FocusUsing machines to model, mimic, and extend human learning and knowledge
- Knowledge is … ever-evolving repertoire of possibility
- Knowing is … appropriate functioning
- Learner is … a digital technology
- Learning is … expanding the repertoire of possibility
- Teaching is … challenging
SynopsisLearning-Machine Discourses focus on the use of digital technologies to make sense of the phenomenon of learning and to augment human possibility. On those matters, some are principally concerned with theoretical insights, while others are driven mainly by pragmatic interests. Associated discourses include:
- Computational Learning Theory (1950s) – a branch of computer science that brings together Machine Learning, Artificial Intelligence, and mathematics to study learning systems
- Statistical Learning Theory (1970s) – a branch of computer science that brings together Machine Learning, Artificial Intelligence, and statistics to examine the utility and effectiveness of particular algorithms (Note that the phrase “Statistical Learning Theory” is also used as an alternative name of Stimulus Sampling Theory – a very different perspective on learning.)
CommentaryOn the surface, it might also appear that all Learning-Machine Discourses address matters of “learning” and “knowledge” – but the truth of that point depends on what one assumes those words to mean. Some proponents and commentators assert that notions associated with “learning” are invoked only metaphorically across Learning-Machine Discourses. Others assume that these discourses are focused on modeling or imitating (but not duplicating) actual processes of learning. Still others assert that Learning-Machine Discourses are dealing with learning by non-organic entities.
- Computational Learning Theory
- Statistical Learning Theory
Please cite this article as:
Davis, B., & Francis, K. (2022). “Learning-Machine Discourses” in Discourses on Learning in Education. https://learningdiscourses.com.
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