DEVICE, SYSTEM, AND METHOD OF AUTOMATIC ASSESSMENT OF PEDAGOGIC PARAMETERS

• US 20100190142A1
• Filed: 01/28/2009
• Published: 07/29/2010
• Est. Priority Date: 01/28/2009
• Status: Abandoned Application

• Current Assignee: Time To Know Ltd.
• Original Assignee: Time To Know Ltd.
• Inventors: Beker, Tuvia, Gal, Michael
• Application Number:
  US12/360,940
• Publication Number:
  US 20100190142A1
• Time in Patent Office: Days
• US Class Current: 434/322
• CPC Class Codes: G09B 5/00 Electrically-operated educational appliances working with questions and answers G09B7/00; simulators G09B9/00; advertising or displaying in general G09F

Abstract

Device, system, and method of automatic assessment of pedagogic parameters. For example, a method of computer-assisted assessment includes: creating a pre-defined ontology of pedagogic concepts; creating a log of interactions of a student with one or more learning activities, wherein the learning activities are concept-tagged based on said ontology; creating a pedagogic Bayesian network based on said log of interactions and based on said ontology; and based on said pedagogic Bayesian network, estimating a pedagogic parameter related to said student.

Claims

1. A method of computer-assisted assessment, the method comprising:

creating a pre-defined ontology of pedagogic concepts;

creating a log of interactions of a student with one or more learning activities, wherein the learning activities are concept-tagged based on said ontology;

creating a pedagogic Bayesian network based on said log of interactions and based on said ontology; and

based on said pedagogic Bayesian network, estimating a pedagogic parameter related to said student.

2. The method of claim 1, wherein creating the pedagogic Bayesian network comprises:

determining a set of one or more observable pedagogic variables based on one or more observable task performance items reflected in the log of interactions.

3. The method of claim 2, wherein creating the pedagogic Bayesian network further comprises:

determining a set of one or more hidden pedagogic variables related to said one or more observable pedagogic variables.

4. The method of claim 3, wherein the hidden pedagogic variables comprise one or more pedagogic capabilities that the student is required to have in order to successfully accomplish a particular pedagogic task.

5. The method of claim 3, wherein creating the pedagogic Bayesian network further comprises:

determining one or more dependencies among the one or more hidden pedagogic variables.

6. The method of claim 5, comprising:

creating a set of one or more conditional distribution functions corresponding to an estimation of the probability of possible values for substantially each one of the hidden pedagogic variables.

7. The method of claim 6, wherein the set of one or more conditional distribution functions has at least three possible values corresponding to a strong value, a medium value, and a weak value, and wherein the sum of probability of the three possible values equals to substantially one.

8. The method of claim 6, comprising:

based on analysis of newly-received observable task performance items reflected in the log of interactions, modifying the probability assigned to at least one of the possible values of the set of one or more conditional distribution functions.

9. The method of claim 8, comprising:

determining a weighted pedagogic score corresponding to said set of one or more conditional distribution function, based on the sum of weights of scores corresponding to said possible values.

10. The method of claim 8, comprising:

generating a report indicating pedagogic progress of at least one of;

a student, a group of students, and a class of students.

11. The method of claim 8, comprising:

generating an alert indicating a discrepancy between an expected pedagogic parameter of a student and an assessed pedagogic parameter of said student.

12. The method of claim 1, wherein the pedagogic Bayesian network is further based on a teacher input indicating at least one of:

a known strength of said student; and

a known weakness of said student.

13. The method of claim 1, wherein creating the pedagogic Bayesian network is comprised within an algorithm which creates one or more statistically evolving models based on relational concept mapping.

14. The method of claim 1, wherein creating the pedagogic Bayesian network comprises creating a dynamic pedagogic Bayesian network;

wherein a plurality of copies of the dynamic pedagogic Bayesian network represent a model of said student at a plurality of interconnected time points; and

wherein estimating the pedagogic parameter is based on said dynamic pedagogic Bayesian network.

15. The method of claim 1, wherein creating the pedagogic Bayesian network comprises creating a hierarchical pedagogic Bayesian network including at least one dependency across two pedagogic domains.

16. The method of claim 1, wherein one or more priors of the pedagogic Bayesian network are dynamically modified based on an analysis which takes into account:

metadata of said student, metadata of said one or more learning activities, and activity log of said student.

17. The method of claim 1, comprising:

verifying the pedagogic Bayesian network by at least one of;

utilization of controlled simulated student-related data; and

utilization of input from a manual assessment process.

18. A system for adaptive learning and teaching, the system comprising:

a repository to store a pre-defined ontology of pedagogic concepts; and

a computer-aided assessment module to create a log of interactions of a student with one or more learning activities, wherein the learning activities are concept-tagged based on said ontology;

to create a pedagogic Bayesian network based on said log of interactions and based on said ontology; and

based on said pedagogic Bayesian network, to estimate a pedagogic parameter related to said student.

19. The system of claim 18, wherein the computer-aided assessment module is to determine a set of one or more observable pedagogic variables based on one or more observable task performance items reflected in the log of interactions.

20. The system of claim 19, wherein the computer-aided assessment module is to determine a set of one or more hidden pedagogic variables related to said one or more observable pedagogic variables.

21. The system of claim 20, wherein the hidden pedagogic variables comprise one or more pedagogic capabilities that the student is required to have in order to successfully accomplish a particular pedagogic task.

22. The system of claim 20, wherein the computer-aided assessment module is to determine one or more dependencies among the one or more hidden pedagogic variables.

23. The system of claim 22, wherein the computer-aided assessment module is to create a set of one or more conditional distribution functions corresponding to an estimation of the probability of possible values for substantially each one of the hidden pedagogic variables.

24. The system of claim 23, wherein the set of one or more conditional distribution functions has at least three possible values corresponding to a strong value, a medium value, and a weak value, and wherein the sum of the probabilities of the three possible values equals to substantially one.

25. The system of claim 23, wherein, based on analysis of newly-received observable task performance items reflected in the log of interactions, the computer-aided assessment module is to modify at least one of the probabilities of the possible values of the set of one or more conditional distribution functions.

26. The system of claim 25, wherein the computer-aided assessment module is to determine a weighted pedagogic score corresponding to said set of one or more conditional distribution functions, based on the sum of weights of scores corresponding to said possible values.

27. The system of claim 25, comprising:

a report generator to generate a report indicating pedagogic progress of at least one of;

a student, a group of students, and a class of students.

28. The system of claim 25, comprising:

an alert generator to generate an alert indicating a discrepancy between an expected pedagogic parameter of a student and an assessed pedagogic parameter of said student.

29. The system of claim 18, wherein the pedagogic Bayesian network is further based on a teacher input indicating at least one of:

a known strength of said student; and

a known weakness of said student.

30. The system of claim 18, wherein the computer-aided assessment module is to create the pedagogic Bayesian network in conjunction with an algorithm which creates one or more statistically evolving models based on relational concept mapping.

31. The system of claim 18, wherein the computer-aided assessment module is to create a dynamic pedagogic Bayesian network;

wherein a plurality of copies of the dynamic pedagogic Bayesian network represent a model of said student at a plurality of interconnected time points; and

wherein the computer-aided assessment module is to estimate the pedagogic parameter based on said dynamic pedagogic Bayesian network.

32. The system of claim 18, wherein the computer-aided assessment module is to create a hierarchical pedagogic Bayesian network including at least one dependency across two pedagogic domains.

33. The system of claim 18, wherein the computer-aided assessment module is to dynamically modify one or more priors of the pedagogic Bayesian network based on an analysis which takes into account:

metadata of said student, metadata of said one or more learning activities, and activity log of said student.

34. The system of claim 18, wherein the computer-aided assessment module is to verify the pedagogic Bayesian network by at least one of:

utilization of controlled simulated student-related data; and

utilization of input from a manual assessment process.