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Abstract

Although significant advances have been made in our understanding of the neural basis of learning and memory over the past hundred years, the translation of these neuroscientific insights into classroom teaching practice has been very limited. In this review, we discuss the historical development of pedagogy, cognitive psychology, and the neuroscience of learning over the past century, and how these separate disciplines are now combining in the new field of educational neuroscience. We examine the successes and promises of this emerging field, but also discuss the considerable practical and conceptual difficulties that face educational neuroscientists who have to be constantly vigilant to “mind the gap.”

Keywords

Bayesian theory cognitive load cognitive psychology education functional neuroimaging neuroscience reinforcement learning science of learning spacing effect testing effect

Learning begins at birth and is a life-long process. During the school age years, what is learnt in the classroom profoundly influences later outcomes in life, and is essential for maintaining a successful and growing economy in the modern world. In the present era of globalization and rapid technological advance, the need for optimal and efficient acquisition of knowledge and skills by school students has never been greater. Increasing automation and the widespread use of artificial intelligence and expert systems are expected to lead to the disappearance of many routine, administrative and technical jobs in industrialized countries (West, 2015): job requirements in the future are thus likely to involve higher levels of knowledge and skill than at present, placing ever greater learning demands and expectations on students. These issues have been well recognized by governments world-wide, resulting in ever increasing funding for education with the long-term goal of improving and sustaining better learning outcomes for all students (OECD, 2014).

Academic interest in education and teaching methods has a long history, stretching back at least as far as Socrates, who advocated open critical inquiry and debate as the best means of teaching and acquiring knowledge (Plato, c. 385 BCE; Vlastos, 1983). Consistent with these philosophical origins, educational research has continued to be profoundly influenced by philosophical and political trends within society. Modern educational research began with the foundation of education departments in American universities at the end of the nineteenth century and drew on the work of such figures as the philosopher John Dewey (1899), and the psychologist, Edward Thorndike (1910). However, research into education has been beset by a number of conceptual and practical difficulties since these origins. Kaestle (1993) opened a review of the field with the question “why is the reputation of education research so awful?”, concluding that it is perceived as irrelevant, politicized and in constant disarray. A decade later, Burkhardt and Schoenfeld (2003) suggested that the reputation of education research may have become even worse, asserting that ‘education research does not have much credibility—even among its intended clients, teachers and administrators.” Vinovskis (2000) concurred with this assessment, commenting that “much of the quality of research and development produced by education researchers is regarded by academics in other behavioral and social science disciplines as second-rate methodologically and conceptually.” Hamilton (2002), in an historical review of education research in the UK, concluded that “the ‘vagaries’, caveats or contingencies that haunted educational research before 1952 (e.g., problems in inference, survey analysis and experimental design) were joined by a new set of procedural problems during the lifetime of the British Journal of Educational Studies (1952–2002).” McWilliam and Lee (2006) discussed the perceived problems of educational research from an Australian perspective, noting that a tendency to conduct qualitative rather than quantitative research, “largely driven by individual choice… and personal beliefs,” has hindered the incremental and systematic growth of knowledge databases. Lagemann (1997), in a wide-raging historical review of American educational research, concluded that “sustained agreement about the methods and focus of the field” has been precluded by “continuing contests among different groups, especially scholars of education, scholars in other fields and disciplines, school administrators and teachers.” A relative lack of progress in education research compared to other disciplines can be attributed to these inter-disciplinary conflicts (Lagemann, 1997). Lagemann (1997) ended her review with the hope that “new, more collegial patterns of cooperation can be nourished and sustained” since “that would seem to be an indispensable precondition for knowledge-based reform in education.”

In parallel with the development of pedagogical ideas in education, significant advances have been made over the last century in our understanding of the psychological and neurophysiological mechanisms involved in learning and memory formation. As the long-term outcomes of educational programs ultimately depend on learning and memory formation, an attractive approach to improving learning outcomes for students involves translating this basic psychological and neuroscientific knowledge into the classroom. In the last few decades, governments and universities have shown an increasing interest in this emerging field of educational neuroscience. The international “Brain and Learning” project at the OECD’s Centre for Educational Research and Innovation was started in 1999 (OECD, 2002); multi-institutional centres for educational neuroscience have been established in Cambridge (2005), London (2008), and Australia (2013); and educational neuroscience departments can be found in an increasing number of universities around the world, including Leiden, Harvard, New York, San Francisco, Wisconsin, and Alabama. This article will provide an overview of the different ways in which psychology, neurophysiology, and education have approached learning, and how they are now combining to form the new discipline of educational neuroscience. Areas where progress has been made or appears promising will be examined, but the article will also look at the considerable challenges and difficulties, both practical and conceptual that have to be faced, and consider how these might be addressed.

Modern psychological investigation of learning began over a century ago with such pioneering figures as Ebbinghaus (1885), Thorndike (1905), Pavlov (1927), and Skinner (1938). The establishment of well-controlled, rigorous experimental paradigms, such as classical (Pavlovian) and operant conditioning, which can be applied to both human and nonhuman subjects, has allowed precise, quantitative descriptions of a wide rage of learning phenomena and greatly facilitated research into fundamental learning mechanisms. Psychological theories of learning have undoubtedly had a profound influence on educational practice: Thorndike himself spent nearly his whole career at Teachers’ College, Columbia University. Thorndike’s “Law of Effect,”—that associations are strengthened by a “satisfying state of affairs,” his “Law of Use” – that associations are strengthened by repetition, and his contention that learning is both incremental and automatic, have had a lasting influence on classroom teaching. However, some of the learning phenomena that have been discovered and described more recently, such as latent inhibition, overshadowing, the Kamin blocking effect, the partial reinforcement extinction effect (PREE) and various contextual effects on learning, are difficult to explain with simple Thorndikian or Pavlovian associative models, casting doubt on their general applicability. As a consequence, alternative learning models have been developed that have variously emphasized the importance of prediction error (Rescorla & Wagner, 1972), surprise-related changes in attention (Pearce & Hall, 1980), comparison of contextual and discrete cues (Stout & Miller, 2007), and stimulus rate estimation (Gallistel & Gibbon, 2000). A common theme in these more recent developments in learning theory is the importance of unpredictability or uncertainty in learning acquisition. To take one example, it has been demonstrated, in a wide variety of situations, that learning tends to be more rapid and persists for longer (e.g., the PREE) with novel or unpredictable stimuli (Courville, Daw, & Touretzky, 2006). In light of this, Bayesian information-theoretic approaches, which give a central importance to stimulus uncertainty, have become increasingly popular in modeling learning and other cognitive processes (Courville et al., 2006; Jacobs & Kruschke, 2011; Xu & Tenenbaum, 2007). For example, it has been reported that states of “confusion,” induced by uncertain situations, remarkably perhaps, leads to enhanced learning in laboratory tests of undergraduate students (D’Mello & Graesser, 2014), a finding that would lend itself to a Bayesian interpretation. To date however, learning models based on such findings from neuroscience and experimental psychology have had a limited influence on classroom practice (Clement & Lovat, 2012; Devonshire & Dommett, 2010). The theoretical and practical barriers to the introduction of neuroscientific findings into educational practice are discussed in detail by Devonshire and Dommett (2010) and Schrag (2011).

The field of cognitive psychology has increased our understanding of a range of phenomena related to learning, such as selective attention (Broadbent & Gregory, 1963; Treisman, 1969), separate forms of memory, for example, short-term, long-term, episodic, and semantic (Baddeley, 1984) and proactive and retroactive interference effects (Darby & Sloutsky, 2015; Robertson, 2012). Modern neuroimaging techniques such as fMRI and MEG have enabled the neural basis of these cognitive functions to be investigated noninvasively in human subjects. The importance of attentional variables, in particular perceptual load (Lavie, 2005) has been extensively investigated in school populations (Couperus, 2011; Matusz et al., 2015). However, while psychological concepts such as perceptual load have helped to provide a unifying approach to the design, analysis and measurement of classroom instructional practices there have been criticisms of the way the research has been conducted. In a review, De Jong (2010) highlighted a lack of conceptual clarity and methodological rigour in educational research on perceptual load and attention. Distinctions in the field between “germane,” “extrinsic,” and “intrinsic” load are often ill-defined or even circular: for example, if learners perform better then the load is considered to be “germane”, that is, not detrimental to performance (De Jong, 2010). Interestingly, De Jong suggested that the use of more objective, quantitative neuroscientific measures, perhaps from functional neuroimaging, may provide a solution to these problems. For example, Jaeggi and colleagues (2007) described distinct patterns of brain activity in high and low performers with respect to increases in cognitive load: a similar identification of distinct brain activity in relation to “germane,” “extrinsic,” and “intrinsic” load may help to remove the ambiguity and circularity in these concepts.

In the second half of the 20th century, great advances were made in our understanding of the electrical excitability of neurons and how neuronal activity is transmitted from cell to cell via synapses thanks to new investigative techniques (Del Castillo & Katz, 1954; Hodgkin & Huxley, 1952; Neher & Sakmann, 1975). Current neurophysiological research employs a wide array of techniques developed from these early pioneers (Kandel & Schwartz, 1982): electrophysiological recordings can be made of single or multiple neurons both in acute brain slice preparations (in vitro) (Dingledine, Dodd, & Kelly, 1980), and in awake behaving animals (in vivo)(Nicolelis, Ghazanfar, Faggin, Votaw, & Oliveira, 1997); recombinant DNA techniques allow the manipulation of genes coding for proteins critical for neural function, for example, neurotransmitter receptor molecules or cell membrane ion channels (Washbourne & McAllister, 2002); optogenetic techniques use light stimuli to obtain precise control of the activation or inhibition of selective populations of neurons in particular brain regions, allowing the testing of specific hypotheses (Deisseroth, 2015); and immunohistochemical staining techniques enable the identification of specific proteins involved in different neural processes or structures (Hökfelt, Johansson, & Goldstein, 1984).

Most neurophysiological models of learning and memory propose that changes in synaptic efficiency are critical for learning, an idea first proposed by Cajal (1894) and formalized by Donald Hebb (1949). The development of the new electrophysiological and molecular techniques described above has allowed this hypothesis to be investigated in unprecedented detail. In 1973, Bliss and Lomo described the phenomenon of long-term potentiation (LTP) in rabbit hippocampal neurons, in which the response of postsynaptic cells shows long-lasting enhancement following high frequency stimulation of presynaptic fibers. Subsequent research into LTP has shown it to have Hebbian characteristics and to be essential for memory formation (Sah, Westbrook, & Lüthi, 2008; Silva, Paylor, Wehner, & Tonegawa, 1992), Today, LTP is widely regarded as the most promising neurophysiological correlate of associative learning in the brain. Interestingly, LTP is known to be critically sensitive to the temporal characteristics of stimulus presentation. Scharf and colleagues (2002) demonstrated that spaced training is more effective than massed training in producing LTP in mouse hippocampal cells, and that this effect is dependent on protein synthesis. Similarly, Wu and colleagues (2001) showed that spaced stimuli but not massed stimuli produce morphological changes in the dendrites of rat hippocampal neurons. More recent research has provided further details of how spacing of stimuli produces distinct, learning-related molecular changes in the brain (Aziz et al., 2014; Kramár et al., 2012; Naqib, Farah, Pack, & Sossin, 2011; Naqib, Sossin & Farah, 2012).

The neuroscientific findings regarding LTP and spaced stimuli are intriguing in the light of the psychological spaced learning effect, first described by Ebbinghaus (1885). More than 300 published studies have replicated Ebbinghaus’s original finding that long term retention of information is enhanced by the temporal spacing of learning sessions, making the spaced or distributed learning effect one of the most widely studied and robust phenomena in the psychology of learning (Cepeda et al., 2009). Parallels between the molecular and cellular effects observed with spaced stimuli (e.g., enhanced LTP) and the enhanced mnemonic effects observed with distributed training sessions are receiving increasing attention from neuroscientists and psychologists (Kornmeier & Socic-Vasic, 2012). However, despite extensive laboratory evidence for the benefits of spaced learning, it has, until recently, received little attention from educationalists (Dempster, 1988; Seabrook, Brown, & Solity, 2005). This situation may now be changing. For example, Kelley and Whatson (2013) reported the results of a study in which a novel spaced learning protocol, explicitly designed in the light of LTP data from nonhuman subjects, was implemented in a normal classroom setting in children learning biology and physics as part of the national curriculum. Subjects in the spaced group, whose shorter learning sessions were distributed across the school day and separated by physical activities, had dramatically increased rates of learning compared to control groups who had more traditional “clustered” learning sessions (Kelley & Whatson, 2013). These results provide a good example of how findings from basic neuroscience can lead to new ways of thinking about education.

In addition to this cross-disciplinary academic interest, there has also been considerable public and media fascination in the neuroscience of learning and “brain-based” approaches to teaching (Blakemore & Frith, 2005). This public interest has been exploited by commercial organizations which have promoted “brain-based” teaching products that have only tenuous links to neuroscientific data and often with no evidence of any advantage in education. The “neuromyths” promoted by these commercial products include: the idea that individuals are either “right-brained” or “left-brained,” and therefore require different teaching approaches; the proposal that each child has a different “brain-based” visual, auditory, or kinaesthetic learning style for which teaching should be tailored; and the idea that there are multiple well defined “critical periods” during which specific skills must be taught and acquired (Dekker, Lee, Howard-Jones, & Jolles, 2012; Howard-Jones, 2012). Unfortunately, the enthusiasm for brain-based approaches to learning led to the widespread adoption of “neuromyth”-related methods by teachers and schools (Dekker et al., 2012). Partly in reaction to this misuse of neuroscience, a number of authors have expressed profound scepticism about the attempts to link neuroscience and education directly, asserting that it is a “bridge too far” (Bruer, 1997; Cubelli, 2009). According to these critics, neuroscience and education are currently too far removed from each other in goals and methodologies to allow meaningful and productive collaboration and that attempts to bring them together are often misguided (Bruer, 1997; Cubelli, 2009; Willingham, 2009). Even those who have been enthusiastic to bring neuroscientific insights to educational practice have acknowledged the gulf that exists between the two disciplines and advocated a role for separate intermediary communicators and interpreters (Goswami, 2006) or “neuroeducators” and “education engineers” (Fischer, 2009) to bridge the gap and thus facilitate cross-disciplinary understanding.

Another attempt to “bridge the gap” between neuroscience and education is exemplified by the establishment of cross-disciplinary centres, such as the Australian Science of Learning Centre (2013), which brings together neuroscientists, cognitive psychologists, educationalists and teachers to work on common research projects. Rather than have intermediaries or “education engineers” performing a post hoc translation from neuroscience research to education, the aim of the Science of Learning Centre is to foster a more radical “bottom-up” integration of disciplines. This is facilitated by resources such as an experimental educational neuroscience classroom, where teachers and students can both be monitored during lessons with an array of psychophysical and neurophysiological recording equipment, for example, eye trackers, heart rate and skin conductance measuring devices and EEG. Such an integrative, two-way approach, in which neuroscientists and educators are enabled to become expert in each other’s fields has been advocated by several authors as a way of achieving progress in educational neuroscience and avoiding the misinterpretations and pitfalls of “neuromyths” (Ansari & Coch, 2006; Mason, 2009).

An integrative approach not only allows educational and psychological protocols to be designed with a view to the neurophysiological variables of interest, but also allows neuroscientific experiments to be designed in the light of relevant psychological and educational behavioural parameters. For example, the psychological spacing effect, described above, may not only help to design educational programs to enhance rates of classroom learning but may also help to inform the design of experiments investigating the role of LTP in the brain. Another example is the testing effect, a learning phenomenon derived from educational research, in which memory retention is enhanced by multiple testing sessions during learning (Karpicke & Roediger, 2008; Rawson & Dunlosky, 2012; Roediger & Butler, 2011). The testing effect has a striking parallel in the psychological and neurophysiological phenomenon of reconsolidation, in which associative memory can be enhanced (or degraded) by the unpredictable presentation of a cue or conditioned stimulus without reinforcement, which appears to return the memory trace to a labile state (Lee, 2008; Pedreira, Pérez-Cuesta, & Maldonado, 2004). Optimizing the testing effect in the classroom could depend on a better understanding of the neural reconsolidation process occurring as a result of repeated testing; on the other hand, understanding more accurately the conditions and timing that produce the behavioural testing effect may help to design experiments that reveal more detail about reconsolidation in the brain.

Despite continuing scepticism and the persistence of “neuromyths” in education, the effort to form an integrated, scientifically rigorous discipline of educational neuroscience continues to grow. One area of undoubted progress is the neuroscientific insights that have been provided to specific learning disorders, such as dyslexia (Shaywitz et al., 2002, 2003, 2004; Temple et al., 2003) and dyscalculia (Butterworth, Varma, & Laurillard, 2011; Isaacs, Edmonds, Lucas, & Gadian, 2001; Molko et al., 2003; Simos et al., 2002). Functional neuroimaging and other measures of brain function, for example, event-related potentials (ERPs), by identifying specific brain regions and patterns of activity associated with learning disorders, can not only resolve disputes over the psychological components involved, for example, phonological versus visual in dyslexia, but also offer the real prospect of early diagnosis, classification, treatment, and monitoring (Butterworth et al., 2011; Gabrielli, 2011; Maurer et al., 2009; Shaywitz et al., 2004). This deepening understanding of how reading and mathematical abilities are implemented in the brain promises to assist not only individuals with specific learning difficulties but all other students. The availability of new techniques for investigating learning, both at the neuronal level (e.g., optogenetics and gene manipulation) and the individual and social level (e.g., experimental classrooms with neurophysiological monitoring) combined with new conceptual and theoretical approaches to learning (e.g., Bayesian models) encourages cautious optimism that a new integrated science of learning is achievable. There is, moreover, a growing realization that attempts to produce multilevel, integrated accounts of learning phenomena such as the spacing effect, the testing effect and the partial reinforcement extinction effect can be of benefit not only in improving learning outcomes in the classroom but in understanding the significance of learning-related cellular and molecular processes in the brain. The recent establishment of educational neuroscience centres and laboratories, bringing together neuroscientists, psychologists, teachers, educationalists and government education departments may be the beginning of the fulfilment of this hope.

Footnotes

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

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Neuroscience and education: Mind the gap

Abstract

Keywords

Footnotes

Declaration of Conflicting Interests

Funding

References