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Learning styles in humans

In the previous step, you saw some ways in which animals learn. These ways are also employed by humans, but there are some styles of learning unique to our species. It is to them we turn in this step.

Classifying how humans learn

Refugio and De Guzman (2018) have explored the ways in which human students learn. This is essentially the way an individual prefers to ‘absorb, process, comprehend and retain information’. The key thing to note is that different humans learn in different ways. Refugio and De Guzman use the example of learning how to build a clock. Some students prepare to learn how to build the clock by listening to verbal instructions whereas others prefer to ‘physically manipulate the clock themselves’. We explore some of the different ways in which humans learn in greater detail below.

Individual learning styles

Individual learning styles are determined by various factors such as emotions, the environment, cognition and previous experiences. In other words, everyone’s different (Refugio and De Guzman 2018), and according to Fleming and Mills (1992) human learning styles fall into four modalities, visual, aural, read/write and kinesthetic. They are described below:

Visual A preference for graphical and symbolic ways of representing information
Aural A preference for ‘heard’ information
Read/write Preferences for information printed as words
Kinesthetic …the perceptual preference related to the use of experience and practice (simulated or real)

(Fleming and Mills 1992: 138)

For a more critical view of the learning styles theory, see May (2018) ‘The Problem with Learning Styles’

Collaborative learning

Another aspect of learning characteristic to humans is collaborative learning. This is learning through cooperation within a team to solve a problem that leads to learning that can be applied to the resolution of other problems. This is relevant to the development of intelligent machines. An example of this is multi-agent cooperation, involving a multi-agent system (‘…a loosely coupled network of problem-solving entities (agents) that work together to find answers to problems that are beyond the individual capabilities or knowledge of each entity’ (Glavic 2006), for example, in a rescue situation such as a natural disaster like an earthquake.

Your task

Find an article comparing different learning styles in humans (visual, auditory, kinesthetic-touch, language, logical) with learning in machines (reinforcement, supervised, unsupervised, deep-learning). What did you find particularly interesting in the article?

Share the name of the article and your thoughts in the comments area with your fellow students


Fleming, N. and Mills, C. (1992). Not Another Inventory, Rather a Catalyst for Reflection. To Improve the Academy, 11, 138

Glavic, M. (2006). Agents and Multi-Agent Systems: A Short Introduction for Power Engineers. Technical Report. University of Liege

May, C. (2018, May 29). The Problem with Learning Styles. Scientific American https://www.scientificamerican.com/article/the-problem-with-learning-styles/

Refugio, C. N. and De Guzman, L. C. (2018). Students’ Learning Style Inventory [Thesis]. Negros Oriental State University

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This article is from the free online course:

Artificial Intelligence: Distinguishing Between Fact and Fiction

Coventry University