I am currently working on grade automation for open questions using machine learning algorithms/natural language processing/text mining etc. for university assessment purposes. My main focus is on the importance of question types on the performance of algorithms and their relation (for instance how to design questions to improve understandably for students which in turn result in more clear answers for automatic grading). I am looking for available literature focusing on the question types. Does anyone has any suggestions for available literature?
Hai Maryam,
Interesting and challenging to work with text mining concept. when it is open ended question complexity will be in larger. Various text analytics open source tool available will optimize the flow of work.
integrating the input with text analytics and combining with scaling makes the complete process along with statistics to add importance.
Key points and words will make the process of pattern matching for evaluating the correctness of answer.
Consider the "Great Books" methodology of the Socratic method for developing questions. The organization is located in Chicago - their education department may be able to address how the methodology works w/technology.
I can't help you, but would love to see your solution. Maybe through key words?
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