This is the beginning of a very good idea. Happily, many, many highly-competent educational researchers have had it already, and some have pursued it to a fair degree of success, particularly in constrained domain fields (think science, technology, engineering, maths, medicine). It certainly seems to be blooming as a field again these last 5-10 years.
Potentially-useful search terms include: intelligent tutoring systems, AI in Education, educational data mining.
One particularly-nifty system is the Pittsburgh Science of Learning Centre’s Datashop, which is a shared, open repository of learner interactions with systems designed to teach along these lines. The mass of data there helps get evidence of what sequence of concepts actual learners find helpful, rather than what sequence teachers think they will.
I am learning Math and Physics after a long break. I found putting together the sequence of things I need to learn has been quite difficult.
Several times I found I had spent a lot of time on something that was not really needed. This is particularly the case with Math as a prerequisite for physics. The mathematicians seem to want you to learn a lot more Math than you need eg Rings, Category theory and Topology may be useful for physics at some stage. But not for me yet.
Conversely I found that I was short on some unexpected topic once or twice eg Linear Algebra was in one case not listed as a prerequisite but would have been very useful.
Others have commented on various difficulties in coming up with a single dependency graph. I do think you have underestimated the difficulties of this.
Overall this post is a bunch of plausible ideas but in need of a) More research on the state of the art b) A test in a real learning situation. It is a cliche in the startup world that ideas are cheap, tested ideas with proof by execution are potentially far more valuable.
This is the beginning of a very good idea. Happily, many, many highly-competent educational researchers have had it already, and some have pursued it to a fair degree of success, particularly in constrained domain fields (think science, technology, engineering, maths, medicine). It certainly seems to be blooming as a field again these last 5-10 years.
Potentially-useful search terms include: intelligent tutoring systems, AI in Education, educational data mining.
One particularly-nifty system is the Pittsburgh Science of Learning Centre’s Datashop, which is a shared, open repository of learner interactions with systems designed to teach along these lines. The mass of data there helps get evidence of what sequence of concepts actual learners find helpful, rather than what sequence teachers think they will.
To add that keyword list: Adaptive learning, educational big data.
I am learning Math and Physics after a long break. I found putting together the sequence of things I need to learn has been quite difficult.
Several times I found I had spent a lot of time on something that was not really needed. This is particularly the case with Math as a prerequisite for physics. The mathematicians seem to want you to learn a lot more Math than you need eg Rings, Category theory and Topology may be useful for physics at some stage. But not for me yet.
Conversely I found that I was short on some unexpected topic once or twice eg Linear Algebra was in one case not listed as a prerequisite but would have been very useful.
Others have commented on various difficulties in coming up with a single dependency graph. I do think you have underestimated the difficulties of this.
Overall this post is a bunch of plausible ideas but in need of a) More research on the state of the art b) A test in a real learning situation. It is a cliche in the startup world that ideas are cheap, tested ideas with proof by execution are potentially far more valuable.
Thanks a lot for the references!! I’ll read up.
I’ve been thinking a lot about this topic as well. This comment is very helpful! Thank you!