SYSTEM AND METHOD FOR QUANTIFYING STUDENT'S SCIENTIFIC PROBLEM SOLVING EFFICIENCY AND EFFECTIVENESS

• US 20090075246A1
• Filed: 09/16/2008
• Published: 03/19/2009
• Est. Priority Date: 09/18/2007
• Status: Abandoned Application

• Current Assignee: The Learning Chameleon, Inc.
• Original Assignee: The Learning Chameleon, Inc.
• Inventors: Stevens, Ronald H.
• Application Number:
  US12/211,661
• Publication Number:
  US 20090075246A1
• Time in Patent Office: Days
• US Class Current: 434/322
• CPC Class Codes: G09B 7/02 of the type wherein the student is expected to construct an answer to the question which is presented or wherein the machine gives an answer to the question presented by a student

Abstract

In a computer implemented system and method for analyzing problem solving abilities, analytic models are produced to quantify how students construct, modify and retain problem solving strategies as they learn to solve science problems online. Item response theory modeling is used to provide continually refined estimates of problem solving ability as students solve a series of simulations. In parallel, student'"'"'s strategies are modeled by self-organizing artificial neural network analysis, using the actions that students take during problem solving as the classifying inputs. This results in strategy maps detailing the qualitative and quantitative differences among problem solving approaches. The results are used to provide reports of strategic problem solving competency for a group of students so that teachers can modify teaching strategies to overcome noted deficiencies.

Claims

1. A computer implemented method of evaluating problem solving skills, comprising:

receiving and storing problem solving input data from a group of students for a series of problems attempted by the students, the data including problem solving status and use of online resource items by each student attempting the problems;

analyzing the collected problem solving status data to determine problem solving effectiveness for each problem attempted by each student;

analyzing the collected data on use of online resources for each problem attempted using a trained artificial neural network (ANN), the ANN analysis generating a problem solving efficiency value based on the selection frequency of each online resource item for each problem attempted by each student;

comparing the problem solving effectiveness values to the problem solving efficiency values;

using the comparison to generate a quantitative numeric value (QV) for each student'"'"'s problem solving proficiency for each problem in the series, the QV comprising a combination of problem solving efficiency and problem solving effectiveness;

storing the QV data;

using the stored QV data to generate problem solving reports including a report comparing QV values for all students and all problems, reports comparing QVs on a problem by problem basis, and reports comparing individual student QVs; and

providing the reports online as feedback to supervisors, whereby teaching strategies can be modified for students identified as having low QV scores.

2. The method of claim 1, further comprising receiving problem solving data for successive sets of similar problems from the group of students at predetermined time intervals and comparing QVs over time to generate reports on students'"'"' problem solving progress.

3. The method of claim 1, wherein problem solving effectiveness data is generated using item response theory (IRT) modeling.

4. The method of claim 1, wherein problem solving effectiveness data is generated using problem solution frequency.

5. The method of claim 1, further comprising receiving input data identifying the teacher of each student in the group and associating each stored student QV with the identity of the student'"'"'s teacher, the reports including reports comparing results of each teacher for each problem taught, whereby effective teaching strategies can be identified for teachers having students with high QVs.

6. The method of claim 1, wherein the QVs comprise at least a QV of 1 corresponding to students using few resources and having a low problem solving outcome, a QV of 2 corresponding to students using many resources and having a low problem solving outcome, a QV of 3 corresponding to students using many resources and having a high problem solving outcome, and a QV of 4 corresponding to students using few resources and having a high problem solving outcome.

7. The method of claim 6, wherein the comparison of problem solving effectiveness to problem solving efficiency comprises producing a plot of problem solving efficiency against problem solving rate, and the QVs are generated by dividing the plot into four quadrants separated by two intersecting lines corresponding to the average effectiveness value and average problem solving efficiency for the set of data analyzed, all points in the upper left hand quadrant being assigned a QV of 1, all points in the lower left hand quadrant being assigned a QV of 2, all points in the lower right hand quadrant being assigned a QV of 3, and all points in the upper right hand quadrant being assigned a QV of 4.

8. The method of claim 7, further comprising associating student identifiers with each point in the plot, and providing an output report for students or supervisors based on the plot.

9. The method of claim 7, further comprising associating teacher identifiers with each point in the plot, and providing an output report to students or supervisors based on the plot.

10. The method of claim 1, further comprising comparing student QVs with standardized test scores on a student by student basis, and providing an output report to supervisors, whereby students having high test scores but low problem solving outcomes, or low test scores with high problem solving outcomes can be identified for intervention.

11. The method of claim 1, further comprising collecting sets of student problem solving input data for the same students at predetermined time intervals, each set being associated with a group of problems related the problems in the other sets, generating QVs for each set of data in the series, and using Hidden Markov Modeling (HMM) to generate learning trajectories across the series of student problem solving performances, developing stochastic models of problem solving progress from the learning trajectories across sequential strategic stages in the learning process, and providing student progress reports based on the generated models.

12. A method of analyzing students problem solving ability, comprising

collecting problem solving input data from users for a series of different problems attempted by the students at different time intervals, the data including problem solving outcomes and problem solving resources used by the users in attempting each problem;

processing the collected problem solving outcome data to generate outcome values which indicate problem solving effectiveness;

processing the collected data on use of resources by each user in attempting each problem to generate strategic efficiency values for each user and problem, the strategic efficiency value being based on the resources used by the users in attempting each problem;

comparing the outcome values with the strategic efficiency values;

using the comparison to generate a set of at least four quantitative numeric values (QV scores) representing each student'"'"'s problem solving ability, the lowest QV score comprising user problem solving attempts with a low outcome combined with low use of resources and the highest QV score comprising user problem solving attempts with high outcome combined with low use of resources; and

generating reports which indicate the number of users in each QV score category for each problem attempted, whereby the reports are a measure of user problem solving proficiency and can be used by supervisors to determine effectiveness of teaching strategies and to modify identified teaching strategies associated with low QV scores.

13. The method of claim 12, wherein the outcome values representing problem solving effectiveness are generated using item response theory (IRT) modeling.

14. The method of claim 12, wherein the strategic efficiency values are generated using a trained artificial neural network (ANN).

15. The method of claim 14, further comprising using Hidden Markov Modeling (HMM) to generate learning trajectories from the problem solving effectiveness values and strategic efficiency values.

16. The method of claim 12, wherein the reports comprise pie charts.

17. The method of claim 12, further comprising displaying the reports on a video display output screen.

18. The method of claim 17, wherein the reports are displayed in the form of a dashboard-like image on a computer display screen.

19. The method of claim 17, wherein the reports comprise at least a first report displaying a comparison of QV scores for all users and all problems and a series of second, problem based reports, each second report comprising a comparison of QV scores for all users for a respective problem.

20. The method of claim 19, wherein the reports further comprise a series of individual user performance reports.

21. The method of claim 19, wherein the reports further comprise a series of teacher based reports each displaying QV scores for all users taught by a respective teacher.

22. The method of claim 19, wherein the reports further comprise progress reports which compare QV scores generated for each user for a series of similar problem sets attempted after a series of successive time periods.

23. The method of claim 19, wherein the input data further comprises problem solving input data from groups of users collaborating together to solve problems, and the reports further comprise third reports which compare QV scores for individual users attempting problems with QV scores for collaborative groups attempting problems together.

24. The method of claim 12, wherein the step of comparing the outcome values with the strategic efficiency values comprises plotting the outcome values against the strategic efficiency values, and the step of generating QV scores for each point in the plot comprises dividing the plot into four quadrants separated by the average outcome value and the average strategic efficiency value for the set of data, and assigning each point in an upper left hand quadrant a QV score of one, assigning each point in the lower left hand quadrant a QV score of two, assigning each point in the lower right hand quadrant a QV score of three, and assigning each point in the upper right hand quadrant a QV score of four.

25. A computer implemented problem solving analysis system, comprising:

an input module which receives problem solving input data from students for a series of different problems attempted by the students, the data comprising problem solving outcome data and data on resources used by students in attempting to solve problems;

a data storage module which stores problem solving input data for the students and associated student identifying data;

a central processor which analyzes the stored data, the processor comprising a problem solving effectiveness module which processes the collected problem solving outcome data to generate outcome values representing problem solving effectiveness for each problem and student attempting the problem, a strategic efficiency module which processes the collected data on resources used by students for each problem to produce strategic efficiency values based on problem solving strategies, a comparison module which compares the outcome values with the strategic efficiency values, a quantitative value (QV) generating module which assigns a quantitative numeric value to each problem solving attempt on a student-by-student basis, and a report output module which generates reports comparing QVs for all problems attempted; and

a display module which displays selected QV reports to users of the system on request.