Machine Learning


Strategies used by computers to surpass their designers’ conceptual competencies

Principal Metaphors

  • Knowledge is … task-based competence
  • Knowing is … operating
  • Learner is … a mechanical processor
  • Learning is … generating inferences that surpass inputs
  • Teaching is … programming




Machine Learning encompasses all efforts and strategies to structure and program machines to improve their performances on specific tasks. In these applications, “learning” is usually defined in operational rather than cognitive terms – that is, in terms of measurable improvements in performance, not any sort of imagined “thinking.” Many strategies are under development, employing different computer architectures and a range of logics. Prominent approaches include genetic algorithms (mimicking the process of natural selection), Bayesian networks (based on probabilistic modeling of a situation), support vector machines (which apply sorting techniques to classify phenomena), and artificial neural networks (Deep Learning; modeling brain structure to perform complex recognitions). At present, there are three major paradigms in Machine Learning:
  • Reinforcement Learning – Drawing inspiration from Behaviorisms’ focus on rewarding desired behaviors and punishing undesired ones, in this approach algorithms run through many iterations of a multi-step task. Positive outcomes are incentivized and negative outcomes are penalized.
  • Supervised Learning – This approach is based on data mining. The machine is provided with a large set of examples, from which it is expected to determine a rule for inclusion and/or exclusion that can then be used to decide if new examples belong or don’t belong.
  • Unsupervised Learning – In this approach, an untrained machine is provided with uncategorized examples and directed to group them according to similarities and differences.
  • Apprenticeship Learning (Learning from Demonstration) – Invoking the notion of Apprenticeship developed within Situated Learning (which involves working with an expert), Apprenticeship Learning is a form of Supervised Learning (see above) in which the dataset comprises a carefully selected set of efficient task executions.


At first blush, it might not seem appropriate to include Machine Learning in a survey of theories of learning associated with formal education. The decision to do so is justified in the fact that Machine Learning has become the source of important insights into and strategies to investigate human learning, enabled by a reversal of Cognitivism’s indefensible grounding metaphor (i.e., “brain as computer”) to an attitude that human cognition might be better understood through efforts to model and mimic the brain’s complexity and situatedness (i.e., “computer as brain”). That is, rather than positing a theory of learning based on an uninterrogated metaphor and, Machine Learning is affording insights into human cognition by attending critically to similarities and differences of brain-based and machine-based structures and dynamics.

Authors and/or Prominent Influences


Status as a Theory of Learning

Machine Learning is a round-about theory of learning – that is, it is a field of study that is affording profound insights into brain function and human learning through modeling and testing a broad array of machine-based strategies for perceiving, manipulating, sorting, and other tasks that are typically interpreted as cognitive.

Status as a Theory of Teaching

Machine Learning is not a theory of teaching, but findings are proving useful for informing basic matters of formatting and sequencing information to make it more accessible.

Status as a Scientific Theory

Machine Learning is a robust domain of scientific inquiry.


  • Apprenticeship Learning (Learning from Demonstration)
  • Reinforcement Learning
  • Supervised Learning
  • Unsupervised Learning

Map Location

Please cite this article as:
Davis, B., & Francis, K. (2021). “Machine Learning” in Discourses on Learning in Education.

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