Love them or hate them, you use them and algorithms are here to stay. In this article, Donald Clark explains the role of algorithms in adaptive learning systems, how they work and, more importantly the Top Ten advantages they have over traditional teaching:
- Gender, race, color, accent, social background
Algorithms are blind to the sort of social biases (gender, race, color, age, ethnicity, religion, accent, social background).
- Free from cognitive biases
Cognitive biases around ability versus effort, marking and grades clearly affect teacher and learner behavior.
- Never get tired, ill, irritable or disillusioned
Algorithm behavior is only variable in the sense that it uses variables. Algorithms are at the top of their game 24/7/365.
- Algorithms can do things that brains cannot
The number of variables, and sheer formulaic power of an ensemble of algorithms, is beyond the capability of the brain.
- Personalizes the speed of learning
Algorithms treat learners as individuals and personalize the learning journey. You are streaming into streams of one.
- Prevents catastrophic failure & drop-out
Slower learners do not get left behind or suffer catastrophic failure in a final exam when it is too late, lowering drop-out.
- Personal reporting
Reports really do match personal attainment, through personal feedback for the learner than motivates and eases progress.
- They learn
It is a mathematical feature of machine learning that the system gets better the more students that take the course.
- Course improvement
Automatically identify erroneous content, questions, good resources, even optimal paths through a network of possibilities.
- Massively scalable
Humans are not scalable but algorithms are massively scalable.
This approach to technology-based learning could be a massive breakthrough in terms of learning outcomes for millions of learners – it is only a matter of when it will be used in more formal learning environments.