초록 ▼

An intelligent tutoring system can be implemented using filters, predictive modeling, and a knowledge warehouse, which allows content to be dynamically selected for individualized presentation to a learner. Such an intelligent tutoring system can be implemented using a variety of techniques, includi

An intelligent tutoring system can be implemented using filters, predictive modeling, and a knowledge warehouse, which allows content to be dynamically selected for individualized presentation to a learner. Such an intelligent tutoring system can be implemented using a variety of techniques, including polynomial vector equations in modeling, extensible markup language storage formats for presentation data, and a knowledge warehouse with information stored in data cubes.

대표청구항 ▼

1. A method of automatically selecting educational content to present to a student and storing information about the student's educational achievement comprising: a) using a set of computer executable instructions stored in local memory on a computer, causing the computer to execute a retrieval modu

1. A method of automatically selecting educational content to present to a student and storing information about the student's educational achievement comprising: a) using a set of computer executable instructions stored in local memory on a computer, causing the computer to execute a retrieval module to retrieve a competency record for the student from a database, wherein said competency record comprises a set of data indicating said student's knowledge in a plurality of competencies, and wherein retrieving the competency record for the student comprises storing said set of data in the computer's local memory;b) using the set of computer executable instructions, causing the computer to execute a transition module to determine a set of educational content for said student based on a comparison of a predicted skill level for the student indicated by a first predictive model comprising a nonlinear polynomial student model and a level for said set of educational content in a first competency from said plurality of competencies, wherein said level is retrieved from metadata associated with said set of educational content, wherein the nonlinear polynomial student model: (i) comprises an equation having a degree of at least three, and(ii) indicates the predicted skill level based on time spent by the student in instruction on the first competency;c) using the set of computer executable instructions, causing the computer to execute an evaluation module to dynamically select a plurality of questions assessing knowledge in said first competency for presentation to said student;d) using the set of computer executable instructions, causing the computer to execute a normalization module to perform a cleaning operation and a transformation operation on a set of evaluation data, wherein said set of evaluation data is based on input received from said student in response to the presentation of the plurality of questions; ande) using the set of computer executable instructions, causing the computer to execute an update module to modify the set of data stored in the computer's local memory to reflect the set of evaluation data and the cleaning and transformation operations performed by the normalization module and to propagate the modification to the set of data back to the database. 2. The method of claim 1 wherein the first predictive model comprises a polynomial vector student model. 3. The method of claim 1, wherein: a) performing said cleaning operation comprises applying a rule for allowable values to said set of evaluation data;b) performing said transformation operation comprises converting a test result from said set of evaluation data into a z-score; andc) the cleaning operation and the transformation operation are indicated in the metadata associated with the set of educational content. 4. A method of automatically selecting educational content to present to a student and storing information about the student's educational achievement comprising: a) using a set of computer executable instructions stored in local memory on a computer, causing the computer to execute a retrieval module to retrieve a competency record for the student from a database, wherein said competency record comprises a set of data indicating said student's knowledge in a plurality of competencies, and wherein retrieving the competency record for the student comprises storing said set of data in the computer's local memory;b) using the set of computer executable instructions, causing the computer to execute a transition module to determine a set of educational content for said student based on a comparison of a predicted skill level for the student indicated by a first predictive model comprising a nonlinear polynomial student model and a level for said set of educational content in a first competency from said plurality of competencies, wherein said level is retrieved from metadata associated with said set of educational content, wherein the nonlinear polynomial student model: (i) comprises an equation having a degree of at least three, and(ii) indicates the predicted skill level based on time spent by the student in instruction on the first competency;c) using the set of computer executable instructions, causing the computer to execute an evaluation module to dynamically select a plurality of questions assessing knowledge in said first competency for presentation to said student;d) using the set of computer executable instructions, causing the computer to execute a normalization module to perform a cleaning operation and a transformation operation on a set of evaluation data, wherein said set of evaluation data is based on input received from said student in response to the presentation of the plurality of questions;e) using the set of computer executable instructions, causing the computer to execute an update module to modify the set of data stored in the computer's local memory to reflect the set of evaluation data and the cleaning and transformation operations performed by the normalization module and to propagate the modification to the set of data back to the database, wherein the set of evaluation data is used, during presentation of the set of educational content to the student, to update the first predictive model; andf) during presentation of the set of educational content, the updated first predictive model indicating a second predicted skill level for the student. 5. A method of automatically selecting educational content to present to a student and storing information about the student's educational achievement comprising: a) using a set of computer executable instructions stored in local memory on a computer, causing the computer to execute a retrieval module to retrieve a competency record for the student from a database, wherein said competency record comprises a set of data indicating said student's knowledge in a plurality of competencies, and wherein retrieving the competency record for the student comprises storing said set of data in the computer's local memory;b) using the set of computer executable instructions, causing the computer to execute a transition module to determine a set of educational content for said student based on a comparison of a predicted skill level for the student indicated by a first predictive model comprising a nonlinear polynomial student model and a level for said set of educational content in a first competency from said plurality of competencies, wherein said level is retrieved from metadata associated with said set of educational content, wherein the nonlinear polynomial student model: (i) comprises an equation having a degree of at least three, and(ii) indicates the predicted skill level based on time spent by the student in instruction on the first competency;c) using the set of computer executable instructions, causing the computer to execute an evaluation module to dynamically select a plurality of questions assessing knowledge in said first competency for presentation to said student;d) using the set of computer executable instructions, causing the computer to execute a normalization module to perform a cleaning operation and a transformation operation on a set of evaluation data, wherein said set of evaluation data is based on input received from said student in response to the presentation of the plurality of questions;e) using the set of computer executable instructions, causing the computer to execute an update module to modify the set of data stored in the computer's local memory to reflect the set of evaluation data and the cleaning and transformation operations performed by the normalization module and to propagate the modification to the set of data back to the database, wherein the set of evaluation data is used, during presentation of the set of educational content to the student, to update the first predictive model;f) during presentation of the set of educational content, the updated first predictive model indicating a second predicted skill level for the student; andg) determining a second set of educational content to present to the student based on a comparison of a third predicted skill level for the student indicated by a second predictive model and a level for the second set of educational content in a second competency from the plurality of competencies, wherein the first predictive model indicates predicted skill levels for the student in the first competency, and wherein the second predictive model indicates predicted skill levels for the student in the second competency.