Err - not really looking so much as I had an idea the other day...
Let's say I have an essay - 4-5 paragraphs and this essay is graded and based on it's quality, determines a pass or fail.
Let's say I have access to a couple hundred that passed.
Could ML be used to scan a new essay and determine based on what I've already scanned if the new essay is a pass or fail and a % chance of either?
Do I need examples of failed essays?
Sidhant Panda
Programmer
Hey Mario,
First and foremost, you have to establish whether or not the paragraphs are relevant to the question asked. To find what the paragraphs are talking about, you could first do a simple test on these parameters:
Now that you have matched the context, you can proceed to find how difficult or easy it is to ready the text. Here is a list of popular tests (in no particular order):
It would help to build a word ontology for similar words and you could crawl wikipedia or a thesaurus for this purpose.
The failed essays would help establish what kind of threshold score in both the context and the ease of reading test you are looking for.
This is really basic and we haven't yet dived into NLP for this problem yet. However I feel this might just do the job.
PS: I worked on something similar in my undergraduate!