System for performing assessment without testing

• US 9,824,603 B2
• Filed: 03/23/2016
• Issued: 11/21/2017
• Est. Priority Date: 09/24/2010
• Status: Active Grant

• Current Assignee: Lexia Learning Systems LLC (Rosetta Stone Incorporated), Rosetta Stone Limited (Rosetta Stone Incorporated)
• Original Assignee: Lexia Learning Systems LLC (Rosetta Stone Incorporated)
• Inventors: Brooke, Elizabeth Crawford, Petscher, Yaacov, Schatschneider, Christopher
• Primary Examiner(s): HONG, THOMAS J
• Application Number:
  US15/078,798
• Publication Number:
  US 20160371994A1
• Time in Patent Office: 608 Days
• Field of Search: 434322
• CPC Class Codes: G09B 19/06 Foreign languages with audible presentation of material to be studied G09B5/04
• CPC Class Codes: G09B 5/10 all student stations being capable of presenting the same information simultaneously G09B5/14 takes precedence
• CPC Class Codes: G09B 7/00 Electrically-operated teaching apparatus or devices working with questions and answers mechanically operated G09B3/00; computing arrangements G06F
• 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

A system for educational assessment without testing is provided that includes one or more client systems that are connected to a network allowing students or school officials to communicate with an education framework that performs and manages educational assessment. The one or more client systems issue a message to the education framework requesting a task to be performed. The educational assessment is administered independent of one or more educators so as to avoid interruption of instruction time. A server system receives the message and the education framework proceeds to process the contents of the message. The education framework includes a plurality of programming modules being executed on the server system that provides to educators specific information used for the educational assessment based on the contents of the message. The programming modules assist in calculating and determining one or more parameters for the educational assessment of the students as well as providing specific reports to educators as to the progress of the students.

Claims

1. An apparatus, comprising:

a server system including a processor and a memory, the processor configured to administer an educational assessment at a plurality of client systems via a network connecting the server system to the plurality of client systems, independent of one or more educators so as to avoid interruption of instruction time,

the processor configured to monitor use of a language learning system by each student from a plurality of students during the educational assessment, the processor configured to calculate an average usage time of each student from the plurality of students over a period of time based on (1) that student'"'"'s monitored usage of the language learning system and (2) a plurality of other student performance variables associated with that student,

the processor configured to calculate a comparison value for each student from the plurality of students by comparing the average usage time of that student over the period of time to norm sample data, the processor configured to calculate a performance predictor for each student from the plurality of students and in a timely manner based on the comparison value for that student, the performance predictor for a student from the plurality of students indicating that student'"'"'s chance of meeting a predefined benchmark, the predefined benchmark being based on the norm sample data and indicating a level of instruction intensity for increasing a likelihood that each student from the plurality of students meets the predefined benchmark, the processor configured to, simultaneous to calculating the performance predictor for each student from the plurality of students, generate a skill set identifier associated with each student from the plurality of students, and

the processor configured to generate a report including (1) the performance predictor for each student from the plurality of students and (2) a quantified risk variable associated with each student from the plurality of students, the report including a prescription for each student from the plurality of students, the prescription for each student from the plurality of students including (1) a target average usage time for that student, and (2) available lessons to target at that student based at least in part on the skill set identifier associated with that student.

2. The apparatus of claim 1, wherein each student from the plurality of students performs one or more selected daily skill activities during the educational assessment.

3. The apparatus of claim 1, wherein the processor is configured to analyze a performance, by a student from the plurality of students, of one or more selected daily skill activities during the educational assessment, so as to calculate the plurality of other student performance variables associated with that student.

4. The apparatus of claim 1, wherein the predefined benchmark is calculated based on a percentage of students included in the norm sample that completed each level of one or more selected daily activities of the educational assessment.

5. The apparatus of claim 1, wherein the skill set identifier associated with each student from the plurality of students defines an educational level that that student has achieved by using the language learning system.

6. The apparatus of claim 1, wherein:

the report is customized for a type of educator;

the plurality of students is a plurality of students associated with a first class; and

the report further includes performance predictors associated with each student from a plurality of students associated with a second class when the type of educator is a school administrator.

7. The apparatus of claim 1, wherein each student from the plurality of students is assigned a usage category based on the average usage time of that student over the period of time.

8. A method, comprising:

administering, to a client system connected to a server system via a network, an educational assessment independent of one or more educators, so as to avoid interruption of instruction time;

monitoring use of a language learning system by a student during the educational assessment;

calculating, at the server system, an average usage of the language learning system by the student over a period of time based on (1) the monitored use of the language learning system by the student and (2) a plurality of other student performance variables associated with the student;

calculating, at the server system, a comparison value for the student by comparing the average usage over the period of time to norm sample data;

calculating, at the server system, a performance predictor of the student in a timely manner and based on the comparison value, the performance predictor indicating the student'"'"'s likelihood of meeting a predefined benchmark, the predefined benchmark being based on the norm sample data, the processor configured to, simultaneous to calculating the performance predictor, generate a skill set identifier associated with the student;

generating, at the server system, a report including (1) the performance predictor, (2) a quantified risk variable indicating a risk of failure of the student to meet the predefined benchmark, and (3) a prescription for the student that includes (A) a target average usage for the student, and (B) available lessons to target at the student based at least in part on the skill set identifier associated with the student; and

prescribing, at the server system, a level of intensity of instruction for improving the student'"'"'s likelihood of meeting the predefined benchmark, based on the report.

9. The method of claim 8, wherein the student performs one or more selected daily skill activities during the educational assessment.

10. The method of claim 8, further comprising:

analyzing the performance, by the student, of one or more selected daily skill activities during the educational assessment, and

calculating the plurality of other student performance variables associated with the student based on analysis of the performance of the one or more selected daily skill activities.

11. The method of claim 8, wherein the student performs one or more selected daily skill activities during the educational assessment and the predefined benchmark is calculated based on a percentage of students included in the norm sample data that completed each level of the one or more selected daily skill activities of the educational assessment.

12. The method of claim 8, wherein the skill set identifier of the student defines an educational level that the student has achieved by using the language learning system.

13. The method of claim 8, wherein:

the report is customized for a type of educator;

a first plurality of students is associated with a first class;

the report further includes a performance predictor associated with each student from the first plurality of students when the educator type is a teacher; and

the report further includes a performance predictor associated with each student from the first plurality of students and a performance predictor associated with each student from a second plurality of students associated with a second class when the educator type is a school administrator.

14. The method of claim 8, wherein the student is assigned a usage category based on the average usage of the student over the period of time.

15. A non-transitory processor-readable medium storing code representing instructions to be executed by a processor, the code comprising code to cause the processor to:

administer, to a client system connected to a server system via a network, an educational assessment independent of one or more educators, so as to avoid interruption of instruction time;

monitor, at the server system, use of a language learning system by a student during the educational assessment;

calculate an average usage of the language learning system by the student over a period of time based on (1) the monitored use of the language learning system by the student and (2) a plurality of other student performance variables associated with the student;

calculate, at the server system, a comparison value for the student by comparing the average usage over the period of time to norm sample data,

calculate, at the server system and in a timely manner, a performance predictor of the student based on the comparison value, the performance predictor indicating the student'"'"'s likelihood of meeting a predefined benchmark, the predefined benchmark being based on the norm sample data, the processor configured to, simultaneous to calculating the performance predictor, generate a skill set identifier associated with the student;

generate a report including the performance predictor, a quantified risk variable indicating a risk of failure of the student to meet the predefined benchmark, and a prescription for the student that includes (1) a target average usage time for the student, and (2) available lessons to target at the student based at least in part on the skill set identifier associated with the student; and

prescribe a level of intensity of instruction for improving the student'"'"'s likelihood of meeting the predefined benchmark, based on the report.

16. The non-transitory processor-readable medium of claim 15, wherein each student from a plurality of students including the student performs one or more selected daily skill activities during the educational assessment.

17. The non-transitory processor-readable medium of claim 15, wherein the code further comprises code to cause the processor to:

analyze a performance, by the student, of one or more selected daily skill activities during the educational assessment; and

calculate the plurality of other student performance variables associated with the student based on analysis of the performance of the one or more selected daily skill activities.

18. The non-transitory processor-readable medium of claim 15, wherein the predefined benchmark is calculated based on a percentage of students included in the norm sample that completed each level of one or more selected daily activities of the educational assessment.

19. The non-transitory processor-readable medium of claim 15, wherein the skill set identifier of the student defines an educational level that the student has achieved by using the language learning system.

20. The non-transitory processor-readable medium of claim 15, wherein the student is assigned a usage category based on the average usage time of the student over the period of time.