Universal Design for Learning is based upon widely replicated findings in educational research. Principally, a student’s response to instruction is highly variable (Tomlinson et al., 2003). Universal Design for Learning is the culmination of research in the fields of cognitive psychology, neuroscience, and the learning sciences. Universal Design for Learning is grounded in concepts such as the zone of proximal development, scaffolding, modelling, and mentors (CAST, 2018). Developmental psychologist Lev Vygotsky emphasised the importance of graduated “scaffolds” for independent learner achievement and considered learners as individuals who learn distinctively (Vygotsky, 1979). The zone of proximal development describes the space between what a learner can do without assistance and what a learner can do with guidance or in collaboration with more capable peers (Vygotsky, 1979). This concept correlates with the Universal Design for Learning principles; engagement with the learning task, recognition of the information to be learned, application of strategies to process information (Rose and Meyer, 2002). For some time, educators, psychologists, and organisational theorists have pointed to similar three-state components to define learning in any organism. For example, Lev Vygotsky described in his 1962 book “Thought and language” the three prerequisites for learning:
- Engagement with the learning task
- Recognition of the information to be learned
- Application of strategies to process that information (Vygotsky, 2012).
For people to learn, they require agency and interest in the subject (Vygotsky, 2012). Benjamin Bloom’s taxonomy of educational objectives defines these components as cognitive, psychomotor, and affective domains (Bloom, 1956). In his work on business innovation, Clayton Christensen (2001) describes these components as values, knowledge, and processes (Christensen, 2001). Although different terminology, these three-part divisions are widely cited in educational theory and present complementary characteristics. Universal Design for Learning is grounded in established educational theory, but more recent scientific developments also influence the framework. Through advances in modern neuroscience and MRI scanning, it is now possible to examine learning through an additional lens of neuronal activity. Neuroscience is a multidisciplinary field that combines anatomy, physiology, biology, and computer science to model emergent properties of neural circuits within the brain (Alivisatos et al., 2013).
Neuroscientific research concerning Universal Design for Learning
Neuroscience considers the brain a complex mesh of integrated and overlapping networks (Sporns, 2013). Like computer networks, these multifaceted connections enable the individual components to communicate rapidly, flexibly, and along multiple pathways (Marzo et al., 2012). When confronted with a specific task, some networks are more engaged and active than others. These active areas “light up” as revealed in images from MRI tractography (Marzo et al., 2012). While thousands of networks for different functions have been identified, Universal Design for Learning emphasises the significance of the affective, recognition and strategic networks that can be seen to engage with specific tasks reliably and predictably in relation to discrete tasks and learning (Glass et al., 2013).
- Affective network: engagement with the learning task.
- Recognition network: recognition of the information to be learned.
- Strategic network: application of strategies to process information (Glass et al., 2013).
The affective, recognition and strategic neural networks correlate to three fundamental elements of learning: the recognition of patterns, the planning and generation of patterns, and the selection and prioritisation of patterns (Cytowic, 1996). A core tenant of Universal Design for Learning are these neural networks relationships to learning, which will be explored in more detail.
Provide multiple means of engagement (The affective network).
The affective network is crucial to learning, and learners differ wildly in how they can be engaged or motivated to learn. An individual’s variation in affect comes from various sources: personal relevance, culture, neurology, subjectivity, and existing knowledge (Glass et al., 2013). A proportion of students will respond positively to spontaneity and novelty, while others will prefer strict routines (Rose, 2000). Individuals also vary in their preference for group work with peers (Stormshak et al., 1999). There is not one means of engagement that will be ideal for all learners in practice. Providing multiple options for engagement acknowledges learner variability and optimises for engagement (Rose, 2000). Individual choice and autonomy need to be considered carefully within instructional settings. It is not always appropriate to provide the choice of learning objectives, but choice and independence can be promoted regarding how learning objectives are achieved. Providing choice and autonomy can develop self-determination in students, pride in accomplishment, and increase the degree to which students feel connected to their learning (Rose, 2000). Individuals also differ in the frequency of choice and what kind of choices they prefer. Simply providing choice is not enough; the correct type of choice and level of independence should be considered to encourage engagement (Rose & Meyer, 2006). Highly regarded studies on “Intrinsic motivation and the process of learning” (Cordova & Lepper, 1996) and “The effects of choice in task materials” (Amabile & Gitomer, 1984) highlighted the benefits of providing students with choices. These studies demonstrated increased student motivation and engagement when presented with options relating to materials, tools, content, and formats. Other studies, such as “Choice is good, but relevance is excellent” (Assor et al., 2002) and “The support of autonomy and the control of behaviour” (Deci & Ryan, 1987), highlighted autonomy and control as mechanisms to promote ownership of individual learning experiences.
Provide multiple means of representation (The recognition network).
Individuals differ in the ways that they perceive and comprehend information that is presented to them. Alternative approaches need to be considered for individuals with sensory disabilities (e.g., blindness or deafness), but cultural differences may also require different approaches to curriculum content (Felder et al., 2005). Providing multiple means of representation offers more opportunities for students to make connections within and between concepts (Kameenui et al., 1998). No one means of representation will be optimal for all learners. Providing multiple means of representation increases the likelihood that students will be able to successfully perceive and comprehend content (Glass et al., 2013).
Provide multiple means of action and expression (The strategic network).
Learners vary in their ability to navigate learning environments and express what they know (Rose, 2000). Individuals with significant physical impairments (e.g., cerebral palsy) or cognitive disabilities (e.g., executive function disorders) may have to approach learning objectives very differently. Some individuals may express themselves clearly in written text, but not through speech, or vice versa (Rose, 2000). Providing students with appropriate action and expression requires strategy, practice, and organisation (Spooner et al., 2007). Promoting multiple means of action and expression affords students more opportunities to express what they know (Roth et al., 1997). Critics of Universal Design for Learning’s references to neuroscientific evidence suggest that focusing on three specific networks, rather than tens or hundreds of more differentiated models of networks, is a gross simplification of learning. The Center for Applied Special Technology (CAST) openly acknowledge this limitation and respond to this criticism by stating – “We have chosen to focus on three classes of networks because this is the most basic way to partition the learning brain. It is possible to take a more complex brain model, recognising many different networks or functions. However, it is impossible to take a simpler model that would still reveal the fundamental foundations of learning” (CAST, 2014:33). While much is still unknown about the brain’s complex nature, empirical evidence begins to shed light on how learning intersects with effective instruction (Chen, 2018). This modern view helps to reframe our understanding of variability. The concept of the “average student” experiencing the curriculum in a “standard” way is rapidly becoming outdated. Variability is both the rule within and between all individuals (Chen, 2018).