U.S. patent application number 16/894254 was filed with the patent office on 2020-09-24 for system and method for optimizing an educational study path.
This patent application is currently assigned to Edunav, Inc.. The applicant listed for this patent is Edunav, Inc.. Invention is credited to Justin FALK, Tsakhi SEGAL, Andrey STEPANTSOV.
Application Number | 20200302565 16/894254 |
Document ID | / |
Family ID | 1000004872216 |
Filed Date | 2020-09-24 |
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United States Patent
Application |
20200302565 |
Kind Code |
A1 |
SEGAL; Tsakhi ; et
al. |
September 24, 2020 |
SYSTEM AND METHOD FOR OPTIMIZING AN EDUCATIONAL STUDY PATH
Abstract
A system and method for optimizing a study path based on at
least one goal are presented. The method includes receiving the at
least one goal and at least one constraint; generating a plurality
of potential study paths based on a plurality of courses that are
required for the at least one goal and the at least one constraint;
determining an optimal study path based on the plurality of
potential study paths, the at least one goal, and the at least one
constraint, wherein the optimal study path satisfies the at least
one constraint, the optimal study path is a combination of courses
and their order during a plurality of study terms; and generating
at least one optimal schedule based on the optimal study path.
Inventors: |
SEGAL; Tsakhi; (Cupertino,
CA) ; FALK; Justin; (Sunnyvale, CA) ;
STEPANTSOV; Andrey; (San Jose, CA) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
Edunav, Inc. |
Cupertino |
CA |
US |
|
|
Assignee: |
Edunav, Inc.
Cupertino
CA
|
Family ID: |
1000004872216 |
Appl. No.: |
16/894254 |
Filed: |
June 5, 2020 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
|
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14712565 |
May 14, 2015 |
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16894254 |
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62079578 |
Nov 14, 2014 |
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62102608 |
Jan 13, 2015 |
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Current U.S.
Class: |
1/1 |
Current CPC
Class: |
G06Q 50/2053 20130101;
G06Q 10/10 20130101 |
International
Class: |
G06Q 50/20 20060101
G06Q050/20; G06Q 10/10 20060101 G06Q010/10 |
Claims
1. A method performed at a server for optimizing a study path for a
student based on at least one goal, wherein a study path is a
combination of courses for each of a plurality of semesters,
comprising: obtaining, by the server, the at least one goal and at
least one constraint for the student, wherein each of the at least
one constraint is assigned a respective constraint priority;
receiving, by the server, from a database, information related to a
plurality of courses required for the at least one goal;
generating, by the server, based on the information, a plurality of
potential study paths for the student that comply with the at least
one goal; receiving, by the server, at least one preference of the
student, each of the at least one preference being assigned a
respective preference priority, wherein each constraint priority is
higher than any preference priority; eliminating, by the server,
from the plurality of potential study paths all study paths that do
not meet the at least one constraint in order of the respective
priorities assigned to each of the at least one constraint from
highest priority to lowest priority to obtain a remaining at least
one study path; eliminating, by the server, from the remaining at
least one study path all study paths that do not meet the at least
one preference in order of the respective priorities assigned to
each of the at least one preference from highest priority to lowest
priority to obtain a remaining at least one study path so as to
retain at least one optimal study path; and supplying as an output,
by the server for the student, at least one optimal schedule of
courses for each semester of the at least one optimal study
path.
2. The method of claim 1, further comprising: collecting credential
information, wherein the plurality of potential study paths is
further generated based on the credential information.
3. The method of claim 1, wherein each of the at least one goal is
any of: a profession, an interest area, and a minor.
4. The method of claim 1, wherein generating the plurality of
potential study paths further comprises: performing degree auditing
on each potential study path; and including in the generated
plurality of potential study paths only those potential study paths
that meet the at least one goal based on the degree auditing.
5. The method of claim 1, wherein the at least one constraint is
any of: a total time to complete the at least one goal and an
educational budget.
6. The method of claim 1, wherein each constraint priority is a
relative weight to be accorded to the constraint to which the
constraint priority is assigned and wherein each preference
priority is a relative weight to be accorded to the preference to
which the preference priority is assigned.
7. The method of claim 1, wherein the at least one preference is
any of: favoring a day of the week, disfavoring a day of the week,
a time, online availability, favored instructors, and areas of
interest.
8. The method of claim 1, wherein the at least one optimal schedule
is further based on scheduling information that includes any of:
course hours of each course, days of the week the course is
presented, location of the classes, a start date of each course,
and an end date of each course.
9. The method of claim 1, wherein the information related to a
plurality of courses includes timing information related to at
least one of the courses.
10. The method of claim 9, wherein the timing information includes
at least one of day of a week on which the at least one of the
courses is taught, hours of the day during which the at least one
of the courses is taught, a midterm exam date for the at least one
of the courses, a final exam date for the at least one of the
courses, a start date for the at least one of the courses, and an
end date for the at least one of the courses.
11. A system for planning study paths, comprising: a database; a
processing unit; and a memory, the memory containing instructions
that, when executed by the processing unit, configure the system
to: obtain the at least one goal and at least one constraint for
the student, wherein each of the at least one constraint is
assigned a respective constraint priority; receive from a database,
information related to a plurality of courses required for the at
least one goal; generate based on the information, a plurality of
potential study paths for the student that comply with the at least
one goal; receive at least one preference of the student, each of
the at least one preference being assigned a respective preference
priority, wherein each constraint priority is higher than any
preference priority; eliminate from the plurality of potential
study paths all study paths that do not meet the at least one
constraint in order of the respective priorities assigned to each
of the at least one constraint from highest priority to lowest
priority to obtain a remaining at least one study path; eliminate
from the remaining at least one study path all study paths that do
not meet the at least one preference in order of the respective
priorities assigned to each of the at least one preference from
highest priority to lowest priority to obtain a remaining at least
one study path so as to retain at least one optimal study path; and
supply as an output, for the student, at least one optimal schedule
of courses for each semester of the at least one optimal study
path.
12. The system of claim 11, further comprising: collect credential
information, wherein the plurality of potential study paths is
further generated based on the credential information.
13. The system of claim 11, wherein each of the at least one goal
is any of: a profession, an interest area, and a minor.
14. The system of claim 11, wherein the system, in generating the
plurality of potential study paths, is further configured to:
perform degree auditing on each potential study path; and include
in the generated plurality of potential study paths only those
potential study paths that meet the at least one goal based on the
degree auditing
15. The system of claim 11, wherein the at least one constraint is
any of: a total time to complete the at least one goal and an
educational budget.
16. The system of claim 11, wherein each constraint priority is a
relative weight to be accorded to the constraint to which the
constraint priority is assigned and wherein each preference
priority is a relative weight to be accorded to the preference to
which the preference priority is assigned.
17. The system of claim 11, wherein the at least one preference is
any of: favoring a day of the week, disfavoring a day of the week,
a time, online availability, favored instructors, and areas of
interest.
18. The system of claim 11, wherein the at least one optimal
schedule is further based on scheduling information that includes
any of: course hours of each course, days of the week the course is
presented, location of the classes, a start date of each course,
and an end date of each course.
19. The system of claim 11, wherein the information related to a
plurality of courses includes timing information related to at
least one of the courses.
20. The system of claim 19, wherein the timing information includes
at least one of day of a week on which the at least one of the
courses is taught, hours of the day during which the at least one
of the courses is taught, a midterm exam date for the at least one
of the courses, a final exam date for the at least one of the
courses, a start date for the at least one of the courses, and an
end date for the at least one of the courses.
21. A method performed at a server for optimizing a study path for
a student that comprises a combination of courses for each of a
plurality of semesters, the method comprising: obtaining, by the
server, the at least one goal, at least one constraint and at least
one preference for the student, wherein each of the at least one
constraint and each of the at least one preference is assigned a
respective priority; receiving, by the server, from a database,
information related to a plurality of courses required over a
plurality of semesters for the at least one goal; generating, by
the server, based on the information, a plurality of potential
study paths for the student that comply with the at least one goal;
eliminating, by the server, from the plurality of potential study
paths at least one potential study path using the at least one
constraint and the at least one preference to reach at least one
optimal study path, wherein the elimination of a potential study
path is performed such that when a first constraint of the at least
one constraint having a first priority conflicts with a second
constraint of the at least one constraint having a second priority,
the second priority being lower than the first priority, the first
constraint is utilized first in performance of the elimination,
wherein the elimination of a potential study path is further
performed such that when a first preference of the at least one
preference having a third priority conflicts with a second
preference of the at least one preference having a fourth priority,
the fourth priority being lower than the third priority, then the
first preference is utilized first in performance of the
elimination, and wherein upon determination of a conflict between a
constraint of the at least one constraint and a preference of the
at least one preference then the constraint is utilized first in
performance of the elimination; and supplying as an output, by the
server for the student, the at least one optimal study path, the
optimal study path comprising a schedule of courses for each
semester of the plurality of semesters.
22. The method of claim 21, wherein each of the at least one goal
is any of: a profession, an interest area, and a minor; wherein the
at least one constraint is any of: a total time to complete the at
least one goal and an educational budget; wherein the at least one
preference is any of: favoring a day of the week, disfavoring a day
of the week, a time, online availability, favored instructors, and
areas of interest; and wherein the information related to a
plurality of courses includes timing information related to at
least one of the courses.
23. The method of claim 21, wherein generating the plurality of
potential study paths further comprises: performing degree auditing
on each potential study path; and including in the generated
plurality of potential study paths only those potential study paths
that meet the at least one goal based on the degree auditing.
24. A method performed at a server for optimizing a study path for
a student that comprises a combination of courses for each of a
plurality of semesters, the method comprising: obtaining, by the
server, at least one goal, at least one constraint and at least one
preference for the student, wherein each of the at least one
constraint and each of the at least one preference is assigned a
respective priority, wherein each constraint priority is higher
than any preference priority, and wherein a higher priority is
assigned a lower value than a lower priority; receiving, by the
server, from a database, information related to a plurality of
courses required over a plurality of semesters to meet the at least
one goal, wherein each constraint priority is higher than any
preference priority, and wherein a higher priority is assigned a
lower value than a lower priority; generating, by the server, based
on the information, a plurality of potential study paths for the
student that meet the at least one goal; sorting, by the server,
any of the plurality of potential study paths that meets at least
one of the group comprising at least one of the at least one
constraint and at least one of the at least one preference, into an
alphabetically ordered list of potential study paths based on the
priority of the at least one of the at least one constraint that is
met and the at least one preference that is met, wherein values of
the priorities are used as an alphabet for the alphabetical
ordering; eliminating any potential study path that is not tied for
being first in the alphabetically ordered list of potential study
paths to produce at least one optimal study path; and supplying as
an output, by the server for the student, the at least one optimal
study path, the optimal study path comprising a schedule of courses
for each semester of the plurality of semesters.
25. The method of claim 24, wherein each of the at least one goal
is any of: a profession, an interest area, and a minor; wherein the
at least one constraint is any of: a total time to complete the at
least one goal and an educational budget; wherein the at least one
preference is any of: favoring a day of the week, disfavoring a day
of the week, a time, online availability, favored instructors, and
areas of interest; and wherein the information related to a
plurality of courses includes timing information related to at
least one of the courses.
26. The method of claim 24, wherein generating the plurality of
potential study paths further comprises: performing degree auditing
on each potential study path; and including in the generated
plurality of potential study paths only those potential study paths
that meet the at least one goal based on the degree auditing.
Description
CROSS-REFERENCE TO RELATED APPLICATIONS
[0001] This application claims the benefit of U.S. application Ser.
No. 14/712,565 filed on May 14, 2015 which further claims the
benefit of U.S. Provisional Application No. 62/079,578 filed on
Nov. 14, 2014 and U.S. Provisional Application No. 62/102,608 filed
Jan. 13, 2015. The contents of all of the aforesaid applications
are hereby incorporated by reference.
TECHNICAL FIELD
[0002] The present disclosure relates generally to creating plans
for obtaining educational degrees, and more particularly for
creating degree plans according to selected study paths.
BACKGROUND
[0003] As society moves towards increasingly specialized
professions, there is also a rising need for new job candidates to
achieve higher education requirements. Typically, this includes a
bachelor's degree, or its equivalent, from an accredited higher
education institution. In some countries, such as the United States
of America, higher education can be an expensive choice which,
coupled with financial uncertainty, may prove risky for a
university candidate.
[0004] It is estimated that roughly nineteen billion ($19B) US
Dollars are spent annually on excess credits, which students are
not required to achieve in order to graduate. This excess spending
often occurs due to complicated registration processes and
prerequisite courses which may not be evident early on in a
candidate's choice of a curriculum. While this is a clear
disadvantage for students, it also presents a challenge to
institutions. It is estimated that roughly half of candidates in
the United States do not graduate within 6 years from entering
college.
[0005] One particular issue for ensuring prompt graduations is that
there is no way to efficiently determine the optimal study path for
any particular student from the beginning of college. The number of
potential combinations of courses that any given student may
undergo during his or her course of study is astronomical. Even in
smaller programs with limited numbers of courses offered, the
number of potential combinations of courses over the course of a
multiple year degree can easily be in the millions. As a result,
students may begin taking courses without a clear study path in
mind, thereby leading to undesirable results ranging from cramming
in courses toward the end of their studies to ultimately missing
important classes and delaying graduation. As a result, the
completion time and costs are more than what the students initially
planned for. For example, currently only about 50% of the students
compete their degree within 6 years and about 30% of the community
colleges' students graduate within 3 years.
[0006] Existing solutions for planning study paths are very limited
and in most cases such solutions do not guide or prevent students
from taking incorrect courses. An example for such solution is a
degree auditing that provides a list of requirements for a
student's major or minor such as, for example, required courses,
minimum numbers of credits, research or other non-classroom
requirements, and so on. At best the degree auditing solution would
prevent enrollment of students in courses which the students have
not met the prerequisites for. For example, a student may be
prevented from enrolling in Calculus II if that student has not yet
taken Calculus I.
[0007] The existing solutions do not, however, guide a student's
study path based on broader goals such as a student's career or
educational plans. Moreover, such solutions do not factor in
scheduling per semester based around a student's employment or
other particular requirements. As a result, such solutions are not
capable of ensuring correct curriculum choices for student study
paths based on student goals and other restrictions.
[0008] Furthermore, currently a career advisor or counselor at best
may advise on profession options that may fit the student's
interests. However, due to the number of course combinations and
schools in which a student may obtain the qualifications to
practice the profession, a career advisor cannot provide the
student with an optimal study path that ensures the student's
graduation based on the student's goals and educational
requirements.
[0009] It would therefore be advantageous to provide a solution
that would overcome the deficiencies of the prior art by generating
an optimal study paths.
SUMMARY
[0010] A summary of several example embodiments of the disclosure
follows. This summary is provided for the convenience of the reader
to provide a basic understanding of such embodiments and does not
wholly define the breadth of the disclosure. This summary is not an
extensive overview of all contemplated embodiments, and is intended
to neither identify key or critical elements of all embodiments nor
to delineate the scope of any or all aspects. Its sole purpose is
to present some concepts of one or more embodiments in a simplified
form as a prelude to the more detailed description that is
presented later. For convenience, the term "some embodiments" may
be used herein to refer to a single embodiment or multiple
embodiments of the disclosure.
[0011] Certain embodiments disclosed herein include a method
performed at a server for optimizing a study path for a student
based on at least one goal, wherein a study path is a combination
of courses for each of a plurality of semesters. The method
includes obtaining, by the server, the at least one goal and at
least one constraint for the student, wherein each of the at least
one constraint is assigned a respective constraint priority;
receiving, by the server, from a database, information related to a
plurality of courses required for the at least one goal;
generating, by the server, based on the information, a plurality of
potential study paths for the student that comply with the at least
one goal; receiving, by the server, at least one preference of the
student, each of the at least one preference being assigned a
respective preference priority, wherein each constraint priority is
higher than any preference priority; eliminating, by the server,
from the plurality of potential study paths all study paths that do
not meet the at least one constraint in order of the respective
priorities assigned to each of the at least one constraint from
highest priority to lowest priority to obtain a remaining at least
one study path; eliminating, by the server, from the remaining at
least one study path all study paths that do not meet the at least
one preference in order of the respective priorities assigned to
each of the at least one preference from highest priority to lowest
priority to obtain a remaining at least one study path so as to
retain at least one optimal study path; and supplying as an output,
by the server for the student, at least one optimal schedule of
courses for each semester of the at least one optimal study
path.
[0012] Certain embodiments disclosed herein include a system for
planning study paths. The study system comprises a database; a
processing unit; and a memory, the memory containing instructions
that, when executed by the processing unit, configure the system
to: obtain the at least one goal and at least one constraint for
the student, wherein each of the at least one constraint is
assigned a respective constraint priority; receive from a database,
information related to a plurality of courses required for the at
least one goal; generate based on the information, a plurality of
potential study paths for the student that comply with the at least
one goal; receive at least one preference of the student, each of
the at least one preference being assigned a respective preference
priority, wherein each constraint priority is higher than any
preference priority; eliminate from the plurality of potential
study paths all study paths that do not meet the at least one
constraint in order of the respective priorities assigned to each
of the at least one constraint from highest priority to lowest
priority to obtain a remaining at least one study path; eliminate
from the remaining at least one study path all study paths that do
not meet the at least one preference in order of the respective
priorities assigned to each of the at least one preference from
highest priority to lowest priority to obtain a remaining at least
one study path so as to retain at least one optimal study path; and
supply as an output, for the student, at least one optimal schedule
of courses for each semester of the at least one optimal study
path.
[0013] Certain embodiments disclosed herein include a method
performed at a server for optimizing a study path for a student
that comprises a combination of courses for each of a plurality of
semesters. The method includes obtaining, by the server, the at
least one goal, at least one constraint and at least one preference
for the student, wherein each of the at least one constraint and
each of the at least one preference is assigned a respective
priority; receiving, by the server, from a database, information
related to a plurality of courses required over a plurality of
semesters for the at least one goal; generating, by the server,
based on the information, a plurality of potential study paths for
the student that comply with the at least one goal; eliminating, by
the server, from the plurality of potential study paths at least
one potential study path using the at least one constraint and the
at least one preference to reach at least one optimal study path,
wherein the elimination of a potential study path is performed such
that when a first constraint of the at least one constraint having
a first priority conflicts with a second constraint of the at least
one constraint having a second priority, the second priority being
lower than the first priority, the first constraint is utilized
first in performance of the elimination, wherein the elimination of
a potential study path is further performed such that when a first
preference of the at least one preference having a third priority
conflicts with a second preference of the at least one preference
having a fourth priority, the fourth priority being lower than the
third priority, then the first preference is utilized first in
performance of the elimination, and wherein upon determination of a
conflict between a constraint of the at least one constraint and a
preference of the at least one preference then the constraint is
utilized first in performance of the elimination; and supplying as
an output, by the server for the student, the at least one optimal
study path, the optimal study path comprising a schedule of courses
for each semester of the plurality of semesters.
[0014] Certain embodiments disclosed herein include a method
performed at a server for optimizing a study path for a student
that comprises a combination of courses for each of a plurality of
semesters. The method includes obtaining, by the server, at least
one goal, at least one constraint and at least one preference for
the student, wherein each of the at least one constraint and each
of the at least one preference is assigned a respective priority,
wherein each constraint priority is higher than any preference
priority, and wherein a higher priority is assigned a lower value
than a lower priority; receiving, by the server, from a database,
information related to a plurality of courses required over a
plurality of semesters to meet the at least one goal, wherein each
constraint priority is higher than any preference priority, and
wherein a higher priority is assigned a lower value than a lower
priority; generating, by the server, based on the information, a
plurality of potential study paths for the student that meet the at
least one goal; sorting, by the server, any of the plurality of
potential study paths that meets at least one of the group
comprising at least one of the at least one constraint and at least
one of the at least one preference, into an alphabetically ordered
list of potential study paths based on the priority of the at least
one of the at least one constraint that is met and the at least one
preference that is met, wherein values of the priorities are used
as an alphabet for the alphabetical ordering; eliminating any
potential study path that is not tied for being first in the
alphabetically ordered list of potential study paths to produce at
least one optimal study path; and supplying as an output, by the
server for the student, the at least one optimal study path, the
optimal study path comprising a schedule of courses for each
semester of the plurality of semesters.
BRIEF DESCRIPTION OF THE DRAWINGS
[0015] The subject matter disclosed herein is particularly pointed
out and distinctly claimed in the claims at the conclusion of the
specification. The foregoing and other objects, features, and
advantages of the disclosed embodiments will be apparent from the
following detailed description taken in conjunction with the
accompanying drawings.
[0016] FIG. 1 is a schematic diagram of a system utilized to
describe the various embodiments;
[0017] FIG. 2 is a flowchart illustrating determining potential
study paths based on student goals according to an embodiment;
[0018] FIGS. 3A-3C are screenshots illustrating interactive study
path planning according to an embodiment;
[0019] FIG. 4 is flowchart illustrating optimizing a study path
based on student constraints and preferences according to an
embodiment;
[0020] FIG. 5 is a screenshot illustrating study path optimization
according to an embodiment;
[0021] FIG. 6 is a flowchart illustrating optimizing a course
catalog based on optimized study paths according to an
embodiment;
[0022] FIG. 7 is a screenshot illustrating a an optimized course
catalog generated based on optimized study paths according to an
embodiment;
[0023] FIG. 8 is a flowchart illustrating generating a connected
study path according to an embodiment; and
[0024] FIG. 9 is a screenshot illustrating a connected study path
according to an embodiment.
DETAILED DESCRIPTION
[0025] It is important to note that the embodiments disclosed
herein are only examples of the many advantageous uses of the
innovative teachings herein. In general, statements made in the
specification of the present application do not necessarily limit
any of the various claimed embodiments. Moreover, some statements
may apply to some inventive features but not to others. In general,
unless otherwise indicated, singular elements may be in plural and
vice versa with no loss of generality. In the drawings, like
numerals refer to like parts through several views.
[0026] FIG. 1 is an exemplary and non-limiting schematic diagram of
a system 100 utilized to describe the various disclosed
embodiments. The system 100 includes at least one processing unit
110 such as, for example, a central processing unit (CPU). The
processing unit 110 is coupled via a bus 105 to a memory 120. In an
embodiment, the memory 120 further includes instructions that, when
executed by the processing unit 110, configure the system to
perform the methods described in more detail herein. The memory may
be further used as a working scratch pad for the processing unit
110, a temporary storage, and so on. The memory 120 may be, but is
not limited to, a volatile memory such as random access memory
(RAM), or a non-volatile memory (NVM), such as Flash memory. The
processing unit 110 may be communicatively connected to an input
device 150 for permitting a user to modify a study path or course
catalog. The processing unit 110 may be further communicatively
connected to a database 130. The database 130 may contain
information related to a plurality of courses, such as a catalog
containing each course and each class section of each course. In an
embodiment, the database 130 further includes credential
information of students.
[0027] In an embodiment, the processing unit 110 may comprise, or
be a component of, a larger processing unit implemented with one or
more processors. The one or more processors may be implemented with
any combination of general-purpose microprocessors,
microcontrollers, digital signal processors (DSPs), field
programmable gate array (FPGAs), programmable logic devices (PLDs),
controllers, state machines, gated logic, discrete hardware
components, dedicated hardware finite state machines, or any other
suitable entities that can perform calculations or other
manipulations of information.
[0028] The processing unit 110 and/or the memory 112 may also
include machine-readable media for storing software. Software shall
be construed broadly to mean any type of instructions, whether
referred to as software, firmware, middleware, microcode, hardware
description language, or otherwise. Instructions may include code
(e.g., in source code format, binary code format, executable code
format, or any other suitable format of code). The instructions,
when executed by the one or more processors, cause the processing
system to perform the various functions described herein.
[0029] In some embodiments, the system 100 may be part of a
cloud-computing infrastructure, an application server, a web
server, and the like. In such embodiments, users (e.g., students)
of the system 100 may access the system remotely via, for example,
a web browser.
[0030] FIG. 2 is an exemplary and non-limiting flowchart
illustrating determination of potential study paths towards degrees
based on student goals in accordance with an embodiment. In S210, a
student's credential information is collected. Credential
information relates to a particular student and may include, but is
not limited to, an identification number, a list of courses and/or
credits the student has completed, any remedial course requirements
of the student, a given name, a surname, and so on.
[0031] In S220, at least one goal of a student is received, wherein
the at least one goal is related to the study path to be
determined. A goal of the user may be, but is not limited to, a
profession, a subject in which to major, a subject in which to
minor, or an interest area, and so on. In an embodiment, a user may
select the at least one goal from among several goals. In a further
embodiment, information respective of each goal may be provided to
the user during selection. The information respective of each goal
typically contains information to help a student select a goal that
he or she will be happy with. The information respective of each
goal may be, but is not limited to, average income for a
profession, employability of graduates with a selected major or
minor, a list of professions within an interest area, and so
on.
[0032] In some embodiments, a focus area may be further displayed
respective of each goal. A focus area is an area of interest
related to the user's goal and may be, but is not limited to, a
concentration for a major, a specialty in a profession, and so on.
Selecting goals and information respective thereof are described
further herein below with respect to FIG. 3A.
[0033] In S230, information related to a plurality of courses is
obtained from a database (e.g., the database 130). The information
collected may be based on which courses will be required for the at
least one goal given the student's credential information. For
example, if a first student's major is Mechanical Engineering, the
first student may be required to take courses such as Calculus I,
Thermodynamics, and Mechanics. The plurality of courses may cover a
plurality of study terms. The information may include hours during
which each course is taught (e.g., Mondays from 6 P.M. to 8 P.M.),
a midterm exam date of each course (e.g., October 11), a final exam
date of each course (e.g., December 9), a start date for each
course (e.g., August 28), an end date for each course (e.g.,
December 2), and so on.
[0034] In S240, a plurality of potential study paths is generated
over the plurality of study terms respective of the plurality of
courses. Each study path is a combination of courses and an order
in which to take the courses during the plurality of study terms.
An exemplary and non-limiting study path is described further
herein below with respect to FIG. 3B. The study paths are
determined to ensure that any prerequisite course is taken at least
one term before the student enrolls in an advanced course that
requires the prerequisite course. The study paths may also be
determined to include any remedial classes. In an embodiment, S240
may further include performing degree auditing on each study path.
In an embodiment, any study paths that fail the audit (i.e., do not
make the student eligible to meet his or her goals) are not
included in the generated plurality of study paths.
[0035] In S250, the plurality of study paths may be the saved in a
database for future usage, e.g., to process a request from a
different student with the same goals or to optimize catalogs. In
some embodiments, S250 may further include displaying the plurality
of study paths on a display as a time diagram over the plurality of
study terms. An exemplary and non-limiting time diagram is
described further herein below with respect to FIG. 3B.
[0036] In S260, at least one optimal study path from the plurality
of potential study paths is determined. The optimal study path is
determined based on goals, constraints, and preferences set by the
user. The process for determining the optimal study path is
discussed herein below with respect to in FIG. 4.
[0037] In S270, information of the at least one optimal study path
is displayed to the user. The displayed information may include the
courses for each term, information about each course, prerequisites
for one or more courses, the expected completion time, the expected
budget, and so on. In an embodiment, if the user is satisfied with
the determined optional study path, a registration process may be
initiated.
[0038] FIG. 3A is an exemplary and non-limiting screenshot 300A
illustrating a drop down box featuring a plurality of potential
goals 310 according to an embodiment. A search bar 320 allows a
user to input a goal. In an embodiment, potential search results
may be automatically populated as potential goals 310 in real-time.
In another embodiment, the search results may be based on the type
of goal 330. In the example shown in FIG. 3A, the type of goal
selected is "Profession," and the user has entered "Psyc" into the
search bar 320, thereby triggering a list of potential goals 310
that are professions related to "Psyc."
[0039] FIG. 3B is an exemplary and non-limiting screenshot 300B of
a user interface for displaying a study path in accordance with an
embodiment. In the example shown in FIG. 3B, the profession of
"Clinical Psychologist" was selected as the ultimate career goal,
and two study paths were determined for arriving at that career
goal--a Master of Arts in Psychology for one educational goal, and
a Bachelor of Arts in Psychology for the other educational goal.
The educational goals are seen in the "Goals" box 350. Each study
path includes a plurality of courses 340. In an embodiment,
connections between courses (such as prerequisite courses) may be
shown. The paths begin at the first trimester of 2015, labeled
T1-15. The illustration further shows possible career opportunities
360 and how each path leads to a certain career opportunity.
[0040] FIG. 3C is an exemplary and non-limiting screenshot 300C of
a user interface for displaying a study path in accordance with an
embodiment. In this example, a goal of "Business" major was
selected (as seen in "Goals" box 350), with two possible focus
areas--law and human resources. Based on this goal, a plurality of
courses 340 needed to achieve the goal are displayed. Additionally,
potential careers 360 utilizing the "Business" major and the
possible focus areas are displayed.
[0041] FIG. 4 is an exemplary and non-limiting flowchart S260
illustrating optimizing a study plan by determining at least one
optimal schedule according to an embodiment. In S410, the plurality
of study paths is received. In an embodiment, S410 may further
include performing degree auditing to ensure that the determined
plurality of study paths will result in achieving the student's
goals. In an embodiment, the received plurality of study paths may
be generated as described further herein above with respect to FIG.
2.
[0042] In S420, at least one of a student's constraints and/or
preferences is received. A constraint is a restriction on the study
path that may be, but is not limited to, a total time to complete
education (e.g., 4 years), an educational budget (e.g., $150,000),
and so on. Each constraint may be assigned a priority. If two
constraints conflict, then the constraint with the higher assigned
priority would be utilized first. For example, a student may want
to prioritize completing his degree in 4 years (i.e. a typical
college course load) rather than take summer courses at a lower
cost and completing his degree in 3 years.
[0043] A preference is a particular inclination of the user that
may be used to guide selection of courses when multiple courses can
be utilized to achieve the student's goals. A preference may be,
but is not limited to, favoring or disfavoring particular days of
the week (e.g., favoring classes on Monday through Thursday but
disfavoring classes on Friday), particular times (e.g., not
beginning class before 10 A.M.), whether the course is offered
online, favored instructors, areas of interest (e.g., sports, art,
reading, writing, etc.), and so on. In an embodiment, each
preference may be assigned a priority. If two preferences conflict,
the higher priority preference would be utilized first. In a
further embodiment, all preferences have a lower priority than all
constraints such that, if a constraint and a preference conflict,
the constraint will be utilized first.
[0044] In S430, scheduling information related to a plurality of
courses included in the study plan and offered during a plurality
of study terms are retrieved. The scheduling information may
include days of the week in which a course is presented, location
of a class of the course, hours during which each course is taught
(e.g., Mondays from 6 P.M. to 8 P.M.), a midterm exam date of each
course (e.g., October 11), a final exam date of each course (e.g.,
December 9), a start date for each course (e.g., August 28), an end
date for each course (e.g., December 2), and so on. In an
embodiment, the scheduling information may be retrieved from a
database (e.g., the database 130). The study terms are the
semesters in which the student plans to attend the school (e.g.,
all semesters between Fall of 2015 and May of 2019).
[0045] In S440, at least one optimal study path is computed
respective of the plurality of study paths, the at least one
constraint and/or preference, and the course information. In some
embodiments, S440 includes eliminating from the received study
paths all paths that do not meet the constraints in order of the
constraints' respective priorities. For example, if the first
priority constraint is a completion time and the second priority
constrain is the budget, then first all the paths having a
completion time higher than the input completion time are
eliminated. Then, from the remaining study paths, all paths that
cost more to complete than the input budget are removed. The
remaining set of study paths are sorted or further eliminated based
on the input preferences. For example, if one of the preference
includes disfavoring classes on Friday, then all paths that include
classes on Friday are filtered out. In an embodiment, the remaining
potential study paths are sorted based on their best match to the
at least one preference. The best match study path is the optimal
study path.
[0046] In S450, at least schedule of courses is generated for each
study term based on each of the at least one optimal study
path.
[0047] In S460, the schedule of courses and/or the determined
optimal study path are returned and/or displayed to a user. In one
embodiment, the optimal study path is automatically modified
whenever circumstances change. For example, circumstances may have
changed when a new schedule is published and/or a selected course
will not be delivered in the coming term. Alternatively or
collectively, the determined optimal study path may be
interactively modified by the user as discussed in more detail with
respect to FIG. 8.
[0048] FIG. 5 is an exemplary and non-limiting screenshot 500
illustrating a user interface displaying an optimal schedule,
according to the constraints of having no more than 4 days of
classes and of having Friday and Saturday free of classes.
[0049] FIG. 6 is an exemplary and non-limiting flowchart 600
illustrating generation of an optimized course catalog for an
institution according to an embodiment. In S610, a plurality of
study paths respective of all expected students is retrieved. In an
embodiment, the plurality of study paths may be retrieved from a
database (e.g., the database 130). Each study path may be
determined as described further herein above with respect to FIGS.
2 and 4.
[0050] In S620, an estimated attendance for each term in which the
course is offered is determined. The attendance may be estimated
based on factors such as, but not limited to, expected numbers of
students in the institution during various study terms, a number of
students whose optimal study paths suggest taking the course during
the particular term, and so on.
[0051] In S630, the schedules for all classes provided by the
academic institution are received. In an embodiment, the schedules
are provide by the different departments of the academic
institution. This allows for further optimization the catalog
across different departments. A schedule of a class includes at
least the class hours assigned to each course. Class hours may be,
for example, every other Monday from 4 P.M. to 6 P.M. and Thursdays
from 11 A.M. to 1 P.M. In an embodiment, S630 may further include
determining a number of classes for each course based on at least a
parameter (for example, two sections of the same class). The
parameter may be determined based on the estimated attendance for
the course during the term.
[0052] In S640, the assignment of class hours is optimized to
maximize the number of students who are able to complete their
respective study paths. In an embodiment, optimization may be
performed by minimizing the number of empty seats in each class.
Minimizing the number of empty seats may be performed based on,
e.g., an estimated number of students attending the class, study
paths of students, and comparing the estimated number to a number
of planned seats for the class. A notification may be generated to
add or remove seats for the class respective of the performed
comparison. In an embodiment, optimization is performed by
minimizing the overlap between class hours of courses which are
taken in the same term according to a study path.
[0053] In S650, the optimized assignment is compared to a proposed
assignment. The proposed assignment may be created by a group of
faculty members based on, e.g., faculty availability, classroom
availability, and so on.
[0054] In S660, an optimal course catalog is determined based on
the optimized assignment and the proposed assignment. The catalog
containing each course and each class of each course respective of
the optimized assignment of class hours may be displayed.
[0055] FIG. 7 is an exemplary and non-limiting screenshot 700 of a
user interface displaying a course catalog in accordance with an
embodiment. In screenshot 700, an alert 710 is presented on the
user interface to indicate issues regarding some courses in
"Economics". For example, "ECN 423," a course named "Economics of
Education", currently offers more seats than students anticipated
to attend. It would therefore be a better use of resources to offer
fewer seats in the course "ECN 423."
[0056] FIG. 8 is an exemplary and non-limiting flowchart 800
illustrating providing an interactive display for planning a study
path towards a degree according to an embodiment. In S810, at least
one desired goal is displayed. A goal of the user may be, for
example, a profession, a subject in which to major, a subject in
which to minor, or an interest area. In an embodiment, information
respective of the at least one goal may also be displayed. The
information respective of the at least one goal may be, but is not
limited to, average wage for a profession, employability of
graduates with a selected major or minor, a list of professions
within an interest area, and so on.
[0057] In S820, a plurality of courses for a plurality of study
terms respective of the at least one desired goal is displayed. In
S830, a plurality of courses respective of the at least a desired
goal and the plurality of study terms is displayed. In S840, a
connecting path between the plurality of courses that connects to
at least one of the at least the desired goal is displayed. In
S850, a user modification is received. The user modification may
be, but is not limited to, adding a class, dropping a class, and so
on. In S860, The user modification is incorporated. In an
embodiment, the incorporation may further include adjusting the
optimal study path of the student so as to incorporate the change
while maintaining the user's goals. In an embodiment, the
modification can be in response to changes made to at least one of
the course's schedules. The changes to the schedules are
automatically incorporated in to the study path.
[0058] It should be appreciated that the ability to interactively
change the determined study paths allow the user to trade-off
between various goals prior to and post registration. As a
non-limiting example, the interactive modification allows a user to
easily assess implications of switching a degree (lost credit hours
and delay in graduation).
[0059] FIG. 9 is an exemplary and non-limiting screenshot 900 of a
user interface for providing an interactive display for planning a
study path according to an embodiment. In this exemplary screenshot
900, the student's courses between Fall of 2015 and Fall of 2017
are displayed. Courses 910 making up course schedules are displayed
for each semester. Links 920-1 and 920-2 between courses serve to
visually indicate a prerequisite course and the advanced course
which requires the prerequisite course be completed. In some
embodiments, an advanced course may be linked to a plurality of
prerequisite courses.
[0060] The various embodiments disclosed herein can be implemented
as hardware, firmware, software, or any combination thereof.
Moreover, the software is preferably implemented as an application
program tangibly embodied on a program storage unit or computer
readable medium consisting of parts, or of certain devices and/or a
combination of devices. The application program may be uploaded to,
and executed by, a machine comprising any suitable architecture.
Preferably, the machine is implemented on a computer platform
having hardware such as one or more central processing units
("CPUs"), a memory, and input/output interfaces. The computer
platform may also include an operating system and microinstruction
code. The various processes and functions described herein may be
either part of the microinstruction code or part of the application
program, or any combination thereof, which may be executed by a
CPU, whether or not such a computer or processor is explicitly
shown. In addition, various other peripheral units may be connected
to the computer platform such as an additional data storage unit
and a printing unit. Furthermore, a non-transitory computer
readable medium is any computer readable medium except for a
transitory propagating signal.
[0061] All examples and conditional language recited herein are
intended for pedagogical purposes to aid the reader in
understanding the principles of the disclosed embodiment and the
concepts contributed by the inventor to furthering the art, and are
to be construed as being without limitation to such specifically
recited examples and conditions. Moreover, all statements herein
reciting principles, aspects, and embodiments of the disclosed
embodiments, as well as specific examples thereof, are intended to
encompass both structural and functional equivalents thereof.
Additionally, it is intended that such equivalents include both
currently known equivalents as well as equivalents developed in the
future, i.e., any elements developed that perform the same
function, regardless of structure.
* * * * *