Introduction

One of the most divisive issues within education concerns the role of digital technologies in supporting the teaching and learning. There is no doubt that online technologies have unique properties that have the potential to support learning (see Garrison & Anderson, 2003). It could be argued that the greatest affordance of the web, for example, is that it has created a myriad of ways to communicate and interact with others, including family, friends and peers. Anderson (2004) suggests that these interactions are formed through a range of online tools including wikis, blogs, social networking sites, podcasts, and syndicated content.

There is some support showing that digital technologies may have a sustained effect on promoting an individual’s cognitive understanding, through enhancing their thinking, learning and problem-solving skills. However, despite quite compelling evidence of strong positive attitudinal and motivational changes to learning (Hativa, 1989; Underwood & Dillon, 2011) many studies have failed to show any benefits of using these digital technologies. This lack of supporting evidence of the effectiveness of these costly tools can be taken at face value but many would argue that it is the nature of the questions that researchers are asking that has resulted in these disappointing data (Underwood & Dillon, 2004; Jenkinson, 2009). There is a need to move beyond measuring the mechanistic or procedural aspects of how these technologies are used to facilitate learning so that we can comprehend how such technologies are affecting the cognitive, behavioural and effectual aspects of the learning process. Our focus on the technology alone has often neglected the role of the learner. As Jonassen, Howland, et al. (2003) assert, technology should be seen as a vehicle to learn rather than simply a vehicle to teach and therefore a greater awareness of the learning process is required before we may see any notable changes in the use of technology for educational gains.

So how can technology provide educational benefits for the learning process? To address this question, it is necessary to refer to psychological theory around learning and instruction. Such approaches offer a rather valuable insight into addressing some of the key issues highlighted in the previous chapter given that they have a specific central focus on understanding the learner and their learning process. The current chapter focuses neither on the affordances of the technology nor on the procedural aspects of implementing technology into the curriculum, but on the individual learning process and the skills and requisites for educational gains. It is only when we consider these psychological approaches, and the importance of the biological, cognitive and social dimensions of learning that the benefits of technology for students’ learning can be recognized.

What is Learning?

One of the very real difficulties in establishing the what, the when and the how of learning is that there are so many, often competing, theories each with an entrenched group of supporters who reject other views of learning. As we pointed out in Chapter 1 translating learning theories into educational practice is not always easy. This is made even more difficult by the plethora of competing psychological theories, emphasizing either the biological, cognitive, behavioural or affective dimensions of learning.

Traditionally there has been a keen focus on the importance of the application of behaviourist theory to education and learning, which reinforces traditional drill-and-practice models of learning, particularly to help encourage the learning of key facts. The use of reward charts to celebrate or reinforce positive behaviour is an example of the application of behaviourist theory found in many mainstream classrooms. However, despite evidence of their success, there is widespread agreement that such an approach fails to embed creativity and deep-level engagement with the learning process itself. There is now an increased focus on recognizing the importance of learner’s active construction of knowledge through their engagement with their own learning process. Although, as an aside, we should not reject the accumulation of factual information as it often provides the building blocks for creative thought.

The current dominant theoretical position within education marries constructivism with situated action, and emanates from Gestalt psychology. It states that learning is an active and collaborative process whereby students engage in solving authentic real-world tasks (Bransford, Brown, & Cocking, 2000; Merrill, 1992). The constructivist metaphor focuses on the learners’ active participation involving their actions and thoughts within a particular context when solving any given problem. Current views of cognition have started to explore ‘the relationships between the person and the environment, and the conditions under which they can exert reciprocal influence’ (Bransford, et al., 2006, p. 28). The concept of ‘context’, ‘environment’ or ‘activity’ is divergently expressed within constructivist perspectives and this view of learning has usurped the ‘cognitivists’, and even the ‘behaviourists’ before them, to focus specifically on the role of authenticity and of active learning within educational contexts (Greeno, 1998).

However, these are not competing theories rather they represent an evolution from simply learning by association and extrinsic rewards to the more active construction of knowledge within the learning process (Mayer, 1983). It has a number of pedagogic implications, the most significant being the shift of focus from teaching per se to creating a meaningful learner-centred learning environment. At its most extreme, constructivists argue for minimal guidance allowing learners to discover or construct essential information for themselves and technology provides a fruitful tool to allow this active exploration to occur. However, there is evidence that some level of guidance or direction is required by most learners and scaffolding this exploration is important especially within the context of formal learning (see Kirschner, Sweller, & Clark, 2006).

It could be argued that focussing solely on the cognitive solutions provides only a partial answer to our understanding of the learning process and the biological and social dimensions to the construction of knowledge cannot be ignored or simply overlooked. For example, although Caine and Caine (1990) argue that all learning is physiological, they recognize the affective aspects of learning. Learners need to be engaged with the learning process to recognize the potential gains that can be provided. They assert that meaning, and hence learning, occur through patterning and that emotions are critical to that patterning. The importance of practice also has biological roots as those neurons that repeatedly fire allow for the strengthening of neural connections (Zull, 2002). In his own work, Zull makes a compelling argument in support of the role of biology and emotion within the learning process. Chemicals, including adrenaline, serotonin and dopamine, which are often released during the act of learning, lead to changes in the neural networks. Neuroscience, then, confirms that practice is central to successful learning especially when that practise occurs in a meaningful way and when the learner is engaged with the task in hand. According to Zull the art of teaching is to find ways that allow learning to become intrinsically rewarding as exhibited in his model of the four pillars of learning, each of which promotes brain activity and so collectively exercises the whole brain as individuals gather, analyse, create and act on new sources of information (Table 2.1).

The perception of learning as a brain exercise rather than as pure knowledge acquisition is gaining currency outside education, not least because there is a need to maintain the mental wellbeing of an ageing population (Doige, 2007). Brain training is seen as one way to slow down and even reduce the inevitable decline in cognitive functions of the ‘silver’ generation and results from the recognition that the brain is not immutable and that environmental influences are capable of altering or rewiring the neural connections within brain structures. This has led both researchers and educators to question the capacity of the brain to respond to enrichment for learners of all ages. Some of the most vivid accounts of the susceptibility of the brain to training, that is its plasticity, occur in Doige’s descriptions of individuals with brain damage. These individuals, who have lost functionality in, for example, perception or motor control, have been shown to recover a certain level of functionality as activity is routed through non-standard, rather than the damaged standard, pathways in the neural network. Diamond (2001) points out that ascertaining the nature of what constitutes ‘enrichment’ for humans is often quite a difficult and complex task given the importance of individual biological and environmental differences. However, what evidence there is confirms the basic finding that dendritic growth in response to environmental stimulation correlates with learning, suggesting that newness and challenge are as important for the development of the human cortex. As Diamond notes, enrichment effects on the brain have consequences for behaviour, and she argues that parents, educators and policymakers can all benefit from such knowledge.

There is also growing evidence for the role of neuroscience in education, particularly in regard to promoting brain activity as a learning process that emphasizes the biological foundations of learning (Small & Vorgan, 2009). There is a large body of scientific research, for instance, documenting the effectiveness of neuro-feedback for ADHD and many areas of psychological or neuro-developmental difficulty (Knox & Anderson-Inman, 2001). In partnership with NASA, ‘SmartBrain Technologies’ has created a number of interactive games, including a non-violent driving game that improves visual tracking skills, hand-eye coordination, planning, concentration, memory and patience. Orlandi and Greco (2004) tested the impact of playing this driving game on boys aged 9–11 years who had a primary diagnosis of ADHD. The results showed that the non-game playing group experienced a 47 per cent study dropout rate from clinical support. However, the experimental group had only 6 per cent study dropout rate and showed a number of positive behaviour changes. Such studies add further credibility to the argument that changing brain structures can have a profound and pervasive impact on learning both within and outside formal educational settings.

Beyond General Theories of Learning

Psychological theories of learning have therefore focused on emphasizing, to various degrees, the biological, cognitive and affective dimensions of the learning process. Alongside general theories of learning there are also a number of theories closely associated with the engagement with technology. Mayer and Moreno (1998, 2002) for instance have applied the Dual Coding Theory from cognitive psychology (Paivio, 1986) to multimodal learning in digital environments. The basic premise of the Dual Coding Theory is that cognition involves two subsystems: a verbal subsystem to process language and a non-verbal imagery subsystem to process non-linguistic information. The theory assumes that visual and auditory information is processed via different verbal and visual systems that can be activated independently but are also connected, thus allowing dual pathways: this permits more efficient coding of information but is limited by the capacity of each pathway. The view that there is limited capacity overlaps with Sweller’s (1988) Cognitive Load Theory, which states that a learner’s attention and working memory is limited. According to this cognitive load theory, if the amount of new information is too great and places additional constraints on working memory, then learning is likely to be impeded. Coupled to this model of processing, Mayer (2005) argues uncontroversially that learning is an active process, which supports both the cognitivist and constructivist descriptions of learning.

Cognitive load theory has been used to explain the superiority of collaborative learning compared to individuals acting alone when working on highly complex but not low-complex tasks (Kirschner, Paas, & Kirschner, 2009). The authors argue that collaboration circumvents the individual’s working memory limitations by creating an expanded cognitive capacity and by distributing the cognitive load among group members. Although there is an increased work associated with the necessary organization of the group and the recombining of individual disparate pieces of information, the costs are minimal compared to the gain achieved by this division of labour if the task is complex. For low-complexity tasks, however, an individual can adequately carry out the required processing activities, but the costs of recombination and coordination are relatively more substantial.

The third theory finding currency in the online learning community is Flow Theory (Csikszentmihalyi & Csikszentmihalyi, 1975). Flow is described as a mental state that occurs when an individual is fully immersed in an activity. Flow experiences are intrinsically rewarding as they require intense involvement, focused attention, clarity of goals that lead to a lack of self-consciousness, and a feeling of full control over the activity. Athletes often describe this state of consciousness as ‘being in the zone’. A state where self and task merge results in the individual being intrinsically motivated to repeat the activity that is now deemed to be worth doing for its own sake. Flow Theory has also been widely used to explain the feeling of telepresence in the virtual environments, that is, the state of consciousness that gives the impression of being physically present in a mediated world. This theory has been extensively used to explain the nature of online social interactions and the lure of video games (Kiili, 2007; Weibel, Wissmath, et al., 2008).

What About the Quality of Learning?

The preparation of young people for life, leisure and work in a rapidly changing world is a concern for parents, educational practitioners and, of course, young people themselves. The view that the learner is an active participant rather than a receptacle for knowledge is coupled with the concept of deep learning. Entwistle (2000) defined a deep learner as one who is able to:

• relate new knowledge to previous knowledge
• use theoretical ideas in everyday experience
• distinguish evidence and argument
• organize and structure content into a coherent whole
• relate knowledge from different sources
• is self-motivated.

According to Entwistle, these are attributes that are highly desirable as they describe the flexible and independent learner who will succeed in a changing society. By contrast, surface learning tends to be superficial and the learners themselves tend not to grasp the point of the learning as an act within itself. It is all about passing the test. However, we should be wary of classifying any student as a ‘deep’ or ‘surface’ learner as one person may use both approaches at different times depending on the context of their learning. For example, disciplines with high content knowledge such as history lend themselves to surface learning, while mathematics requires deep learning; although to advance as an historian, deep learning is also essential. There are also cultural differences in the value placed on these two types of learning with Chinese students’ associating memorization with understanding and academic performance. Deep learning has been shown to correlate with intrinsic motivation, and surface learning with extrinsic motivation. Once again we should be wary of thinking the terms are interchangeable as any person can adopt either approach at any time. It is important to consider how these individual characteristics influence the process of learning and how technology can engage and promote an authentic form of active, deep learning.

Active Versus Passive Learning

A key focus in the literature concerns the distinction between active and passive learning and has strong foundations within cognitive psychology. The active-learning hypothesis predicts that learning from interactive systems increases learning by engaging individuals more closely with the material. On the other hand, the passive-learning hypothesis predicts that learning from interactive systems has no special effect on learning since the information content is no different from that contained in a non-interactive system. The active-learning hypothesis derives from constructivist models of learning (Jonassen, 1992; Mayer, 2005). Under the constructivist model, the learning process involves learners constructing their own individual knowledge of a subject on the basis of their prior knowledge and new information that they receive. When they learn, students play an active role in receiving and processing information. When required to interact with a learning system, learners have to make decisions about when to receive information (e.g., by button-clicking), and what information they receive (e.g., by selecting from a number of options). They have an active relationship with the material. As a consequence the active-learning hypothesis predicts that learning should increase when learners use interactive as opposed to non-interactive multimedia systems. While for Drave (2000) the quality of interactivity is more important than content for the success of learning Sim, (1997) believes that the level of interactivity plays a crucial role in knowledge acquisition and the development and refinement of cognitive skills.

A number of principles have already been formulated for the design of multimedia learning systems to promote interactivity, which often consist of a combination of words and images (Mayer, 2005). These include the multimedia principle (using both words and pictures), the coherence principle (avoiding extraneous media), the modality principle (using narration rather than text), the spatial contiguity principle (placing words and pictures close together) and the temporal contiguity principle (presenting words and pictures at the same time). The empirical evidence for these principles is strong. The systems developed to establish these principles were generally non-interactive, that is, they required no input from the learner in the form of mouse-clicking or key-pressing in order to finish a lesson. Commonly the lessons consisted of uninterrupted narrated animations such as Mayer and Anderson’s (1991) 30-second narrated computer-based pump lesson and Mayer and Moreno’s (1998) continuous 140-second narrated computer-based lightning lesson.

From a cognitive perspective, the utility of incorporating interactivity in computer-based systems is that it allows the learner to influence the flow of information in terms of timing or content. For example, button-clicking can be used to allow the learner to indicate when they want the next portion of text to be displayed; and interactive multiple-choice questions can be used to provide meaningful feedback for self-assessment. Supporters of the constructivist model usually contrast it with the information or knowledge transfer model of learning (e.g., Mayer, 2005). The passive-learning hypothesis has its origins in this model. Under the information-transfer model, the learning process involves the transfer of information from subject experts (e.g., through lectures or textbooks) to learners. The role of the learner in this process is primarily as a passive recipient of knowledge, whose task is simply to store information to memory. What matters is the quality of the content to which they are exposed. As a consequence, the passive-learning hypothesis predicts that for two systems with the same multimedia material, the level of learning should be the same regardless of whether the systems are interactive or non-interactive. However, is not simply the level of interaction and the distinction between passive and active learning that remains crucial to the learning process. A further aspect is tailoring the educational experience to meet the individual learning styles of the student.

Preferred Learning Styles

The central principle of learning styles is that children learn in different ways. Enthusiasts of learning styles claim that everyone has a preferred style and it is possible to test children to determine their preferences and the success they have in storing, processing and retrieving information as part of this learning process.

Gardner’s (1993) influential theory of multiple intelligences takes a holistic and general approach to identifying the cognitive building blocks and views intelligence as an ability. This principle can be extended to recognize individual differences in student’s approaches to learning. Although logical and linguistic forms of intelligence are perhaps the most valued ways of thinking within education, these represent just two of the eight intelligences outlined in Gardner’s original theory. However, if we translate these intelligences into different modes of learning (or preferred learning styles) this offers a real potential to personalize the learning experience for all students and to tailor the teaching and learning activities to meet the preferred styles of individuals. While many educators would see the perceived benefits in such a personalized approach, in reality this is often difficult to implement given time constraints. This raises additional questions about the feasibility of such attempts and whether the pedagogy of teaching and learning should actually be closely aligned to the individual, and often preferred, style of the learner, especially given that individual learners’ rarely know how best to learn. It could be argued that a preferred style of learning may not be the most effective way of learning new information.

Within the literature, there is a clear suggestion that educators can not only identify individual learning styles but can also deliver pedagogy that tailors to these particular modes of learning (Pashler, McDaniel, et al., 2008). Perhaps one of the most influential and widely recognized approach is Kolb’s classification of learning styles, which focuses on experiential learning and identifies four distinct styles: diverging, assimilating, converging and accommodating. Each of these four styles places a slightly different emphasis on the actions and perceptions required for learning (Kolb & Kolb, 2012). According to Kolb’s theory, the impetus for the development of new concepts is provided by new experiences and each learning style represents a focus on concrete experience and abstract conceptualization. Diverging emphasizes an innovative and imaginative approach and requires observation rather than acting. Assimilating emphasizes the ability to reason inductively and draws on different observations to influence thinking. Both of these approaches focus on the reflective nature of learning. In contrast, there are more active approaches to learning. For example, converging emphasizes the practical application of ideas and problem-solving while accommodating emphasizes problem-solving through trial-and-error rather than reflection. This has led to a number of measurement instruments, both questionnaires and inventories that help teachers identify individual student’s preferred learning styles. For instance, Honey and Mumford (2000) developed a learning questionnaire that remains one of the most popular learning styles resources, identifying four discrete categories of learner: activists, reflectors, theorists and pragmatists. Activists learn best when confronted with new ideas; reflectors prefer to observe others and listen to several viewpoints; theorists learn by drawing on their existing knowledge to analyse complex situations; and pragmatists make progress by making clear links between work in the classroom and life outside it.

Perhaps a more widely recognized approach is the VAK theory (visual, auditory or kinaesthetic learners) often seen as an extension from neuro-linguistic programming models and offering a more simplistic differentiation between learning styles. There has been a profound lack of understanding especially in the use of VAK styles within the school context (Sharp, Bowker, & Byrne, 2008). Many school activities are not purely visual, auditory or kinaesthetic, but a mixture of all three. A learner may be reading a book (visual) while listening to instructions (auditory) and actively making written notes (kinaesthetic). However, there is a real emphasis on promoting teaching and instruction that targets all pupils with all styles of learning and part of this remains reliant on delivery rather than content.

Does meeting the preferred learning styles of students actually enhance their learning outcomes? The evidence is mixed at best although there is some partial recognition that new technologies can help to support personalized learning in some way. New technology affords both teachers and learners the opportunities to embrace new ways of working, particularly using a broad spectrum of applications such as: social networking sites (SNSs), e-communities, collaborative authoring, and information sharing to create material using a range of social networking tools such as YouTube (video sharing), Flickr (photo sharing) and Blogger (interactive online diary). It is perhaps easy to see how multimodality is addressed. Our own work, among others, illustrates the pedagogic potential of allowing greater flexibility and choice of medium from which learners can select, editing and producing material for classroom activities and assessment to offer greater opportunities for visual or kinaesthetic learning styles rather than a reliance on the purely written form (Fisher & Baird, 2005; Underwood, Baguley, et al., 2007, 2008).

However caution is needed. As Hargreaves and colleagues (2005) suggest, the evidence in support of promoting preferred learning styles for effective learning is, at best, highly variable and lacks scientific validity. Similarly, Kirschner and van Merriënboer (2013) talk about the myths or ‘urban legends’ around learning styles arguing that the effects of tailoring teaching methods to accommodate preferred learning styles lacks any rigorous scientific evidence. Identifying individual student learning styles is problematic in itself, and accommodating these preferred styles in teaching may not, in fact, lead to positive measurable outcomes. As they also acknowledge: a learning style that might be desirable in one situation might be undesirable in another situation due to the multifaceted nature of complex skills (Kirschner & van Merriënboer, 2013, p. 175). We need to be wary, especially given the lack of robust evidence to show that matching the instructional style to individual learning style improves learning outcomes.

What About the Learner?

While good pedagogic design and new tools for delivery, specifically through the means of technology, can provide a potentially effective learning environment, our own work (Underwood and Banyard, 2008) and that of Entwistle (2000) reminds us that learners have to take up those affordances. The use of interactive multimedia systems and digital technologies may, for example, appear engaging and appealing, especially among enthusiasts. The technology must also promote learning in some meaningful way for the individual. Entwistle describes three main approaches to learning each of which involves an intention and a process. These different approaches generally lead to a qualitatively different outcome. Students may adopt:

• A deep approach to learning, that is, work to understand the information for oneself (linked to intrinsic motivation). This brings into play the integration of ideas and uses evidence and logic in reaching conclusions.
• A surface approach where the learner’s aim is pass without too much effort or thinking. This may result in inappropriate attempts to rote learn or to follow procedures blindly.
• A strategic approach where the intention to do well and/or achieve personal goals depends on organizing studying, effort, concentration and monitoring studying.

While we might describe the deep approach as the gold standard of learning (the type of learning that produces an Einstein), a strategic approach can also be very effective – we might call it the silver-gilt medal perhaps? However, it is the case that we all despise surface learning or so we argue. In his essay on deep and surface learning Entwistle emphasizes the power of deep learning, a learning that can be encouraged through the use of technology; but, as the quote below shows, deep learning does not always deliver rewards.

A deep strategic approach to studying is generally related to high levels of academic achievement, but only where the assessment procedures emphasise and reward personal understanding. Otherwise, surface strategic approaches may well prove more adaptive.

(Entwistle, 2000, p. 4)

Indeed Entwistle (2000) found that although Chinese students are more prone to the use of rote memorization and are more passive and less interactive than most students, they achieve well academically. A small fly in the ointment, is that these so-called rote learners also have higher deep and strategic scores than their Western counterparts (Biggs & Watkins, 2001). Those who reject content for process should remember that we need material to process and that is the content. The deep learning comes from making new connections between old content, but connections are not possible in a knowledge vacuum. Entwistle is arguing that if we assess facts then a learning strategy that emphasizes deeper understanding may not provide higher grades. If, as many educators have argued, technology is best used as a support of deep learning, then surface-level assessments are unlikely to be able to tap into the growing skills and knowledge of the tech-savvy student.

What has been called the Chinese paradox is understandable from Ericsson’s theory of expertise (Ericsson & Smith, 1991). Ericsson downplays innate capacities and focuses on hard work and practice when discussing the acquisition expert performance. He argues that specific training and practice provide the opportunity for all individuals, regardless of perceived talent, to have the potential to achieve expert performance if they are sufficiently motivated to endure a significant amount of time engaged in intense deliberate practice. Ethnic Chinese, wherever they reside, show that motivation is crucial to success and as a consequence they consistently appear at the top of the academic performance league tables. But what of rote factual learning you cry! The more facts you have at your fingertips the more likely you are to be able to see new patterns in data, thus enabling the capacity to indulge in creative thought. The current political debate in the United Kingdom around the value of a more content-rich factual curriculum and the assertion that such a curriculum is detrimental to children’s development misses the point. Learners need both facts and problem–solving abilities; the former providing the building blocks on which the thinking tools operate.

Risks, Skills and Opportunities

Tailoring teaching and learning activities to meet the needs of individual students and focusing on the learning process (rather than the technology itself) can provide real opportunities for improving the overall educational learning experience. Technology may assist in supporting, developing and refining the learning process but only when digital resources are incorporated into the curriculum in an authentic and meaningful way. Technology can be valuable in applying those psychological theories of learning into educational contexts. For example, the promotion of cognitive strategies that facilitate student learning can be achieved with the effective use of multimedia instruction (Smith & Woody, 2000). Individuals can effectively control their own rate and style of learning when using multimedia and other digitally pervasive online learning environments. Moreover, the development of video games as educational tools can support learning styles in quite innovative ways. These are often based on a range of instructional designs focusing on the principles of cognitive psychology and exemplify how best to manage cognitive load and the flow of information for learners (see Kester, Kirschner, & van Merriënboer, 2005). Nonetheless, such opportunities are not without their risks and attempting to create a new curriculum that caters for all individuals and their various approaches to learning may be an unrealistic and rather complicated process.

Conclusions

In our quest to improve teaching and learning practices, we are often susceptible to the ‘Silver Bullet Syndrome’ believing that new technological advances will be an easy-fix for all individuals (Watson, 2012). However, despite strong attitudinal and motivational improvements in learning through the implementation of digital technology, it has been argued that focusing solely on the mechanistic aspects of the technology will provide an incomplete picture at best. As we have discussed there is a need to look at the processes involved in learning and to allow some discussion concerning the relative stability versus variability in student learning and its implications for instruction. When we consider the relative impact of cognitive neuroscience on our understanding of styles and abilities, we are fully able to explore what a deep approach to learning involves and how to ascertain a student-centred approach to teaching and learning. However, focusing on the individual and their own learning process is far from easy and raises a number of equally challenging issues (Goswami, 2006). Perhaps one of the greatest challenges facing education is how we engage individuals with the learning process and design and tailor instructional methods to sustain positive changes that are feasible, accessible and realistic.