Learning a new STEM subject is unlike learning a new language. When you learn a new language, you learn new words for familiar concepts. When you learn a new STEM subject, you learn new words for unfamiliar concepts.
I frequently find that a big part of the learning curve is trying to “reason from the jargon.” You haven’t yet tied a word firmly enough to the underlying concept that there’s an instant correspondence, and it’s easy to completely lose track of the concept.
One thing that can help is to focus early on building up a strong sense of the fundamental thing you’re talking about.
For example, let’s say you’re learning statistics. Most people don’t think in terms of “categorical” and “quantitative” data, “means” and “counts.” These terms don’t trigger an immediate image in most people’s minds.
I find it really helpful to build up a stock of images—a mental image of box plots for ANOVA, a mental image of a table of counts for chi squared, and so on.
Then the process of learning the topic is of manipulating that fundamental ground truth image.
It’s essentially the Feinman “fuzzy green ball” approach but applied to just basic scholarship rather than new mathematical ideas.
Learning a new STEM subject is unlike learning a new language. When you learn a new language, you learn new words for familiar concepts. When you learn a new STEM subject, you learn new words for unfamiliar concepts.
I frequently find that a big part of the learning curve is trying to “reason from the jargon.” You haven’t yet tied a word firmly enough to the underlying concept that there’s an instant correspondence, and it’s easy to completely lose track of the concept.
One thing that can help is to focus early on building up a strong sense of the fundamental thing you’re talking about.
For example, let’s say you’re learning statistics. Most people don’t think in terms of “categorical” and “quantitative” data, “means” and “counts.” These terms don’t trigger an immediate image in most people’s minds.
I find it really helpful to build up a stock of images—a mental image of box plots for ANOVA, a mental image of a table of counts for chi squared, and so on.
Then the process of learning the topic is of manipulating that fundamental ground truth image.
It’s essentially the Feinman “fuzzy green ball” approach but applied to just basic scholarship rather than new mathematical ideas.