FocusStrategies used by computers to surpass their designers’ conceptual competencies
- Knowledge is … task-based competence
- Knowing is … operating
- Learner is … a mechanical processor
- Learning is … generating inferences that surpass inputs
- Teaching is … programming
SynopsisMachine 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). Associated discourses include:
- Active Learning – In the context of Machine Learning, Active Learning refers to strategies aimed at maximizing performance while minimizing trials/samples. (Note: Should not be confused or conflated with Active Learning.)
- Actor–Critic Model – a two-component, iterative model of learning that combines the activities of an actor (which establishes associations between stimuli and responses) and a critic (which assesses the value of stimuli)
- 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.
- Automated Learning – 1. a synonym for Technology-Mediated Individual Learning. 2. a synonym for Machine Learning
- Deep Active Learning – a mash-up of Deep Learning and Active Learning (see above), aimed at rendering Deep Learning more efficient (Note: There’s also a Deep Active Learning associated with Deep vs. Surface Learning.)
- Feature Learning (Representation Learning) – in Machine Learning, any technique that requires a system to distill the defining features of a preselected set of representations so that it can use that information to analyze new data
- 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.
CommentaryAt 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 InfluencesDiffuse
Status as a Theory of LearningMachine 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 TeachingMachine 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 TheoryMachine Learning is a robust domain of scientific inquiry.
- Active Learning
- Actor–Critic Model
- Apprenticeship Learning (Learning from Demonstration)
- Automated Learning
- Deep Active Learning
- Feature Learning (Representation Learning)
- Reinforcement Learning
- Supervised Learning
- Unsupervised Learning
Please cite this article as:
Davis, B., & Francis, K. (2022). “Machine Learning” in Discourses on Learning in Education. https://learningdiscourses.com.
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