MULTIDIMENSIONAL ASSESSMENT SCORING USING MACHINE LEARNING

1 . A computer implemented method for enhanced monitoring of learning progressions, the method comprising:

obtaining a first set of examinations and a first set of responses corresponding to the first set of examinations;

obtaining, from a graphical user interface, a first set of examination assessments;

training a multidimensional scoring model based on the first set of examinations, the first set of responses, and the first set of examination assessments;

generating a confusion matrix based on the first set of examination assessments;

determining a performance assessment value from the confusion matrix; and

determining that the multidimensional scoring model has been sufficiently trained if the performance assessment value exceeds a selected threshold value.

2 . The method of claim 1 , further comprising:

obtaining a second set of examinations and a second set of responses corresponding to the second set of examinations;

applying the trained multidimensional scoring model to each of the second set of responses and second set of examinations to determine a learning progression level associated with each response of the second set of responses; and

displaying the learning progression on the graphical user interface.

3 . The method of claim 1 , further comprising:

applying the trained multidimensional scoring model to each of the second set of responses and second set of examinations to determine a learner progression sublevel representing a learner error type; and

displaying the learner progression sublevel on the graphical user interface.

4 . The method of claim 1 , wherein the multidimensional scoring model comprises a machine learning process.

5 . The method of claim 4 , wherein the machine learning process comprises a convolutional neural network, a decision tree, Bayes networks, or a logistic regression.

6 . The method of claim 1 , wherein the first set of examinations comprise questions requiring constructed responses, forced choice responses, or mixed form responses.

7 . The method of claim 1 , wherein the first set of examinations comprise questions requiring constructed responses, forced choice responses, and mixed form responses.

8 . The method of claim 7 , wherein training the multidimensional scoring model further comprises determining a level of correlation between constructed responses and forced choice responses for related examination question features.

9 . The method of claim 1 , further comprising:

selecting a set of feature parameters from multiple examination questions of the first set of examinations; and

generating the first set of examination assessments by tokenizing each response into sub-responses according to the selected feature parameters and evaluating each sub-response.

10 . The method of claim 9 , further comprising adjusting a number of sub-features to increase the performance assessment value until the performance assessment value exceeds the selected threshold value.

11 . The method of claim 1 , wherein the performance assessment value comprises a Kappa value, a quadratic weighted Kappa value, an F score, a Matthews correlation coefficient, an informedness value, a null error rate, a positive predictive value, a negative predictive value, a prevalence value, a precision value, a specificity value, or a sensitivity value.

12 . The method of claim 1 , wherein the performance assessment value comprises a quadratic weighted Kappa value.

13 . The method of claim 12 , wherein the selected threshold value is more than about 0.6.

14 . The method of claim 12 , wherein the selected threshold value is more than about 0.7.

15 . A system for enhanced monitoring of learning progressions, the system comprising:

a N-dimensional scoring logical circuit, a data store, and a graphical user interface, wherein the N-dimensional scoring logical circuit comprises a processor and a non-transitory medium with computer executable instructions embedded thereon, the computer executable instructions being configured to cause the processor to:

obtain, from the data store, a first set of examinations and a first set of responses corresponding to the first set of examinations;

obtain, from the graphical user interface, a first set of examination assessments;

train a multidimensional scoring model based on the first set of examinations, the first set of responses, and the first set of examination assessments;

generate a confusion matrix based on the first set of examination assessments;

determine a performance assessment value from the confusion matrix; and

determine that the multidimensional scoring model has been sufficiently trained if the performance assessment value exceeds a selected threshold value.

16 . The system of claim 15 , wherein the computer executable instructions are further configured to cause the processor to:

obtain a second set of examinations and a second set of responses corresponding to the second set of examinations;

apply the trained multidimensional scoring model to each of the second set of responses and second set of examinations to determine a learning progression level associated with each response of the second set of responses; and

display the learning progression on the graphical user interface.

17 . The system of claim 15 , wherein the computer executable instructions are further configured to cause the processor to:

apply the trained multidimensional scoring model to each of the second set of responses and second set of examinations to determine a learner progression sublevel representing a learner error type; and

display the learner progression sublevel on the graphical user interface.

18 . The system of claim 15 , wherein the multidimensional scoring model comprises a machine learning process.

19 . The system of claim 15 , wherein the machine learning process comprises a convolutional neural network, a decision tree, Bayes network, or a logistic regression.

20 . A computer implemented method for enhanced monitoring of learning progressions, the method comprising:

obtaining a first set of examinations and a first set of responses corresponding to the first set of examinations;

obtaining, from a graphical user interface, a first set of examination assessments;

training a multidimensional scoring model based on the first set of examinations, the first set of responses, and the first set of examination assessments;

generating a confusion matrix based on the first set of examination assessments;

determining a Quadratic Weighted Kappa value from the confusion matrix; and

determining that the multidimensional scoring model has been sufficiently trained if the Quadratic Weighted Kappa value exceeds about 0.6;

wherein the multidimensional scoring model comprises a logistic regression machine learning model.