Normalization and Cumulative Analysis of Cognitive Educational Outcome Elements and Related Interactive Report Summaries

• US 20120244510A1
• Filed: 03/21/2012
• Published: 09/27/2012
• Est. Priority Date: 03/22/2011
• Status: Abandoned Application

• Current Assignee: East Carolina University
• Original Assignee: East Carolina University
• Inventors: Watkins, Robert Todd Jr.
• Application Number:
  US13/425,627
• Publication Number:
  US 20120244510A1
• Time in Patent Office: Days
• US Class Current: 434/362
• CPC Class Codes: G09B 7/00 Electrically-operated teaching apparatus or devices working with questions and answers mechanically operated G09B3/00; computing arrangements G06F

Abstract

The systems, methods and associated devices electronically collect, report and generate normalized educational outcome summaries of multiple different educational inputs, including didactic, experiential and problem solving events and/or assessments.

Claims

1. A method for providing data for evaluating student competency, comprising:

generating an evaluation grid for at least one student, the grid including a plurality of different microcompetencies, a plurality of scores that are associated with corresponding ones of the plurality of different microcompetencies, the plurality of scores corresponding to at least one didactic event, at least one experiential event, and at least one discussion event,

wherein generating the evaluation grid is performed using at least one computer processor.

2. The method according to claim 1,

wherein ones of the plurality of scores for corresponding ones of the plurality of different microcompetencies are relative educational value (RVU) scores,

wherein each of the at least one didactic event, the at least one experiential event and the at least one discussion event that is used to generate a respective score is associated with a metadata code identifying a topic code corresponding to ones of the plurality of different microcompetencies and RVU scores, and

wherein generating the evaluation grid is performed using the metadata codes.

3. The method according to claim 1, further comprising:

accumulating RVU scores for different didactic events, experiential events and discussion events, correlated to respective students over time; and

updating the evaluation grid based on the accumulated RVU scores.

4. The method according to claim 3, wherein the grid is automatically electronically updated at a substantially regular periodic interval.

5. The method according to claim 4, wherein the substantially regular periodic interval is at least a weekly interval to reflect changes in student scores corresponding to ones of the plurality of different microcompetencies.

6. The method according to claim 2,

wherein the RVU scores from each event are time-normalized scores, and

wherein the didactic and experiential RVU scores are based on binary characterizations of test and experience events.

7. The method according to claim 2,

wherein the experiential environment RVU scores are based on a pre-defined assessment of difficulty and an estimated time to complete a respective experiential task, and

wherein the experiential task is associated with more than one topic code corresponding to ones of the plurality of different microcompetencies.

8. The method according to claim 2, wherein the discussion environment RVU scores are based on user-defined RVU scores for a student that are assigned after evaluating a student online discussion.

9. The method according to claim 1, wherein the grid is an interactive grid, the method further comprising allowing a user to select a cell in the grid to reveal underlying supporting data of a respective microcompetency and/or student.

10. The method according to claim 1, further comprising displaying the grid with cells in a respective microcompetency having a color that is associated with a defined status.

11. The method according to claim 10, wherein the defined status corresponds to a relative performance of the student among a plurality of other students in a plurality of the students that includes the student.

12. The method according to claim 11, wherein the relative performance is based on a standard deviation of the RVU scores for the plurality of students, wherein cells in the grid in a respective microcompetency are displayed using a first color that corresponds to a score identified as being below a statistically defined minimum, a second color that corresponds to a score that is above the statistically defined minimum and below a statistically defined excellence threshold, and a third color that corresponds to a score that is above the statistically defined excellence threshold.

13. The method according to claim 12, wherein cells in the grid in a respective microcompetency are displayed using the first color that corresponds to a score identified as being below a non-statistically defined minimum exclusive of the statistically defined minimum.

14. The method according to claim 12, wherein cells in the grid in a respective microcompetency are displayed using the third color that corresponds to a score that is above a non-statistically defined excellence threshold exclusive of the statistically defined excellence threshold.

15. A method of providing data for evaluating a student'"'"'s competency in a topic, the method comprising:

obtaining relative educational value unit (RVU) scores for different defined microcompetencies by electronically identifying associated ones of a plurality of metadata codes for a plurality of different microcompetencies that are correlated to student identifiers from didactic, experiential and discussion environments over time; and

storing the obtained RVU scores in association with supporting reports,

wherein at least one of obtaining the RVU scores and storing the RVU scores is performed using at least one processor.

16. The method according to claim 15, further comprising generating a cumulative analysis grid based on the RVU scores.

17. The method according to claim 16, wherein generating the cumulative analysis grid comprises:

mathematically summing RVU scores from each of the didactic, experiential and discussion environments for respective ones of the plurality of different microcompetencies; and

updating the cumulative analysis grid based on subsequently obtained cumulative data for respective students.

18. The method according to claim 15,

wherein didactic RVU scores are based on binary characterizations of test events, and

wherein experiential RVU scores are based on binary characterizations of experiential events.

19. The method according to claim 15, wherein obtaining relative educational value unit (RVU) scores comprises:

receiving an exam data file that corresponds to each didactic event, the exam data file including a unique student identifier, a test item identifier, a microcompetency code corresponding to the test item and a binary answer choice value.

20. The method according to claim 19, further comprising:

modifying the received exam data file to include at least one of a program identifier, an exam date and a course identifier; and

storing the modified exam data file.

21. The method according to claim 20, further comprising programmatically validating the modified exam data file by comparing contents therein with contents of the exam data file.

22. The method according to claim 20, further comprising displaying content of the exam data file for validation by a user.

23. The method according to claim 20, further comprising:

receiving a commitment input; and

responsive to receiving the commitment input, converting data from the modified exam data file into summary data correlated by microcompetency to provide topic-associated results.

24. The method according to claim 23, further comprising:

receiving a validation input that indicates that the summary data correlated by microcompetency is approved; and

responsive to receiving the validation input, generating aggregate data that associates RVU scores corresponding to the summary data with corresponding students.

25. The method according to claim 24, further comprising:

receiving a commitment input that indicates that the aggregate data is approved;

tagging a file corresponding to the aggregate data, the summary data and/or the modified exam data as committed; and

updating a cumulative analysis grid based on RVU scores in the aggregate data.

26. A circuit configured to generate an interactive cumulative grid of a plurality of defined educational topics associated with a cognitive competency of a student based on a plurality of different microcompetencies that are correlated to student identifiers from didactic, experiential and discussion environments over time.

27. A computer program product for providing competency-based student evaluations, the computer program product comprising:

a non-transitory computer readable storage medium having computer readable program code embodied in the medium, the computer-readable program code comprising;

computer readable program code that generates a summative grading output based on an evaluation of didactic test events associated with defined associated microcompetency topic codes and relative educational value units;

computer readable program code that generates a summative grading output based on an evaluation of experiential individual experience elements associated with defined associated microcompetency topic codes and relative educational value units;

computer readable program code that generates a summative grading output based on an evaluation of individual discussion events associated with defined associated microcompetency topic codes and relative educational value units; and

computer readable program code that generates a cumulative analysis student evaluation grid using the summative grading outputs.

28. The computer program product according to claim 27, wherein the cumulative analysis student evaluation grid using the summative grading outputs includes cells in the grid that are displayed in a respective microcompetency having a color that is associated with a defined status.

29. The method according to claim 28,

wherein the defined status corresponds to a relative performance of the student among a plurality of other students in a plurality of the students that includes the student based on a standard deviation of the relative educational value units for the plurality of students, a non-statistically defined minimum that is defined independent of the plurality of other students and a non-statistically defined excellence threshold that is defined independent of the plurality of other students.

30. An educational analysis system, comprising:

at least one web-based service with at least one server that is configured to accept electronic input from professors/teachers and students to communicate with the web-based service to interactively participate in timed discussion events with students and student groups, and wherein the system is configured to provide an input window to allow professors/teacher to input microcompetency codes and relative educational value unit scores for a respective discussion event for each student and each student group participating in the discussion event.

31. The educational analysis system according to claim 30, wherein the at least one web-based service is further configured to use metadata codes to relate defined individual experiential events with an associated one of the microcompetency codes and at least one of the relative educational value units.

32. An educational analysis system comprising:

a computer that comprises;

a processor that is configured to execute computer program code; and

a display device that is configured to display an interactive cumulative competency grid for students.