Inclusive Learning and Educational Equity 5

Cognitive Neuroscience Approach
Discoveries of cognitive neuroscience about learning brain: neuroplasticity and 
variability (Fandakova & Hartley, 
2020
), curiosity (Gruber et al., 
2019
), memory 
(Quent et al., 
2021
), goal-directed behavior (Huang et al., 
2020
), importance of 
prior knowledge (Alonso et al., 
2020
) help teachers to design learning environments 
that take advantage of these brain characteristics.
Contemporary neuroscience research suggests learning results from different 
large-scale network activity in the brain (Cantor et al., 
2018
; Quent et al., 
2021
; 
Mattar & Bassett 
2020
). These networks cooperate in the learner’s brain for com-
pleting specific cognitive functions (Siew et al., 
2019
). Cognitive neuroscience 
offers a theoretical framework for understanding brain functioning as a system cov-
ered by neurons’ structural and functional networks (Petersen & Sporns, 
2015
). 
Hartwigsen (
2018
) emphasizes a new perspective on the compensatory flexibility of 
these brain networks.
The cognitive neuroscience approach to learning brain includes the standard 
action of three sets of neural networks: (1) recognition network that helps the learner 
to recognize the patterns, collect information, and put it into meaningful categories, 
(2) strategic network that assists a learner to plan and generate the patterns and per-
form tasks, and (3) effective network that determines which patterns are most 
important for a learner and manage motivation and engagement (Rose & Strangman, 
2007
). Cognitive neuroscience studies of the brain have confirmed that these three 
main networks are active during learning (Siew, 
2020
; Wardle & Baker, 
2020
; 
Allegra et al., 
2020
; Markett et al., 
2018
).
The use of these networks in inclusive teaching and learning is applicable and 
makes the development of expert learners more effective through meeting learners’ 
needs and considering individual learners’ differences. Expert learners are manag-
ing specific networks of knowledge in their brains: “knowing what”, “knowing 
how”, and “knowing why”. Therefore, expert learners are prepared for learning, 
know how to learn, and want to learn. An expansion of the “knowing what” network 
contributes to the development of resourceful and knowledgeable learners, a 
strengthening of the “knowing how” network develops strategic and goal-directed 
learners, and activation of the “knowing why” network develops purposeful and 
motivated learners.
J. Navaitien
ė and E. Stasiūnaitienė

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29
Siew (
2020
) points to the fundamental goal of education as structural and func-
tional changes within the knowledge networks of learners. The learners assimilate 
the meaningful knowledge when they progress from being the novices to the experts. 
Expertise is associated with developing complex and hierarchical cognitive struc-
tures in the brain (Bilali
ć & Campitelli, 
2018
).
Persky and Robinson (
2017
) present several characteristics of experts. For exam-
ple, they argue that experts know more, and their knowledge is better organized and 
integrated into the existing knowledge system. They also have effective strategies 
for using knowledge, have strong motivations, and are sufficiently self-regulated 
because their knowledge is well-organized. Consequently, making inferences, con-
cluding, and finding solutions do not seem impossible to them.
Transforming learners into expert learners is doable if the three brain networks 
(recognition, strategic, and affective) are targeted by the UDL framework.