Abstract:

Learning hierarchies such as Bloom’s Taxonomy (1956) and Gagne (1974) were first proposed decades ago. These have been hardly updated in recent decades and fail to incorporate and reflect the huge leaps forward in understanding cognition, learning and behaviour through the behavioural sciences and, for example, the neurosciences. More recent taxonomies such as Anderson et al. (2001) and Fink’s (2003) have incorporated some more recent work but still fall short of reflecting current evidence from the cognitive, behavioural and developmental sciences.

Fink’s is the only taxonomy that isn’t hierarchical, and hierarchies can pose many challenges for they may not reflect natural development and cognitive abilities. For example, analysing and evaluation are placed higher on Bloom’s learning hierarchy but this conflicts with recent research recent showing that babies as young as 3-months old have statistical problem-solving abilities (Markant et al. 2016), recognise pro-social behaviour (Hamlin et al. 2010), exhibit in-group bias (Mahajan and Wynn 2012), and show desire for punishment of anti-social individuals (Wynn 2007). Similarly, memory research shows that declarative memory – often a focus of educators, is different to conceptual and process memory (Atkinson-Shiffrin model) and hence raises questions of the consistent focus on declarative processes in education. Similarly, some skills that are considered cognitive such as playing chess have been shown to be less cognitive than previously thought: the most competent chess players are the best at pattern detection rather than analysing possibilities of different moves (Schneider et al.1993).

We therefore propose that these taxonomies and models should not be updated but rather discarded and a new taxonomy and hierarchy given that reflects evidence-based and empirical research, and the nature of brain development, growth and learning. We propose a holistic framework that reflects how concepts arise with different brain regions coming online, how psychological needs drive learning and well-being, and that psychomotor, affective and cognitive theories can be incorporated into one broad human behavioural framework.

A final addition is that contextual situations may provide very different challenges and goals for educators. A recent twist to the cognitive control experiment, the famous marshmallow test, showed that trustworthiness of the adult giving instructions was more predictive of the result than other factors (Kidd et al. 2012). This means that context plays a critical role: this will have far-reaching implication for educators in different socio-economic environments and cultures. It is therefore unwise to consider a learning taxonomy without considering the environment. We also propose guidance for some environmental factors.

Keywords:

Learning taxonomy, learning hierarchy, cognitive neuroscience.