Method and apparatus for advancing through a deck of digital flashcards

• US 10,699,271 B2
• Filed: 11/12/2015
• Issued: 06/30/2020
• Est. Priority Date: 01/31/2015
• Status: Active Grant

• Current Assignee: USA Life Nutrition, LLC
• Original Assignee: USA Life Nutrition, LLC
• Inventors: Shea, Ronald Raymond
• Primary Examiner(s): Yip, Jack
• Application Number:
  US14/939,553
• Publication Number:
  US 20160225272A1
• Time in Patent Office: 1,692 Days
• CPC Class Codes: G06Q 10/0639 Performance analysis of employees; Performance analysis of enterprise or organisation operations
• CPC Class Codes: G06Q 20/10 specially adapted for electronic funds transfer [EFT] systems; specially adapted for home banking systems
• CPC Class Codes: G06Q 20/40 Authorisation, e.g. identification of payer or payee, verification of customer or shop credentials; Review and approval of payers, e.g. check credit lines or negative lists
• CPC Class Codes: G06Q 30/0207 Discounts or incentives, e.g. coupons or rebates
• CPC Class Codes: G06Q 50/20 Education
• CPC Class Codes: G06Q 50/205 Education administration or guidance
• CPC Class Codes: G09B 19/00 Teaching not covered by other main groups of this subclass teaching or practice apparatus for gun-aiming or gun-laying F41G3/26
• CPC Class Codes: G09B 5/06 with both visual and audible presentation of the material to be studied
• CPC Class Codes: G09B 5/08 providing for individual presentation of information to a plurality of student stations
• 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
• CPC Class Codes: G09B 7/06 of the multiple-choice answer-type, i.e. where a given question is provided with a series of answers and a choice has to be made from the answers

Abstract

Parents and entities incentivize students to learn using pledges of cash, goods, and services. Students earn incentivized rewards by interacting with lessons on a digital computing device, such as a desktop computer, notebook, tablet, or “smart phone.” The lessons may include digital flash cards, text, pictures, figures, audio and video recordings ranging from classroom lectures, to classical music, to animation of scientific principles. In flashcard embodiments, the accuracy of each answer is recorded on a histogram corresponding to a particular flashcard, or the principles taught therein. The application generates performance metrics from histogram data. The performance metrics are used to generate control parameters that control the frequency and order flashcard presentation. Statistical analysis, adaptive learning and/or artificial intelligence programs refine the control parameters governing flashcard review, thereby optimizing the functionality. As a student satisfies various performance metrics and/or courseware requirements, incentives are released to the student.

Claims

1. A method for selecting from among a first plurality of digital flashcards, a first digital flashcard for review by a first student on a digital computing device, at a time calculated to optimize the first student'"'"'s retention of material described in the first digital flashcard, the method comprising:

a) recording, on a dedicated digital histogram logically related to a first digital flashcard, histogram data derived from at least five review cycles of the first digital flashcard by the first student, wherein a first review cycle includes,

i) presenting, to the first student, the first digital flashcard;

ii) providing, by the first student, an answer to said first digital flashcard;

iii) recording, in a first histogram entry of the dedicated digital histogram, a digital value representing an accuracy of said answer; and

,

iv) recording, in the dedicated digital histogram, in logical relationship to the digital value representing the accuracy of said answer, a first digital timestamp corresponding to a time and date of said answer;

b) selecting a threshold probability of the first student correctly answering the first digital flashcard;

c) calculating, through an optimal predictive model on a digital computing device, a future time at which a probability of the first student correctly answering the first digital flashcard falls below the threshold probability, wherein said optimal predictive model analyzes input data, including, but not limited to, said histogram data; and

,

d) queueing the first digital flashcard for presentation to the first student after the probability of the first student correctly answering the first digital flashcard has fallen below the threshold probability.

2. The method according to claim 1, wherein a probability of the first student correctly answering the first digital flashcard is measured on a scale having at least one hundred gradations of accuracy.

3. The method of claim 1, wherein the optimal predictive model is part of an analytic process that utilizes digital tools selected from among a group of digital tools consisting of an equation, a data table, a logical flow chart, and combinations thereof.

4. The method of claim 1, wherein the threshold probability is a user adjustable probability.

5. The method according to claim 1 further comprising the step of modifying the optimal predictive model by an offset to compensate for a circumstance selected from among a group of circumstances consisting of a difficulty of a specific flashcard, a difficulty of a subject, a performance history of the first student, a native language of the first student, demographic features of the first student, and combinations thereof.

6. The method of claim 5 wherein the offset is selected from among a group consisting of constants, variables, weighted coefficients, mathematical operations, logical flow charts, and combinations thereof.

7. This method of claim 1, wherein a probability of the first student correctly answering the first digital flashcard degrades as a function of time.

8. The method according to claim 7, further comprising the step of multiplying at least some of the data derived from the first review cycle of the first digital flashcard, by a value inversely proportional to a length of time from a timestamp of the first review cycle «

t₁»

to a future time «

t_F»

for which a probability of the first student correctly answering the first digital flashcard is being calculated.

9. The method according to claim 8, further comprising the step of calculating a durability value Δ

₁ of the first student'"'"'s answer during the first review cycle according to the formula;

«

Δ

₁»

∝

«

t₁»

−

«

t₂»

wherein «

t₁»

represents the first digital timestamp corresponding to the first review cycle, «

t₂»

represents a second digital timestamp corresponding to a second review cycle that preceded the first review cycle, and wherein the answer provided by the first student in the first review cycle was a correct answer.

10. The method according to claim 8, further comprising the step of calculating, from data from data stored in the first histogram entry of the dedicated digital histogram, a first freshness value «

F₁»

wherein;

a) the answer provided by the student during the first review cycle was incorrect; and

,

b) the first freshness value «

F₁»

is a positive number.

11. The method according to claim 8, further comprising the step of calculating, from data stored in the first histogram entry of the dedicated digital histogram, a first accuracy value «

Δ

₁»

, wherein;

a) the answer provided by the student during the first review cycle was incorrect; and

,

b) the first accuracy value «

A₁»

is a negative number.

12. The method of claim 1, wherein the optimal predictive model is determined by a process comprising the steps:

a) generating a plurality of predictive models;

b) quantifying an accuracy of each of the plurality of predictive models; and

c) selecting, as the optimal predictive model, a predictive model with a higher quantitative accuracy than other predictive models.

13. The method of claim 12, wherein the step of quantifying a predictive accuracy of each of the plurality of predictive models includes calculating a predictive accuracy for at least a portion of each of the plurality of predictive models using a statistical technique selected from among a group of statistical techniques consisting of least squares, standard deviation, line of best fit, regression analysis, quantifying a linearity of a graph, and combinations thereof.

14. The method of claim 1, further comprising the steps:

a) recording, in a compound digital histogram that is digitally linked to at least two digital flashcards, histogram data relating to the first review cycle of the first digital flashcard by the first student; and

,

b) recording, in said compound digital histogram, histogram data relating to a review cycle of a second digital flashcard by the first student.

15. A method for presenting a digital flashcard for review by a first student on a digital computing device, at a future time calculated to optimize the first student'"'"'s retention of material described in the first digital flashcard, the method comprising:

a) recording, in a dedicated digital histogram logically related to a first digital flashcard, digital histogram data relating to at least five review cycles of said first digital flashcard by the first student, wherein each entry of digital histogram data includes a value representing an accuracy of an answer by the first student in logical relationship to a digital timestamp corresponding to a time and date of a respective review cycle, a first review cycle comprising the steps,

i) presenting, to the first student, the first digital flashcard;

ii) providing, by the first student, a first answer for the first flashcard;

iii) determining an accuracy of the first answer;

iv) determining a digital value corresponding to the accuracy of the first answer;

v) inputting, into a first histogram entry of the dedicated digital histogram, input data relating to the first review cycle, including the value corresponding to the accuracy of the first answer, and a first digital timestamp corresponding to a date and time of the first answer;

b) analyzing, through an optimal predictive model, input data, including but not limited to said digital histogram data;

c) determining, through said optimal predictive model, an optimal time for presenting said first digital flashcard to said first student; and

,

d) presenting said first digital flashcard to said first student at said optimal time.

16. The method of claim 15, wherein step of determining, through said optimal predictive model, an optimal time for presenting said first digital flashcard to said first student, comprises the steps:

a) selecting a minimum acceptable threshold probability of the first student correctly answering the first digital flashcard; and

,

b) calculating, through the optimal predictive model, a time at which a probability of the first student correctly answering the first digital flashcard falls below the minimum acceptable threshold probability.

17. The method of claim 16, wherein said optimal predictive model is determined by a process comprising the steps:

a) generating a plurality of predictive models;

b) quantifying an accuracy of at least some of the plurality of predictive models; and

c) selecting, as the optimal predictive model, a predictive model with a highest quantitative accuracy in comparison with other predictive models.

18. The method of claim 17, wherein the step of quantifying an accuracy of at least some of the plurality of predictive models comprises using a statistical technique selected from among a group of statistical techniques consisting of least squares, standard deviation, line of best fit, regression analysis, quantifying a linearity of a graph, and combinations thereof.

19. The method of claim 16, wherein the threshold probability is a user adjustable probability.

20. The method of claim 16, wherein a probability of the first student correctly answering the first digital flashcard is measured on a scale having a range of at least one hundred gradations of accuracy.

21. The method of claim 16, wherein a probability of the first student correctly answering the first digital flashcard degrades as a function of time.

22. The method of claim 15, wherein the optimal predictive model utilizes digital tools selected from among a group of digital tools consisting of an equation, a data table, a logical flow chart, and combinations thereof.

23. The method according to claim 15, wherein the optimal predictive model includes an offset to compensate for a circumstance selected from among a group of circumstances consisting of a difficulty of a flashcard, a difficulty of a subject, a performance history of a student, a native language of a student, demographic features of a student, and combinations thereof.

24. The method of claim 15, further comprising the steps:

a) recording, in a compound digital histogram that is digitally linked to at least two digital flashcards, histogram data relating to a review cycle of said first digital flashcard by the first student; and

,

b) recording, in the compound digital histogram, histogram data relating to a review cycle of a second digital flashcard by the first student.

25. The method according to claim 15 further comprising the step of multiplying at least some data derived, in part, from the first review cycle of the first digital flashcard, by a value inversely proportional to a length of time from a timestamp of the first review cycle «

t₁»

to a future time «

t_F»

for which a probability of the first student correctly answering the first digital flashcard is being calculated.

26. The method according to claim 25, further comprising the step of calculating a durability value Δ

₁ of the first student'"'"'s answer during the first review cycle according to the formula;

«

Δ

₁»

∝

«

t₁»

−

«

t₂»

wherein «

t₁»

represents the first digital timestamp relating to the first review cycle, «

t₂»

represents a second digital timestamp relating to a second review cycle that preceded the first review cycle, and wherein the answer provided by the first student in the first review cycle was a correct answer.

27. The method according to claim 25, further comprising the step of calculating, from data from data stored in the first histogram entry of the dedicated digital histogram, a first freshness value «

F₁»

wherein;

a) the answer provided by the student during the first review cycle was incorrect; and

,

b) the first freshness value «

F₁»

is a positive number.

28. The method according to claim 25, further comprising the step of calculating, from data from data stored in the first histogram entry of the dedicated digital histogram, a first accuracy value «

A₁»

wherein;

a) the answer provided by the student during the first review cycle was incorrect; and

,

b) the first accuracy value «

A₁»

is a negative number.