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study.com tips for passing quickly?
#21
(01-19-2019, 11:50 AM)cookderosa Wrote: I would love to build a sequence list for some of these - but I haven't figured out how without actually completing a class.  

My son's social psychology class is complete, so I can look up the other psych courses to find how much is done, but there are too many variables to figure this out - at least I can't figure it out.  Anyone here figured out how to guesstimate the % gains in a class before taking another class?

I know how to do this, unfortunately the initial data extraction would be a pain in the ass, and it also would not be trivial to keep up to date.  To begin: how to look at correlating data.  If you look at the Economics 101 and compare it to Accounting 303, here are two example entries:
Economics 101 Chapter 6 Lesson 1 - Purpose & Components of Cost Accounting
Accounting 303 Chapter 2 Lesson 1 - Purpose & Components of Cost Accounting

Economics 101 Chapter 6 Lesson 9 - Opportunity Cost: Definition, Calculations & Examples
Accounting 303 Chapter 1 Lesson 7 - Opportunity Cost: Definition, Calculations & Examples

Having taken Economics 101, but not done Accounting 303, I can verify that the lesson titles do not deviate, but chapter and lesson order can.  So, in order to correlate everything, you would need to capture all of the lesson titles from every single course (or at least the subset of courses you are interested in).  If you captured the list from all of the accredited courses right now, this would produce a list over 21,000 entries long.  You could write a bot to capture that info for you, but you would have to throttle it since Study.com uses anti-spidering technology to prevent people from clicking links too quickly.  Once you capture all of the info, it would just be a simple matter of writing a script to search for duplicates.  This last part is the easy part, it's the data capture that is quite daunting.  And, since SDC is constantly updating old courses while also creating new ones, this could be quite troublesome to keep up to date.
John L. Watson
Earned: WGU: BS-NOS (2019), WGU: MS-CSIA (2021)
Current Programs: UC: PhD in InfoSec (2025), AMA: DIT (2024), ENEB: MBA (2023)
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