Understanding Learning at the Cognitive Level 21

In this section, the human cognitive architecture is described in order to

understand the processes occurring in the human memory system that contribute to learning. Cognitive Load Theory (CLT) builds on the human cognitive

architecture model to demonstrate why some learning designs are more

effective than others, with reference to the limitation of the human cognitive architecture.

2.2.1 Human Cognitive Architecture and Schema Development

The process of learning involves gaining new knowledge and skills, and

assimilating them alongside existing knowledge and skills. This means that the brain needs both to store items and then reuse them in the right situation. Hence, it is worth knowing at the outset how the brain processes information in order to store and reuse stored information. Atkinson and Shiffrin [13] proposed a psychological model which describes the structure of the human memory. In the Atkinson-Shiffrin model, the human memory is divided into three sub-

components, which are sensory memory, working memory and long-term memory.

 Sensory memory is the shortest-term element of memory and retains the impressions of sensory information, which come from the human senses. It is considered to be outside of cognitive control and is instead an

automatic response, and is not considered here.

 Working memory is generally considered to have limited capacity. It has the ability to remember information over a brief period of time, but it is long enough to be used for further information processing, such as

internally repeating or refreshing the information in working memory, or then passing it on to the long-term memory, described below.

 Long-term memory is the element of memory in which associations among items are stored. It provides the lasting retention of information, from minutes to a lifetime and seems to have an almost limitless capacity. Sweller introduced the concept of a schema as a cognitive structure for storing information in long-term memory [14]. A schema is a particular structuring of information and experts in a discipline have structuring that enables highly effective retrieval of the stored information. They associate elements together so that the collection can be remembered as a single element. For example, the number 100 is recognized as a single element, not as three separate digits. The same principle underlies the mind-mapping revision technique [15] of condensing written notes repeatedly, using a tree structure of headings to remember a large body of related elements.

Sweller [16] also gave his definition of working memory, long-term memory and schemata from the angle of learning and proposed a natural information

processing system. Working memory is a structure that processes information coming from either the environment or long-term memory and that transfers learned information for storage in schemata into the long-term memory. Long- term memory has a massive capacity for information storage. It contains cognitive schemata that are used to store and organize knowledge by

incorporating multiple elements of information into a single element with a specific function.

Cowan proposed an integrated model of working memory in which some of the representations (schemata) held in working memory are an activated subset of the representations held in long-term memory. In Cowan's model, working memory was organized in two embedded levels. The first level, called activated memory, consists of an unlimited set of long-term memory representations that are activated. The second level, called the focus of attention, holds up to four items or chunks of information from the environment [17]. This second level is the central limitation of the working memory system.

In the problem solving domain, a schema represents a category of problems as a set of key characteristics of such problems and captures the variations that have been seen before. Each new problem belonging to this category can be stored under the same schema. Furthermore, the method for solving problems of this category is also stored with the schema[18].

With such schemata, problem solving is a process of recognizing the essential characteristics of the new problem, and then matching these against the characteristics held within each problem category schema. Cowan’s model indicates that an expert can hold the activated schemata of a very large number of previously seen problems in working memory alongside the relatively few key characteristics of the new problem, and therefore perform the necessary pattern matching, as all necessary data is in the working memory.

Studies of chess grandmasters [19] and also Soloway’s work on programming comprehension [20] back up this view by showing that experts solve problems better that novices when those problems follow well-known patterns, but not when they are random problems adhering to no commonly-seen pattern. It is this pattern-recognition based on acquired schema that makes for expert behaviours.

If the learning result is to alter the long-term memory, it is important to know how the learning process is achieved. Kirschner and Van Merrienboer [21] described learning in terms of four processes involving the creation and modification of schemata. This can be combined with Adaptive Control of Thought-Rational (ACT-R) framework [22] to give five stages in schemata development.

1. Prior to schema construction: many separate examples are seen and recorded in long-term memory independently. A new example is related to these stored examples in an attempt to recognize by analogy. Learners’ performance is slow, error prone and working memory load is high,

because the details of the new example must be compared with the details of every stored examples. For instance, the experiences of seeing many different animals are all stored separately, and when a horse is seen it is compared against all examples of animals.

2. Schema construction: repeatedly seeing related examples and recognising features common to all causes a new schema to be formed. For example, the numerous instances of different horses (e.g. with different sizes and colours and names) are stored under a schema for 'horse' with the

common characteristics of e.g. four long thin legs, long straight-haired tail, long neck and nose, long-haired mane, can be ridden, with hooves, and so on.

3. Schema assimilation: new elements of information are incorporated into existing schemata. For example, a child seeing a zebra for the first time and calling it a horse, associating it with the horse schema.

4. Schema elaboration: elements consisting of lower level schemata are combined into higher level schemata building increasing numbers of ever more complex schemata. The child takes into consideration the different properties of a zebra compared to a horse, perhaps calling a zebra a horse with stripes. In this case, the horse schema would have a sub-schema of horse with stripes. Of course, the child may choose other classifications, for example, wild animal, separating out common and different features of a zebra compared to the more generic wild animal.

5. Schema accommodation: existing schemata based upon recurring new information which are incongruous or inconsistent with existing schemata are adapted. When the child eventually learns the name of zebra, this information is accommodated, perhaps adjusting learning information about horses to now have a "horse-like" schema, with sub-schemata for horses and zebras.

Because a schema can be treated by working memory as a single element if a schema has become sufficiently automated after long and consistent practice, the limitations of working memory disappear for more knowledgeable learners

when dealing with previously learned information stored in the long-term memory.

2.2.2 Cognitive Load, Cognitive Overload, and Learning

Cognitive Load Theory (CLT) [14] builds on the notion of a limited working memory capacity and a vast long-term memory capacity as the model used for understanding the human cognitive architecture. It is a set of learning principles that deals with the optimal usage of the limited working memory. Sweller [14] and Cowan’s work [17] proposes that since working memory for new information is limited, if the complexity of instructional materials is not properly managed, this could result in cognitive overload for learners. Tuovinen and Sweller [23] and Pass et al. [24] further develop this theory and suggest the free exploration of a highly complex environment may generate a heavy working memory load which is harmful for learning. Especially it is not good for novice learners, due to their lack of proper schemata, which are needed for the integration of the new information with their prior knowledge. Novices, not possessing appropriate schemata, are not able to recognize and memorize particular problem

configurations. Sweller et al.[25] also found that cognitive overload can impair schema acquisition, later resulting in a lower performance.

CLT [16] distinguished three types of cognitive load that occur in working memory during learning, which are:

 Intrinsic Cognitive Load refers to the number of elements that must be present simultaneously in working memory for a concept that is to be learned to be understood. For a particular learning task, the relative numbers of new elements and elements drawn from long-term memory are dependent on the learner’s degree of prior experience (what is complex for a beginner is simple for an expert).

 Germane Cognitive Load results from active schema construction

processes and is the result of beneficial cognitive processes for learning. It is effective cognitive load. For example, explaining the material to oneself, or rehearsal from practice repetitions.

 Extraneous Cognitive Load is the result of instructional techniques that require learners to engage in working memory activities that are not

directly related to schema construction. It can be caused by an inappropriate presentation of the learning material or by requiring students to perform activities that are irrelevant to learning. It is ineffective cognitive load, from the point of view of learning. For example, visual search processes for information during learning.

Based on CLT, learning outcomes are optimized when cognitive load fully utilizes the capacity of working memory with elements that allow for optimal schema acquisition. Too little or too much cognitive load results in low learning outcome. Optimising learning is a question of balancing, not minimizing or maximizing cognitive load. However, a reduction of extraneous cognitive load frees working memory capacity to be used for germane learning processes. If intrinsic load is low, learning can be successful despite a high extraneous load, although the exercise may seem tedious or boring. The total amount of cognitive load required for a learning activity needs to remain within the working memory capacity.

Based on this review of human cognitive architecture and cognitive load theory, supporting novice students to develop schemata in an efficient way should be the target for instructional design. Problems cannot be solved without proper and sufficient schemata; students must see enough problems and solutions and build enough experience in order to develop generic schemata. However, how can students develop these generic schemata efficiently, and crucially, how can cognitive overload and consequent wasted effort be avoided? This leads to an exploration of apprenticeship models.