Systems and methods for electronic prediction of rubric assessments

1 . A method for automatically assessing a plurality of users of an electronic learning system, the electronic learning system including a plurality of user computing devices, an instructor computing device, at least one data storage device, and at least one processor, the method comprising:

receiving grade objects for the plurality of users from the instructor computing device, wherein, for an essay, the grade object is obtained by:

training first, second, third, and fourth common n-gram classifiers and storing the first, second, third, and fourth common n-gram classifiers on the at least one data storage device, wherein the second common n-gram classifier using a different type of n-gram than the first common n-gram classifier, the third common n-gram classifier is trained separately from the first and second common n-gram classifiers, and the fourth common n-gram classifier is trained separately from the first and second common n-gram classifiers and uses a different type of n-gram than the third common n-gram classifier;

receiving, at the at least one processor, the essay from the user computing device of a user, the essay sent as a first electronic file and sent to the at least one processor via an electronic communications channel;

pre-processing, using the at least one processor, the essay, including removing stop words and stemming remaining words of the essay and extracting n-grams;

automatically accessing, using the at least one processor, the first common n-gram classifier stored on the at least one storage device and automatically applying, using the at least one processor, the first common n-gram classifier to generate a first score for a style dimension of the essay;

automatically accessing, using the at least one processor, the second common n-gram classifier stored on the at least one storage device and automatically applying, using the at least one processor, the second common n-gram classifier to generate a second score for the style dimension of the essay;

automatically accessing, using the at least one processor, the third common n-gram classifier stored on the at least one storage device and automatically applying, using the at least one processor, the third common n-gram classifier to generate a third score for an organization dimension of the essay;

automatically accessing, using the at least one processor, the fourth common n-gram classifier stored on the at least one storage device and automatically applying, using the at least one processor, the fourth common n-gram classifier to generate a fourth score for the organization dimension of the essay; and

generating, using the at least one processor, a grade object for the essay, the grade object including the first, second, third, and fourth scores;

automatically applying one or more contributor policies to the grade objects to generate processed grade objects;

automatically applying an aggregator to the processed grade objects to generate an aggregate grade object;

automatically applying one or more result policies to the aggregate grade object to generate a result grade object for each of the plurality of users;

sending, from the at least one processor, the result grade object to each of the plurality of user computing devices, wherein the result grade object is sent from the at least one processor to the user computing device via the electronic communications channel.

2 . The method of claim 1 , wherein the method further comprises storing the result grade object in a data storage device of the electronic learning system.

3 . The method of claim 1 , wherein the method further comprises at least one of displaying the result grade object on a display, and generating a hardcopy output of the result grade object.

4 . An electronic learning system including automated marking for a plurality of users, the system comprising:

at least one data storage device, the at least one storage device storing a first common n-gram classifier, a second common n-gram classifier, a third common n-gram classifier, and a fourth common n-gram classifier, the second common n-gram classifier using a different type of n-gram than the first common n-gram classifier, wherein the third common n-gram classifier is trained separately from the first and second common n-gram classifiers, and wherein the fourth common n-gram classifier is trained separately from the first and second common n-gram classifiers and uses a different type of n-gram than the third common n-gram classifier; and

at least one processor communicatively coupled to the at least one data storage device, the at least one processor operable to:

receive grade objects for the plurality of users from an instructor computing device, wherein, for an essay, the grade object is obtained by:

receive an essay from a user computing device of each of a plurality of users, the essay sent as a first electronic file and sent to the at least one processor from the user computing device via an electronic communications channel;

pre-process the essay, including removing stop words and stemming remaining words of the essay and extracting n-grams;

automatically access the first common n-gram classifier stored on the at least one data storage device and automatically apply the first common n-gram classifier to generate a first score for a style dimension of the essay;

automatically access the second common n-gram classifier stored on the at least one data storage device and automatically apply the second common n-gram classifier to generate a second score for the style dimension of the essay;

automatically access the third common n-gram classifier stored on the at least one data storage device and automatically apply the third common n-gram classifier to generate a third score for an organization dimension of the essay;

automatically access the fourth common n-gram classifier stored on the at least one data storage device and automatically apply the fourth common n-gram classifier to generate a fourth score for the organization dimension of the essay; and

generate a grade object for the essay, the the grade object including the first, second, third, and fourth scores;

automatically applying one or more contributor policies to the grade objects to generate processed grade objects;

automatically applying an aggregator to the processed grade objects to generate an aggregate grade object;

automatically applying one or more result policies to the aggregate grade object to generate a result grade object for each of the plurality of users;

send the result grade object for each of the plurality of users to the user computing device of the user via the electronic communications channel.

5 . The automated marking system of claim 4 , wherein the at least one processor is further configured to store the result grade object in the data storage device.

6 . The automated marking system of claim 4 , wherein the at least one processor is further configured to display the result grade object on a display or generate a hardcopy output of the evaluation.

7 . A non-transitory computer readable medium comprising a plurality of instructions executable on at least one processor of an electronic device for configuring the electronic device to implement a method for automatically marking a plurality of users, wherein the method comprises:

receiving grade objects for the plurality of users, wherein, for an essay, the grade object is obtained by:

training first, second, third, and fourth common n-gram classifiers and storing the first, second, third, and fourth common n-gram classifiers on at least one data storage device, wherein the second common n-gram classifier using a different type of n-gram than the first common n-gram classifier, the third common n-gram classifier is trained separately from the first and second common n-gram classifiers, and the fourth common n-gram classifier is trained separately from the first and second common n-gram classifiers and uses a different type of n-gram than the third common n-gram classifier;

receiving, at the at least one processor, the essay from at least one of the plurality of users, the essay sent to the at least one processor via an electronic communications channel;

pre-processing the essay, including removing stop words and stemming remaining words of the essay and extracting n-grams;

automatically accessing the first common n-gram classifier stored on the at least one data storage device and automatically applying the first common n-gram classifier to generate a first score for a style dimension of the essay;

automatically accessing the second common n-gram classifier stored on the at least one data storage device and automatically applying the second common n-gram classifier to generate a second score for the style dimension of the essay;

automatically accessing the third common n-gram classifier stored on the at least one data storage device and automatically applying the third common n-gram classifier to generate a third score for an organization dimension of the essay;

automatically accessing the fourth common n-gram classifier stored on the at least one data storage device and automatically applying the fourth common n-gram classifier to generate a fourth score for the organization dimension of the essay; and

generating a grade object for the essay, the the grade object including the first, second, third, and fourth scores;

automatically applying one or more contributor policies to the grade objects to generate processed grade objects;

automatically applying an aggregator to the processed grade objects to generate an aggregate grade object;

automatically applying one or more result policies to the aggregate grade object to generate a result grade object for each of the plurality of users;

sending the result grade object to each of the plurality of users via an electronic communications channel.

8 . The method of claim 1 , wherein grade objects are numerical.

9 . The method of claim 1 , wherein the grade objects are aggregated across a plurality of subjects.

10 . The method of claim 1 , wherein one or more of the grade objects are pre-emptive grade objects to forecast a result grade object.