) Implementing Computer Supported Cooperative Learning, Kogan Page, 0.7494.0946.0

Ohlsson, S. (1995). Learning to do and learning to understand: a lesson and a challenge for cognitive modelling. In P. Reimann & H. Spada (Eds.) Learning in humans and machines: towards an interdisciplinary learning science (pp. 37-62). London: Pergamon.

Patterson, Freeman (1997). Photography and the Art of Seeing, Van Nostrand Reinhold Ltd., Toronto, Canada, ISBN: 0-442-29780-7 bd.

Shuell, T. (1992). Designing instructional computing systems for meaningful learning. In M. Jones & P. Winne (Eds.), Adaptive Learning Environments. New York: Springer Verlag.

Soloman, B. S.(1992) Inventory of learning styles. [Available at: http://www2.ncsu.edu/unity/lockers/users/f/felder/public/ILSpage.html.]

Spiro, R. J., Feltovich, R. J., Jacobson, M. J., & Coulson, R. L. (1992) Cognitive flexibility, constructivism, and hypertext: Random access instruction for advanced knowledge acquisition in ill-structured domains. In T. M. Duffy & D. H. Jonassen (Eds.) Constructivism and the technology of instruction, Hillsdale, NJ: Lawrence Erlbaum.

Wærn, Y. (1998). Cognitive ergonomy and co-operative work.
http://www.tema.liu.se/people/yvowa/Cooperg.htm 

Vygotsky, L.S. (1978). Mind in Society: The development of higher psychological processes. Cambridge, MA: Harvard University Press.