Azilawati Jamaludin

With increasing interest in the possible contributions of neuroscience research to educational practice, the field of ‘educational neuroscience’ has emerged. Educational neuroscience (also known as ‘mind, brain, and education’ or ‘neuroeducation’) integrates the disciplines of neuroscience, cognitive psychology, and education, and it seeks to study the relationship between the brain, mental processes, and behaviours using a combination of neuroscience and behavioural methods (Szűcs & Goswami, 2007). Neuroscience and behavioural data can inform our understanding of learning and can therefore inform educational practice (Howard-Jones et al., 2016). Some examples are: neuroscience data alongside behavioural data constrain psychological theories (Gabrieli, 2016), neuroscience provides new insights into the learning processes (De Smedt, 2018), and neuroscience leads to the development of new instructions (Howard-Jones et al., 2016). However, challenges exist in applying theoretical knowledge from neuroscience research to inform educational practice in order to impact classroom outcomes in the real world (Bowers, 2016a, 2016b; De Smedt, 2018; Thomas, Ansari, & Knowland, 2019).

A major challenge in applying neuroscience research to inform educational practice is that there is a gap between the study of how the brain works and the practice in classroom, i.e., the neuroscience-education gap. Neuroscientists understand the relationship between brain and behaviour, but they have little knowledge about classroom instruction; educators understand classroom instruction, but they have little knowledge about the relationship between brain and behaviour (Ansari, De Smedt, & Grabner, 2012). The different languages used in the fields of neuroscience and education make the communication between the two fields and the understanding of each other difficult. Misinterpretations can occur when neuroscientists who have little knowledge about classroom instruction turn an experimental task into a classroom intervention or when educators who have little knowledge about the relationship between brain and behaviour over-interpret brain imaging findings (De Smedt, 2018). As a result, efforts to translate neuroscience research into meaningful educational practice have been quite limited.

Bridges can be built at multiple levels to bring the neuroscience-education gap closer, and one way of applying neuroscience research to inform educational practice is by developing educator brain literacy (Ansari & Coch, 2006). Brain literacy is the understanding of the relationship between brain and behaviour; developing educator brain literacy is helping educators understand how the brain learns. The rationale for developing educator brain literacy is: (1) the brain is constantly changing in response to the environment (e.g., Dubinsky, Roehrig, & Varma, 2013); (2) cognitive diversity is the norm (i.e., there are individual differences in the ability to learn) for all children (e.g., Hale, Fiorello, Kavanagh, Holdnack, & Aloe, 2007); and (3) designing instruction based on the understanding of cognitive diversity maximises a student’s learning and potentially prevents learning difficulties from developing into a lifelong disability (e.g., Koziol, Budding, & Hale, 2013).

Given that teaching changes the brain, brain literacy is potentially very useful for educators (Walker, Chen, Poon, & Hale, 2017). First, brain literacy can sensitise educators to individual differences in the ability to learn, which can help them differentiate instruction to meet the needs of diverse learners (e.g., Tomlinson, 2014). Specifically, brain literacy can help educators develop the skills to serve all students by recognising the impact of individual differences in the ability to learn on their instructional processes and student outcomes. Brain literate educators are more likely to understand and meet the diverse learning needs of students by recognising the signs and symptoms exhibited by students and applying alternative instructional strategies. Second, brain literacy enables educators to consider both brain and behavioural information when designing curriculum and instruction to improve student outcomes. Considering both brain and behavioural information may be more beneficial compared to considering behavioural information alone (Gabrieli, Ghosh, & Witfield-Gabrieli, 2015). Therefore, acquiring brain literacy has potential to empower teachers to re-evaluate the effects of their practices (Schwartz et al., 2019) in light of newfound neuroscience evidence, which although has yet to be empirically tested, may be beneficial for their students.

The use of games for learning is ubiquitous in today’s classrooms. Numerous digital game based learning (DGBL) research has demonstrated enhanced learning outcomes in various areas, such as developing proficiency in cognitive skills, developing critical thinking and problem-solving dispositions, enhancing students’ motivation and engagement in challenging content areas and improving attention and overall learning experience. Yet we also encounter verbal cautionary statements about the excessive use of games particularly in areas of prospective addiction, overly dependence on gameplay, and social isolation of gamers. To address both cognitive and social issues associated with games, schools have integrated the teaching of cyber wellness during Character and Citizenship Education which focuses on the well-being of students as they navigate cyberspace. To further extend the effective use of games as an ICT modality. How can teachers, parents and students balance optimal use of games in education such that learning is maximised? What are the critical educational game aspects that teachers can take note of, in the current technology-dominated times, to enhance learners’ learning experiences and learning outcomes?

In this chapter, we elucidate critical educational game-based learning aspects, harnessing the power of technology for optimal learning. These include four key aspects of (1) alignments between learning aims and assessment measurements, (2) harnessing technological affordances for enhanced design features, (3) integrating learning analytics to capture learning progression, and (4) designing appropriate contexts for gameplay that optimises both individual learning and social interactions. This chapter also aims to clarify terms such as gamification and game-based learning.