System and method for adapting lessons to student needs

• US 10,347,148 B2
• Filed: 07/13/2007
• Issued: 07/09/2019
• Est. Priority Date: 07/14/2006
• Status: Active Grant

• Current Assignee: Dreambox Learning, Inc.
• Original Assignee: Dreambox Learning, Inc.
• Inventors: Slivka, Benjamin W, Gray, Lou, Leban, Roy, Green, Nigel J, Kerns, Daniel R, Smith, Neil, Weary, Mickelle, Cook, Cristopher, Dodge, Cheryl A, Kornfeld, Ronald Anthony
• Primary Examiner(s): Yao, Samchuan C
• Assistant Examiner(s): Ermlick, William D
• Application Number:
  US11/777,998
• Publication Number:
  US 20080038708A1
• Time in Patent Office: 4,379 Days
• Field of Search: 434118, 434322, 434323, 434350, 434353
• CPC Class Codes: G09B 7/08 characterised by modifying the teaching programme in response to a wrong answer, e.g. repeating the question, supplying further information

Abstract

In one embodiment, the invention discloses a method for adapting educational content. The method comprises generating data for each of a plurality of students, the data pertaining to an aspect of the student'"'"'s interaction with an educational system; combining the generated data to form a combined data set; analyzing the combined data set to identify clusters, each representing similar students according to a mathematical model; and adapting the educational system to provide a customized learning experience for a particular student based on an identified cluster.

Claims

1. A method for a computing system to adapt educational content for delivery to a student, the method comprising:

aggregating, with the computing system, data for each of a plurality of students to form a combined data set, the combined data set reflecting how a population of students responded to questions from an educational system including data reflecting steps taken to resolve the questions and whether hints are requested after one or more of the questions is presented, the questions associated with an objective within the educational system that was completed by the population of students;

automatically fitting, with the computing system, the combined data set with one or more mathematical models to generate multiple clusters of students having similar skills, each cluster identified by a grouping of students having similar accuracy in responses to the questions and similar actions taken in responding to the questions, wherein the actions taken in responding to the questions include steps taken to resolve the questions and whether hints are requested after one or more of the questions is presented;

receiving, with the computing system, responses to at least some of the questions and monitoring actions taken in responding to the at least some of the questions from a new student that is working towards the objective within the educational system;

associating, with the computing system, the new student with one of the multiple clusters of students based on the received responses and actions taken in responding to the questions from the new student;

adapting and presenting, with the computing system, questions to the new student based on the cluster of students with which the new student is associated to provide a customized learning experience for the new student; and

predicting an expected response trait when adapting the questions for the new student based on the cluster of students with which the new student is associated.

2. The method of claim 1, further comprising predicting an expected accuracy when resolving the questions for the new student based on the cluster of students that the new student is associated with.

3. The method of claim 1, wherein adapting the questions comprises generating a micro-sequence of lessons to present to the new student.

4. The method of claim 3, wherein the micro-sequence of lessons comprises multiple lessons from which the student can choose a lesson.

5. The method of claim 3, wherein generating the micro-sequence comprises selecting the micro-sequence from a set of micro-sequences assigned to the associated cluster, the selection being based on an evaluation of an effectiveness of the micro-sequences in the set.

6. The method of claim 3, wherein generating said micro-sequence comprises at least one of altering an existing micro-sequence and creating a new micro-sequence.

7. The method of claim 1, wherein adapting the questions comprises at least one of splitting a lesson into multiple lessons, combining multiple lessons into a single lesson, and skipping lessons based on the new student'"'"'s associated cluster.

8. The method of claim 1, further comprising associating the new student with a plurality of the multiple clusters.

9. The method of claim 1, wherein each cluster is further identified by a learning style associated with the students in the cluster.

10. The method of claim 1, wherein each of the multiple clusters is based on a situation-dependent differentiation criterion.

11. The method of claim 10, wherein the situation-dependent differentiation criterion is selected from the group consisting of age, subject matter, gender, school district, and geographic location.

12. The method of claim 1, wherein aggregating the data comprises receiving the data in the form of an event stream for each student.

13. The method of claim 12, wherein the event stream data includes steps taken to resolve each question, a response time for each question, whether a student requested a hint for each question, and derivatives of the response time for each question.

14. The method of claim 12, wherein the event stream comprises events selected from the group consisting of student-generated events, system-generated events, and biometric information pertaining to the student.

15. The method of claim 14, wherein the student-generated events are selected from the group consisting of mouse clicks, pen clicks, pointing-device motion, keystrokes, and requests for help by the student.

16. The method of claim 14, wherein the system-generated events comprise a history of every screen, problem, image, audio, and video element the educational system generated for the student.

17. The method of claim 14, wherein the biometric information is selected from the group consisting of heart rate information, respiration information, eye movement information, and stress level information for the student.

18. The method of claim 1, wherein adapting the questions is performed automatically without human intervention.

19. The method of claim 1, wherein adapting the questions is performed at least in part by a client system that is communicatively coupled to the computing system.

20. The method of claim 1, wherein adapting the questions delivered to the new student comprises adapting a type of question and adapting a manner of delivery of the question.

21. A non-transitory computer-readable medium having instructions which, when executed by a processor of a computing system, cause the computing system to execute a method to adapt educational content for delivery to a student, the method comprising:

aggregating data for each of a plurality of students to form a combined data set, the combined data set reflecting how a population of students responded to questions from an educational system including data reflecting steps taken to resolve the questions and whether hints are requested after one or more of the questions is presented, the questions associated with an objective within the educational system that was completed by the population of students;

automatically fitting the combined data set with one or more mathematical models to generate multiple clusters of students having similar skills, each cluster identified by a grouping of students having similar accuracy in responses to the questions and similar actions taken in responding to the questions, wherein the actions taken in responding to the questions include steps taken to resolve the questions and whether hints are requested after one or more of the questions is presented;

receiving responses to at least some of the questions and monitoring actions taken in responding to the questions when resolving the at least some of the questions from a new student that is working towards the objective within the educational system;

associating the new student with one of the multiple clusters of students based on the received responses and actions taken in responding to the questions from the new student;

adapting and presenting questions to the new student based on the cluster of students with which the new student is associated to provide a customized learning experience for the new student; and

predicting an expected response trait when adapting the questions for the new student based on the cluster of students with which the new student is associated.

22. The computer-readable medium of claim 21, wherein the instructions further cause the computing system to predict an expected accuracy when resolving the questions for the new student based on the cluster of students that the new student is associated with.

23. The computer-readable medium of claim 21, wherein adapting the questions comprises generating a micro-sequence of lessons to present to the new student.

24. The computer-readable medium of claim 23, wherein the micro-sequence of lessons comprises multiple lessons from which the student can choose a lesson.

25. The computer-readable medium of claim 23, wherein generating said micro-sequence comprises selecting the micro-sequence from a set of micro-sequences assigned to the associated cluster, the selection being based on an evaluation of an effectiveness of the micro-sequences in the set.

26. The computer-readable medium of claim 23, wherein generating the micro-sequence comprises at least one of altering an existing micro-sequence and creating a new micro-sequence.

27. The computer-readable medium of claim 21, wherein adapting the questions comprises at least one of splitting a lesson into multiple lessons, combining multiple lessons into a single lesson, and skipping lessons based on the new student'"'"'s associated cluster.

28. The computer-readable medium of claim 21, wherein the instructions further cause the computing system to associate the new student with a plurality of the multiple clusters.

29. The computer-readable medium of claim 21, wherein each cluster corresponds to a learning style associated with the students in the cluster.

30. The computer-readable medium of claim 21, wherein each of the multiple clusters is based on a situation-dependent differentiation criterion.