Final Synthesis Paper
On Cognitivist Approach to Online Learning
By Lydia Georgieva
Instructor: Diane Hall
Cognitivist Approach to Online Learning
The expanding use of digital devices provide educators with a variety of ways to improve the motivation and performance of learners. This is specifically applicable to e-learning and design of online courses. The research in the field of cognitive learning theory has provided a wide basis for understanding how learners perceive the world around them and how they learn. Cognitive load theory has taken this a step further by differentiating between the different types of cognitive load and explaining the role the cognitive load types play into the effectiveness of learning which is especially important in e-learning.
Researchers have explained the limitations of the short-term memory and the importance of this memory to not get overloaded when presented with new information. They have also created ways to measure the cognitive load of tasks and designed specific strategies to reduce the unimportant cognitive load from learning tasks and thus improve performance of learners and the efficiency of e-learning.
This paper synthesizes some of the research in the field, looking at the implications of cognitive learning theory and cognitive load theory for different type of aspects involved in online learning such as : interactivity of learning material, its effects on motivation, impact of different types of cognitive load on online discussion design and the design of other learning tasks. It also sums up some strategies suggested by researchers that can improve the efficiency of learning by application of cognitive load theory in instructional design.
The use of educational technology in learning is a very strong driving force in education today, be it in academic or business environments. More and more content and learning opportunities are offered to learners by all types of academic institutions with universities leading this development. Businesses also are expanding their training opportunities by the use of computer aided learning for job training. The expanding access to the Internet by computers and portable handheld devices in the classroom, at work, at home makes it easier to deliver educational content to learners. The effectiveness of these educational approaches will determine the achievement of the educational objectives. Researching the cognitive learning theory and applying it to learning via educational technology will help instructional designers meet individual learning requirements and utilize more fully the potential that educational technology has to offer. This paper will explore the current cognitivist learning theories and how these theories apply to online learning environments aimed to improve the learning outcomes.
The Cognitivist Approach to Learning
The focus of cognitive researchers has been on identifying mental processes, related to the internal and conscious representations of the world which they consider essential for human learning. Fontana (1981) sums up the cognitive approach to learning as an approach that ‘lays stress not only on the environment, but upon the way in which the individual interprets and tries to make sense of the environment.” According to Fontana (1981) this approach views the individual as ‘an active agent in the learning process and not as the somewhat mechanical product of his environment. The individual deliberately is trying to process and categorize the stream of information fed into him by the external world”. (Fontana, 1981, p.148)
The cognitive approach to learning covers a wide range of areas and research. Some cognitivists view the generic nature of basic mental processes as something that can be programmed or modified externally by factors such as new experiences. The concept of the mind as a computer was the focus of interest for early cognitivists and several technology-based developments in teaching resulted from this:
Other cognitivists that are closer to the constructivist school would argue that mental states and mental processes are not something that is fixed but are constantly evolving for seeking meanings that are developed by the individual (Bates).
Cognitive Load Theory (CLT)
Cognitive Load Theory was developed by John Sweller (Sweller, 1988). It centers around the assumption that memory is composed of a limited working memory and an unlimited long-term memory. CLT explored various ways to address the limitations of the working memory and thus promote more effective learning (Kirschner, 2002).
Sweller, (2004) determines that the working memory is with a very limited capacity when dealing with novel information, and states that “humans are conscious of and can monitor only the contents of working memory. All other cognitive functioning is hidden from view unless and until it can be brought into working memory “ (p.252). One of the key implications of a limited working memory is that it is easily overloaded if learners are presented with abundance of information and the capacity to carry out reasoning tasks will be exhausted.
Due to the limitations of working memory, theorists have pointed out to long-term memory as the place where permanent knowledge is being stored. Kirschener (2002) points out that “long-term memory is what you use to make sense and give meaning to what you are doing now. It is the repository for more permanent knowledge and skills and includes all things in memory that are not currently being used but are needed to understand. (p.2)”
Piaget (1952) talks about the existence of schema and defines a schema as “a cohesive, repeatable action sequence possessing component actions that are tightly interconnected and governed by a core meaning”. He called the schema “the basic building block of intelligent behavior- a way of organizing knowledge”. Thus ‘schema’ can be considered as a “unit” of knowledge related to a single aspect of the world.
Wadsworth (2004) points out that ‘schemata’(plural for ‘schema’) should be thought of as ‘index cards filed in the brain, each one telling an individual how to react to incoming stimuli or information”.
Piaget emphasized the significance of schemas in cognitive development and described how they were developed. According to him a schema is a set of linked mental representations of the world, which we use both for understanding and for responding to various situations. Then we store these representations and apply them when the need arises.
Ayres(2005) agrees that the knowledge stored in these cognitive schemas are what makes up human expertise. He believes that human expertise does not arise from an ability to engage in figuring out many new elements but comes through the construction of an increasing numbers of complex schemas. Expertise develops through the automation of some of those schemas, which automation happens when they are repeatedly and successfully applied. This automation can free the capacity of the working memory for other activities. Well-designed training programs encourage not only schema construction but also schema automation for those aspects of a task that are consistent across problem situations (van Merrienboer, Kirschner &Kester, 2003).
Three types of cognitive load
Cognitive schemas are stored in and retrieved from long-term memory (LTM) but the processing of novel information happens in working memory. Cognitive load theory (CLT) focuses on the ease with which this processing of novel information can be done. It looks at what can affect working memory load and determines that it can be affected by three types of cognitive load: intrinsic cognitive load (intrinsic nature of the learning tasks), extraneous cognitive load (the manner in which the tasks are presented) and germane cognitive load (the amount of cognitive resources that learners willingly invest in schema construction and automation).
Intrinsic cognitive load is created by the nature of interaction between the learning materials and the level of expertise of the learner. The number of elements that must be simultaneously processed in the working memory is its primary element. Materials with high element interactivity are difficult to understand and the only way is to develop schemas that incorporate the interacting elements (Ayres, 2005).
An example of intrinsic cognitive load has been given by Cerpa, Chandler, and Sweller (1995) and it is the learning of basic operations in a spreadsheet program. These operations consist of: selecting a cell or group of cells, entering data into a cell or modifying data that has already been entered and these are tasks with low complexity and low interactivity. Each operation can be learned independently of other tasks being referenced. But creating a formula requires that the learner is familiar with intersections of rows and columns and can manipulate them, that the learner knows that the elements of a formula and the operators(i.e., equals/=, add/+, subtract/-) must be learned and understood in conjunction of each other.
Extraneous cognitive load refers to processes that are not directly necessary for learning and can be changed by instructional interventions. This type of cognitive load may be triggered by the need for learners to search for information necessary to complete a learning task in instructional material or when learners use weak problem-solving skills. As working memory can be divided into partially independent visual and auditory working components (Penney, 1989), overloading either one of those may also increase the extraneous load. An example of such overload is when learners must search for the information needed to perform a learning task (e.g. searching for data needed in a cell somewhere else or determining what the value of a variable might be while the task is to learn how to use a spreadsheet). This process of searching does not directly contribute to learning and thus increases the extraneous cognitive load.
Germane cognitive load refers to processes that are directly relevant to learning, such as schema construction and automation. For instance, when learners encounter a variety of problem situations this encourages them to construct their own cognitive schemas. When the degree of variability is high this requires the thoughtful engagement of the learners and they apply more effort in genuine learning which increases the cognitive load. An example of this type of cognitive load is when learners connect new information to what is already known rather than focusing on the details of the task (e.g., when they connect the fact that the operator needed for a cell acts in a similar way to a different one but its special characteristics are different).
Cognitive load theorists argue that intrinsic, extraneous and germane cognitive load are additive (Paas et al.,2003) and the total load of the three should not exceed the capacity of the working memory. When the intrinsic cognitive load need to be high during instruction this will make it necessary to lower the extraneous cognitive load and vise versa, low intrinsic cognitive load may render the high extraneous cognitive load, resulting from inadequate instructional design, harmless for the learning process.
Cognitive Load Theory and E-learning
The advancement of online learning has made a shift in the focus of CLT. Complex learning tasks, characterized by a large number of interactive elements have led to the creating of many e-learning applications.
In e-learning achieving efficient learning does not mean that the element interactivity should be high. Researchers suggest that high element of interactivity may not allow for adequate learning and suggest that information should be gradually introduced and interactive elements should be added gradually instead of presenting all information at once.
The learners expertise should also be taken into account and the same learning materials adequately differentiated for low and high expertise learners in terms of the element interactivity.. Researchers have pointed out that instructional methods which work well for low-expertise learners may be ineffective or even have adverse effect when learners acquire more expertise. They often call this ‘expertise reversal effect” (Kalyuga, Ayres, Chandler, &Sweller, 2003). A good instructional strategy starts with the presentation of worked examples and builds up to independent problem solving skills.
The impact of expertise has been researched by Tracy Clarke, Paul Ayres, and John Sweller (2005). In their experiment students with low and high spreadsheet knowledge were taught mathematical skills using a spreadsheet application. The students with low prior knowledge achieved higher test results but the students with high prior knowledge showed a tendency toward the reverse effect. They concluded that presenting learning tasks from simple to complex is important only if the complex task represents a high level of element interactivity for the target group. If the combined task represents a low level of element interactivity for the target group such sequencing is not desirable.
Paas et al(2004) has researched the relationship between motivation, the investment of mental effort, and performance. Motivation is identified as an important factor that determines learning success and also the lack of it causes the high withdrawal rate among online learners..
Roxana Moreno and Fred Valdez researched the effect of interactivity in learning from unimodal and multimodal presentations in the domain of meteorology. They found out that multimodal presentations, with integrated, non-redundant words and pictures are superior to unimodal presentations. When students were asked to evaluate their actions before corrections this had positive effects on learning. They found out that in multimedia learning the positive effects are dependent on the mental interaction needed to actively involve the learner in the learning process.
As central element in e-learning are online discussions it is interesting to explore how the cognitive learning theory applies to this element. Researchers argue that the heavy cognitive load is the reason for the lack of quality of online discussions.
A study conducted by Darabi and Jin (2013) reveals that lack of higher level learning was a frequent problem when the online learners were provided with discussion prompts and were expected to demonstrate a good quality discussion indicating higher level learning. They speculated that the problem might originate from the heavy extraneous cognitive load imposed on learners by the lack of structure and instructional strategies. They argue that in an online course where the instructor assigns an ill-structured discussion task by providing just a discussion prompt and expecting learners to participate in the discussion, a great amount of extraneous cognitive load is imposed on the learners. Learners have to spend a great amount of their cognitive resources just to figure out the context, requirement, strategies and participation rules and as a result they post a simple response.
In view of that researchers developed four discussion strategies according to the principles of cognitive load theory to address the issue. The current practice in many educational online settings provides learners with a discussion topic, some participation rubric, and a discussion board that displays all the posts in threaded or timed format. Learners are expected to understand the discussion topic, read others’ posts, process related information and apply participation rules to contribute to the discussion. As a result, rather than using their cognitive capacity for handling the germane load of the learning task, the learners have to deal with extraneous activities to create the structure that has not been provided to them.
The four discussion strategies proposed by Darabi and Jin and based on CLT principles are:
The researchers measured two outcome variables: the quality of discussion and the strategies’ instructional efficiency. The results of the study partially confirmed the hypothesis that the CLT – based online discussion strategies reduce the cognitive load of the discussion task and enhance student discussion quality. Example-posting users and limited-number-of-posting users exhibited better performance. Also the participants who used the CLT-based strategies reported lower mental effort. Thus the researchers point out that effective instructional design may reduce cognitive load while keeping the performance level constant, or even learners may achieve better performance as a result of the cognitive load reduction. However, only two of the strategies seem to contribute to the discussion quality- example-posting and limited-number-of-posting strategies.
From this synthesis paper on some of the research of cognitive approach to online learning it is evident that CLT research has led to the development of a variety of instructional formats and strategies involved in e-learning and instructional design. It has enabled certain instructional design principles aimed to lower the extraneous cognitive load in online tasks and provide more space for developing the intrinsic and germane cognitive loads. It also has pointed out the importance of interactivity being too low or too high for the learners and has suggested strategies to overcome this so no adverse effects are experienced by learners.
The importance to investigate the motivational effects of various instructional methods has also been noted as worthy of further research by cognitivist researchers.
Cognitive Load Theory has provided guidelines to assist in presentation of information in a way that encourages learners to optimize their performance. The limitations of the learner’s working memory have been taken into account in this learning theory and used as a basis to guide instructional design in their efforts to create a motivational and effective learning environment.
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