U.S. patent application number 15/989527 was filed with the patent office on 2018-09-27 for systems and methods for motivation-based course selection.
The applicant listed for this patent is D2L Corporation. Invention is credited to Jeremy Auger.
Application Number | 20180276205 15/989527 |
Document ID | / |
Family ID | 56690495 |
Filed Date | 2018-09-27 |
United States Patent
Application |
20180276205 |
Kind Code |
A1 |
Auger; Jeremy |
September 27, 2018 |
SYSTEMS AND METHODS FOR MOTIVATION-BASED COURSE SELECTION
Abstract
An electronic method for course selection. The method includes
identifying at least one user motivation associated with at least
one user, identifying at least one course recommendation based on
the at least one user motivation, and displaying the at least one
course recommendation to the user on a display device. In some
cases the method may include receiving an input from the user
associated with the at least one course recommendation. The method
may also include enrolling the user in a course based on the input
received in association with the course recommendation.
Inventors: |
Auger; Jeremy; (Breslau,
CA) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
D2L Corporation |
Kitchener |
|
CA |
|
|
Family ID: |
56690495 |
Appl. No.: |
15/989527 |
Filed: |
May 25, 2018 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
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14629059 |
Feb 23, 2015 |
9984073 |
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15989527 |
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Current U.S.
Class: |
1/1 |
Current CPC
Class: |
G09B 5/00 20130101; G06Q
50/205 20130101; G06F 16/00 20190101; G06Q 30/02 20130101 |
International
Class: |
G06F 17/30 20060101
G06F017/30; G06Q 30/02 20060101 G06Q030/02; G06Q 50/20 20060101
G06Q050/20; G09B 5/00 20060101 G09B005/00 |
Claims
1. An electronic method for course selection, comprising:
identifying at least one user motivation associated with at least
one user; identifying at least one course recommendation based on
the at least one user motivation; and displaying the at least one
course recommendation to the user on a display device.
2. The method of claim 1, further comprising receiving an input
from the user associated with the at least one course
recommendation.
3. The method of claim 2 further comprising enrolling the user in a
course based on the input received in association with the course
recommendation.
4. The method of claim 2, wherein, if the input is a decision to
ignore a particular course, then removing that particular course
from the display and updating the at least one course
recommendation displayed to the user.
5. The method of claim 1, wherein the course recommendation
includes identifying a recommended course for that user to
enroll.
6. The method of claim 1, wherein the course recommendation
includes identifying a course for that user to avoid enrolling
in.
7. The method of claim 1, wherein the user motivation includes an
explicit user motivation.
8. The method of claim 7 wherein the explicit user motivation
includes an identified user interest.
9. The method of claim 7 wherein the explicit user motivation
includes a user employment objective.
10. The method of claim 7 wherein the explicit user motivation
includes a user educational objective.
11. The method of claim 7 wherein the explicit user motivation
includes an identified social relationship.
12. The method of claim 1 wherein the user motivation includes an
inferred user motivation.
13. The method of claim 12 wherein the inferred user motivation is
based on historical data about the user.
14. The method of claim 12 wherein the inferred user motivation is
based on behavioral data for that user.
15. The method of claim 1, wherein the at least one user motivation
includes a plurality of user motivations, and the user motivations
are weighted differently.
16. The method of claim 15 wherein the plurality of user
motivations are weighted differently depending on whether the user
motivations are explicit or inferred.
17. The method of claim 1 wherein the at least one course
recommendation is identified based on information received about at
least one course.
18. The method of claim 17, wherein the information received about
the at least one course includes information manually associated
with the at least one course.
19. The method of claim 18 wherein the information manually
associated with the at least one course includes at least one of a
title and meta-data.
20. The method of claim 17, wherein the information received about
the at least one course includes information automatically
determined about the at least one course by analyzing data
associated with the at least one course.
Description
FIELD
[0001] Various embodiments are described herein that generally
relate to systems and methods for educational course selection by a
user, and in particular to recommending courses for selection by a
user based on user motivations.
INTRODUCTION
[0002] Electronic learning (also called e-Learning or eLearning)
generally refers to education or learning where users engage in
education related activities using computers and other computer
devices. For examples, users may enroll or participate in a course
or program of study offered by an educational institution (e.g., a
college, university or grade school) through a web interface that
is accessible over the Internet. Similarly, users may receive
assignments electronically, participate in group work and projects
by collaborating online, and be graded based on assignments and
examinations that are submitted using an electronic dropbox.
[0003] Electronic learning is not limited to use by educational
institutions, however, and may also be used in governments or in
corporate environments. For example, employees at a regional branch
office of a particular company may use electronic learning to
participate in a training course offered by their company's head
office without ever physically leaving the branch office.
[0004] Electronic learning often occurs without any face-to-face
interaction between users in an educational community. Accordingly,
electronic learning overcomes some of the geographic limitations
associated with more traditional learning methods, and may
eliminate or greatly reduce travel and relocation requirements
imposed on users of educational services. Furthermore, because
course materials can be offered and consumed electronically, there
are fewer physical restrictions on learning. For example, the
number of students that can be enrolled in a particular course may
be practically limitless, as there may be no requirement for
physical facilities to house the students during lectures.
Furthermore, learning materials (e.g., handouts, textbooks, etc.)
may be provided in electronic formats so that they can be
reproduced a virtually unlimited number of students. Finally,
lectures may be recorded and accessed at varying times
(particularly at different times that are convenient for different
users), thus accommodating users with varying schedules, and
allowing users to be enrolled in multiple courses that might have a
scheduling conflict when offered using traditional techniques.
[0005] Users of electronic learning systems (as well as traditional
"brick and mortar") institutions normally have some ability to
select the courses to enroll in. Selecting courses can be a
difficult experience, and there are often many factors that can
influence a user's decision about whether or not to enroll in a
particular course.
DRAWINGS
[0006] For a better understanding of the various embodiments
described herein, and to show more clearly how these various
embodiments may be carried into effect, reference will be made, by
way of example, to the accompanying drawings which show at least
one example embodiment, and in which:
[0007] FIG. 1 is a block diagram illustrating an example embodiment
of an educational system for providing electronic learning that
incorporates a recommendation engine according to one
embodiment;
[0008] FIG. 2 is a block diagram illustrating the recommendation
engine shown in FIG. 1;
[0009] FIG. 3 is a block diagram illustrating an example of an
output from the recommendation engine shown in FIG. 2;
[0010] FIG. 4 is a block diagram illustrating another example
output from the recommendation engine shown in FIG. 2; and
[0011] FIG. 5 is a flow chart illustrating an exemplary method for
automatically recommending courses for selection by a user
according to one embodiment.
DESCRIPTION OF VARIOUS EMBODIMENTS
[0012] Various systems and methods will be described below to
provide exemplary embodiments. In general, no embodiment described
below limits any particular claim, and any claim may cover methods
or systems that differ from those described below. The claims are
not limited to methods or systems having all of the features of any
one system or method described below, or to features common to some
or all of the systems or methods described below. It is possible
that a system or method described below is not an embodiment of any
claim.
[0013] Any embodiment disclosed in a system or method described
below that is not claimed in this document may be the subject
matter of another protective instrument, for example, a continuing
patent application, and the applicants, inventors and/or owners do
not intend to abandon, disclaim or dedicate to the public any such
embodiment simply by disclosure in this document.
[0014] It will be appreciated that for simplicity and clarity of
illustration, where considered appropriate, reference numerals may
be repeated among the figures to indicate corresponding or
analogous elements. In addition, numerous specific details are set
forth in order to provide a thorough understanding of the
embodiments described herein. However, it will be understood by
those of ordinary skill in the art that the embodiments described
herein may be practiced without these specific details. In other
instances, well-known methods, procedures and components have not
been described in detail so as not to obscure the embodiments
described herein.
[0015] Some embodiments of the systems and methods described herein
may be implemented in hardware or software, or a combination of
both. For example, some embodiments may be implemented in computer
systems and computer programs, which may be stored on a physical
computer readable medium (particularly a non-transitory computer
readable medium), executable on programmable computers (e.g.,
computing devices and/or processing devices) that each comprise at
least one processor, a data storage system (including volatile and
non-volatile memory and/or storage elements), at least one input
device (e.g., a keyboard, mouse or touchscreen), and at least one
output device (e.g., a display screen, a network, or a remote
server).
[0016] For example, and without limitation, the programmable
computers may include servers, personal computers, laptops,
tablets, personal data assistants (PDA), cell phones, smart phones,
gaming devices, and other mobile devices. Program code can be
applied to input data to perform the functions described herein and
to generate output information. The output information can then be
supplied to one or more output devices for outputting to one or
more users.
[0017] In most learning environments, including e-Learning
environments, users will often have the ability to select
particular courses they would like to take. These courses are often
called "electives" in that the student will have some freedom to
elect to enroll in a course.
[0018] In some cases, a user may have a limited number of electives
available, often when their program of instruction has a
significant number of required courses (for instance, an
engineering student in a first year engineering program may have
very few electives). In other cases, users may have a large number
of elective courses to pick from. For example, senior students in
an engineering program or students in a humanities program may have
significantly higher numbers of electives available.
[0019] In any case, selecting particular courses to enroll in can
be a difficult experience for a user, particularly since there are
many factors that can influence a user's choices. One important
factor to most students is academic performance. Specifically, most
students would like to do well in a particular course, and thus may
be influenced to select a course based on their expected
performance in that course.
[0020] One technique for assisting users in their course selection
is to use a recommendation engine. A recommendation engine can
provide guidance to a user and recommend courses that may be
suitable for that user. For instance, a recommendation engine may
receive historical information about a student's performance in
previous courses that they have taken (i.e., overall grades,
results from midterms, finals, and other assessments). The
recommendation can also receive information about the courses that
are available for enrollment (i.e., which courses are difficult or
have low average grades, which courses are easy or have high
average grades, etc.).
[0021] Based on this information, the recommendation engine can
then suggest courses in which the user may be particularly
successful from an academic perspective. For example, if the user
has struggled with science based courses but has received high
grades in humanities based courses, the recommendation engine may
encourage the user to select history or geography based courses, as
opposed to physics and chemistry based courses.
[0022] This approach tends to be successful at ensuring good
academic performance, and may in particular be helpful at
preventing a user from failing a course. Unfortunately, however,
this approach really only focuses on academic success. As a result,
at the end of a particular program, a student may have received
high grades in their courses (based on those courses being easy
courses, or based on that student being well-suited to the subject
matter, and so on). However, when other considerations are taken
into account, the student may have enrolled in courses that are
less than desirable. For instance, the courses may not be
especially helpful for a student seeking employment in a particular
field, or the courses may not have been particularly enjoyable for
the student.
[0023] The embodiments described below generally relate to
techniques for improving the course selection process. In
particular, the teachings herein describe systems and methods for
recommending educational courses for selection by a user based on
user "motivations". By including user motivations in the
recommendation process, a recommendation engine can go beyond
merely using academic success as a performance metric.
[0024] These user motivations may take various forms. For instance,
user motivations could include explicit motivations that are
inputted by a user (such as user preferences, self-identified
interests, and employment or educational goals for that user).
[0025] Motivations could also include inferred motivations that may
be determined in other ways, such as by analyzing other information
about the user. This might include, for instance, monitoring some
elements of user behavior to identify patterns that provide insight
into user interests or aptitudes that are relevant to course
selection.
[0026] In some cases, user motivations may result in
recommendations for course selections that are less than optimal
for the student from the perspective of pure academic success. For
instance, a recommendation engine may recommend courses that are
outside of the particular areas of expertise for a user, which may
mean that the user is less likely to achieve a high grade. However,
this recommended course might be desirable when the other
motivations of the user are taken into account. For example a user
may be very interested in organic chemistry, and thus want to take
courses in this area, even though this may be a very difficult
field of study for them.
[0027] Moreover, the recommendations provided by the recommendation
engine may be well suited to address other user motivations, such
as obtaining employment in a particular field. For instance, a
student who has a history of struggling in math-based courses may
nevertheless be encouraged to take a course in accounting if that
student is motivated to pursue a career in a related field.
[0028] Thus, consideration may be given to other motivations (such
as future employment) beyond pure academic performance when ranking
or recommending courses for a user. This could include tailoring
courses for a career that the user might find interesting, as
opposed to a career that may be financially rewarding. For
instance, a user may desire to participate in a particular field of
employment and prioritize doing something the user "loves to do"
over receiving a large paycheck. In such cases the recommendation
engine can encourage the student to select courses related to
career interests, even where the student is not necessarily
predicted to receive high grades.
[0029] On the other hand, a user may be particularly interested in
pursuing a graduate degree. In such cases, obtaining high grades
may be a primary concern to increase the likelihood that the user
will be admitted into the desired graduate program. A
recommendation engine, taking this into account, may increase the
weighting for courses that are easier or for which the user is more
likely to be academically successful. This may conversely decrease
the likelihood of recommending courses the user finds interesting,
particularly if those courses are difficult.
[0030] While some various embodiments of the system as described
herein are described from the perspective of an electronic
educational learning (e-Learning) system, it should be understood
that at least some of the techniques described herein may be
applicable in other contexts, particularly for instance in
traditional "brick and mortar" education institutions, or in the
context of corporate or government in-house training programs.
[0031] Referring now to FIG. 1, shown therein generally is an
example embodiment of an educational system 10 for providing
electronic learning according to one embodiment.
[0032] One or more users 12 and 14 can use the educational system
10 to communicate with an educational service provider 30 to
participate in, create, and consume electronic learning services,
including enrolling in and participating in various educational
courses. In some cases, the educational service provider 30 may be
part of or associated with a traditional "bricks and mortar"
educational institution (e.g., an elementary school, a high school,
a university or a college), another entity that provides
educational services (e.g., an online university, a company that
specializes in offering training courses, or an organization that
has a training department), or an independent service provider
(e.g., for providing individual electronic learning).
[0033] It should be understood that a "course" is not limited to
formal courses offered by formal educational institutions. The
course may generally include any form of learning instruction
offered by an entity of any type. For example, the course may be a
training seminar at a company for a small group of employees, a
professional certification program with a larger number of intended
participants (e.g., PMP, CMA, etc.), and so on.
[0034] In some embodiments, one or more educational groups can be
defined that involve one or more of the users 12 and 14. For
example, as shown in FIG. 1, the users 12 and 14 may be grouped
together in an educational group 16 representative of a particular
course (e.g., History 101, French 254), in which the first user 12
is an "instructor" and is responsible for providing the course
(e.g., organizing lectures, preparing assignments, creating
educational content, etc.), while the other users 14 are "learners"
or "students" that consume the course content (e.g., the users 14
are enrolled in the course to learn the course content).
[0035] In some cases, the users 12 and 14 may be associated with
more than one educational group. For instance, the users 14 may be
enrolled in more than one course, while the user 12 is enrolled in
at least one course and is responsible for teaching at least one
other course (which is common for example for graduate
students).
[0036] In some cases, educational sub-groups may also be defined.
For example, two of the users 14 are shown as part of an
educational sub-group 18. The sub-group 18 may be defined in
relation to a particular project or assignment (e.g., sub-group 18
may be a lab group) or based on other criteria. In some cases, due
to the nature of the electronic learning, the users 14 in a
particular sub-group 18 need not physically meet, but may
collaborate together using various tools provided by the
educational service provider 30.
[0037] In some cases, the groups 16 and sub-groups 18 could include
users 12 and 14 that share common interests (e.g., interests in a
particular sport), that participate in common activities (e.g.,
users that are members of a choir or a club), and/or have similar
attributes (e.g. users that are male, users under twenty-one years
of age, etc.).
[0038] Communication between the users 12 and 14 and the
educational service provider 30 can occur either directly or
indirectly using any suitable computing device. For example, the
user 12 may use a computing device 20 such as a desktop computer
that has at least one input device (e.g., a keyboard and a mouse)
and at least one output device (e.g., a display screen and
speakers).
[0039] The computing device 20 can generally be any suitable device
for facilitating communication between the users 12 and 14 and the
educational service provider 30. For example, the computing device
20 could be a laptop 20a wirelessly coupled to an access point 22
(e.g., a wireless router, a cellular communications tower, etc.), a
wirelessly enabled personal data assistant (PDA) 20b or smart
phone, a terminal 20c over a wired connection 23 or a tablet
computer 20d or a game console 20e over a wireless connection.
[0040] The computing devices 20 may be connected to the service
provider 30 via any suitable communications channel. For example,
the computing devices 20 may communicate to the educational service
provider 30 over a local area network (LAN) or intranet, or using
an external network, such as, for example, by using a browser on
the computing device 20 to browse one or more web pages presented
over the Internet 28 over a data connection 27.
[0041] The wireless access points 22 may connect to the educational
service provider 30 through a data connection 25 established over
the LAN or intranet. Alternatively, the wireless access points 22
may be in communication with the educational service provider 30
via the Internet 28 or another external data communications
network.
[0042] For example, one user 14 may use a laptop 20a to browse to a
webpage that displays elements of an electronic learning system
(e.g., a course page).
[0043] In some cases, one or more of the users 12 and 14 may be
required to authenticate their identities in order to communicate
with the educational service provider 30. For example, the users 12
and 14 may be required to input a login name and/or a password or
otherwise identify themselves to gain access to the educational
system 10.
[0044] In other cases, one or more users (e.g., "guest" users) may
be able to access the educational system 10 without authentication.
Such guest users may be provided with limited access, such as the
ability to review only one or a few components of the course, for
example, to decide whether they would like to enroll in a
particular course.
[0045] The educational service provider 30 generally includes a
number of functional components for facilitating the provision of
electronic learning services.
[0046] For example, the educational service provider 30 generally
includes one or more processing devices 32 (e.g., servers), each
having one or more processors. The processing devices 32 are
configured to send information (e.g., HTML or other data) to be
displayed on one or more computing devices 20, 20a, 20b and/or 20c
in association with social electronic learning (e.g., course
information). In some cases, the processing device 32 may be a
computing device 20 (e.g., a laptop or a personal computer).
[0047] The educational service provider 30 also generally includes
one or more data storage devices 34 (e.g., memory, etc.) that are
in communication with the processing devices 32, and could include
a relational database (such as an SQL database), or other suitable
data storage devices. The data storage devices 34 are configured to
host data 35 about the courses offered by the service provider.
[0048] For example, the data 35 can include course frameworks,
educational materials to be consumed by the users 14, historical
records about assessments or grades of users 14 or assignments
completed by the users 14, as well as various other
information.
[0049] The data storage devices 34 may also store authorization
criteria that define which actions may be taken by the users 12 and
14. In some cases, the authorization criteria may include at least
one security profile associated with at least one role. For
example, one role could be defined for users who are primarily
responsible for developing an educational course, teaching it, and
assessing work product from students of the course. Users with such
a role may have a security profile that allows them to configure
various components of the course, to post assignments, to add
assessments, to evaluate performance, and so on.
[0050] In some cases, some of the authorization criteria may be
defined by specific users 40 who may or may not be part of the
educational community 16. For example, users 40 may be permitted to
administer and/or define global configuration profiles for the
educational system 10, define roles within the educational system
10, set security profiles associated with the roles, and assign
roles to particular users 12 and 14 who use the educational system
10. In some cases, the users 40 may use another computing device
(e.g., a desktop computer 42) to accomplish these tasks.
[0051] The data storage devices 34 may also be configured to store
other information, such as personal information about the users 12
and 14 of the educational system 10, information about which
courses the users 14 are enrolled in, roles to which the users 12
and 14 are assigned, particular interests of the users 12 and 14,
and historical information about the performance of the users 12
and 14.
[0052] The processing devices 32 and data storage devices 34 may
also provide other electronic learning management tools (e.g.,
allowing users to add and drop courses, communicate with other
users using chat software, etc.), and/or may be in communication
with one or more other vendors that provide the tools.
[0053] As shown in FIG. 1, the educational service provider 30 also
generally includes a recommendation engine 80, which is operable to
generate recommendations for course enrollment based on user
motivations, as will be discussed further below.
[0054] In some cases, the educational service provider 30 may also
have one or more backup servers 31 that may duplicate some or all
of the data 35 stored on the data storage devices 34. The backup
servers 31 may be desirable for disaster recovery to prevent
undesired data loss in the event of an electrical outage, fire,
flood or theft, for example.
[0055] In some cases, the backup servers 31 may be directly
connected to the educational service provider 30, but could located
within the educational system 10 at a different physical location.
For example, the backup servers 31 could be located at a remote
storage location that is some distance away from the service
provider 30, and the service provider 30 could connect to the
backup server 31 using a secure communications protocol to ensure
that the confidentiality of the data 35 is maintained.
[0056] Turning now to FIG. 2, illustrated therein a block diagram
of the recommendation engine 80 shown in greater detail according
to one exemplary embodiment. In this embodiment the recommendation
engine 80 is operable to communicate with the user 14 via the
computing device 20. When course selection is initiated, user
motivations associated with the user 14 may be collected and that
will assist the course recommendation module 80 in generating a
suitable list of courses for that user.
[0057] For instance, as shown in this embodiment the user 14 may be
prompted to provide explicit motivations 82 to the recommendation
engine 80. In this example, the explicit motivations 82 include
user interests, job or employment goals, education goals, and a
list of identified friends. In some cases the user 14 may be
presented with pre-determined categories of information from which
they can pick motivations (e.g., via drop-down menus). In other
cases, the user 14 could input motivation information in a free
form manner (i.e. a text entry form).
[0058] In general, explicit motivations 82 can include any number
of suitable items. For example, explicit motivations 82 could
include user interests, such as interests in certain sports and
activities, participation in clubs and so on. Explicit motivations
82 could include membership in certain groups 16 and/or sub-groups
18. Explicit motivations 82 could also include user attributes,
such as age or gender (although in some cases this information may
already be known by the electronic learning system 10).
[0059] As shown in FIG. 2, explicit motivations 82 could also
include identifying "friends" or other social relationships with
peers and other classmates. In some cases, users may be able to
identify one or more "friends", and this relationship can be used
when recommending courses. For instance, a recommendation engine
may be more likely to recommend a course where some of the
registered participants in that course are "friends" of the
user.
[0060] In some cases, friends could be identified manually via
input from the user 14. In other cases, the user 14 might provide
the recommendation engine 80 with access to a social media service
that has social information about the user 14 (such as "friend"
relationships in Facebook and Instagram for example), allowing the
recommendation engine 80 to automatically obtain "friend"
information therefrom.
[0061] In some cases, the recommendation engine 80 may determine
when the user 14 is a member of a certain group (particularly a
small sub-group 18 such as a lab group) and may recommend courses
to the user 14 based on the course selections of other members of
that group.
[0062] In some cases, a user's motivations may be inferred, for
example based on information gathered about the user 14. This might
include, for instance, examining historical data about the user's
14 previous explicit choices, such as courses previously selected
by that user 14.
[0063] In some cases, the inferred motivations might also include
information gathered by observing user 14 behavior. For example,
data about user's 14 participation levels within particular courses
may be collected, or data about interactions between the user 14
and other students or teacher may be monitored. For instance, the
behavior of the user 14 can be monitored to determine whether the
user is very active in class discussions or forums.
[0064] These data may be useful at making course recommendations
based on criteria that the user 14 may not even be aware of. For
instance, the user 14 may be observed to be very active at leading
social groups and facilitating discussions between classmates. This
may suggest that the user 14 has an affinity for leadership, and a
leadership course may be recommended to the user 14 on this basis
(even where that user 14 may not have self-identified leadership as
a particular area of interest).
[0065] In some cases, inferred motivations can be generated based
on relative activity levels, such as the level of participation in
discussions, number of course logins, and so on.
[0066] In some cases, inferred motivations could be generated based
on a user's relative performance.
[0067] In some cases, inferred motivations could include inferred
social relationships. For example, a user may be observed to have a
significant social relationship with other specific users based on
connections made in discussions or forums, group participations, or
other forms of contact. These relationship can be observed and used
to generated inferred social relationship (i.e., inferred
friendships) even where a user may not have self-identified those
specific users as "friends" (either manually or via a social media
service).
[0068] Returning to FIG. 2, once the user motivations have been
identified, a decision module 84 can process the motivations and
look for suitable courses to recommend. In some cases, course
information may come from external sources, such as a course
database 86 managed by the educational service provider 30, or
another database 88 local to the recommendation engine 80 for
example.
[0069] In particular, the decision module 84 will receive
information about courses available for selection and use various
techniques for making one or more course recommendations. In
particular, a course recommendation may include determining which
courses may be particularly suitable for presentation to the user
based on the user's motivations (i.e., a "recommended course"), as
well as determining which courses may not be particularly suitable
for presentation to the user (i.e., a course to "avoid").
[0070] In some cases, different user motivations may be given
different priorities or "weights". For instance, explicit user
motivations (such as an expressed interest in physics) may be
weighted very heavily as compared to inferred motivations, which
may be weighted less heavily.
[0071] Moreover, user motivations may be combined with other
information to further adjust the weighting. For example, if the
user 14 has expressed a strong interest in physics and has
historically shown strong performance in physics courses, then the
recommendation engine 80 may determine that physics-based courses
are highly appropriate for that user 14, and make strong
recommendations accordingly.
[0072] On the other hand, where the user 14 has struggled with a
particular area of study (i.e., languages) and inferred motivations
suggest that the user 14 has little interest in related activities
(i.e., in geography or foreign travel), then the recommendation
engine 80 may determine that a course in French history should not
be recommended (and indeed should be avoided).
[0073] Understanding whether a course is suitable for a particular
motivation can be accomplished based on different information that
may be known about a course. For instance, in some cases a course
instructor may manually input keywords that identify topics or
other features of the course. This could include information such
as the course title, as well as meta-tags that may be manually
associated with the course and stored in the course database
86.
[0074] In other cases, the recommendation engine 80 may
automatically identify courses of interest by analyzing data
associated with that course. For example, the recommendation engine
80 may apply language-processing techniques to the course
description of a course to identify relevant topics and other
features (for example by identifying keywords of interest). Similar
language-processing techniques could be applied to other data, such
as the lecture materials used in the course (i.e., presentation
materials, assignments, etc.). In some cases this information may
be stored locally in the database 88 for further processing as
needed when subsequent users are using the recommendation engine
80.
[0075] For instance, where a user's "friends" (either
self-identified or inferred friends") have already enrolled in a
particular course, then the recommendation engine 80 may suggest
that particular course to the user. Similarly, the recommendation
engine 80 may look for historical patterns of enrollment to
identify people that the user has historically taken a number of
courses with (i.e., former classmates) and can make course
recommendations based on the enrollment decisions of these former
classmates.
[0076] In some cases, user generated content (such as content from
previous cohorts or semesters of a course) may be analyzed and used
to perform semantic subject matching to interests or other
motivations of a particular user. This might include, for example,
previous years' discussion boards, blog posts, wiki-style entries,
and so on.
[0077] Turning now to FIG. 3, illustrated therein is a block
diagram of an example output from the recommendation engine 80
according to one embodiment. In some cases, this output could be
presented as a webpage displayed on one of the computing devices
20, via client software running on the computing device 20, or in
any other suitable manner.
[0078] As shown in this example the recommendation engine 80 has
generated course recommendations that include a list 50 of
recommended courses based on the motivation information associated
with the user 14. In other cases the recommended courses could be
presented in a different format. For example, the recommendation
engine 80 could generate a proposed class schedule or timetable for
the user.
[0079] In this example the list 50 includes information about the
course, such as a course number 52 and course description 54. In
some cases, the list 50 could also include other course information
such as times offered, who is teaching the course, credits,
pre-requisites, and so on. In some cases this additional course
information may be available by "drilling down" into more detail
about the course, such as via a hyperlink or pop-up screen that
shows information in response to a user action (i.e., clicking on
or hovering over the course number).
[0080] In this embodiment, the list 50 also allows the user to
enroll 58 directly in the course. In particular, as shown each
recommended course is associated with a particular control 59
(i.e., a button or other input device) that allows the user to
select that course for enrollment.
[0081] In some cases, enrolment in one or more courses could happen
automatically. For instance, the user could be automatically
enrolled in the recommended courses, and then be presented with an
option to delete one or more courses.
[0082] In some embodiments, enrollment could happen directly
through the recommendation engine 80 or other associated system. In
other cases, enrollment could be handled via other methods, such as
via linked to an enrollment system or module for performing the
enrolment, or via other means such as calling the registrar, or
enrolling in person.
[0083] As shown, in this embodiment the list 50 also includes an
identified reason 56 for the recommendation. In particular, the
recommendation engine 80 may provide the user with explicit
information about why each course is being presented to the user.
For instance, in this embodiment the first course CALC 332 is being
recommended to the user 14 based on the user's previous success in
math courses. In contrast, the second course ART 101 is being
recommended based on the user's expressed (explicit) interests in
architecture. On the other hand, a third course, PSYC 201, is being
recommended based on the inferred motivation that three of the
user's friends have already enrolled in that course.
[0084] In some cases, the user may be presented with the ability to
learn more about the reason for the course recommendation 56. For
instance, the user may be able to access a hyperlink 57 to see
which of their friends have enrolled in PSYC 201, or other
information about why a course was recommended.
[0085] In some embodiments, the list 50 may also include an option
for ignoring 53 a particular course recommendation. For example,
although the user may be interested in fishing they may have no
real interest in taking a course on wildlife management, and thus
may choose to ignore that course (NATR 430). Ignoring a course can
remove that course from the list 50 of recommended courses.
[0086] Choosing to ignore a recommendation can also be used to
modify the list 50 of other recommended courses. For instance,
other similar courses on wildlife management may be ranked lower or
even removed entirely in response to the user's decision to ignore
the wildlife management course NATR 430.
[0087] Turning now to FIG. 4, in some embodiments the course
recommendations output from the recommendation engine may be a list
of courses that are not recommended (i.e., a list 60 of courses the
user should consider avoiding). Similar to list 50, the list 60 may
include course numbers 62 for courses that are not recommended, a
course description 64, as well as a reason 66 as to why the course
is not being recommended. For example, in this case the user is
being encouraged to avoid CHEM 400 since this is a difficult
course, avoid FRE 201 since the user doesn't seem to like French,
and avoid HIS 220 because of previous performance issues in history
related courses.
[0088] In some cases the user may still be prompted with the option
to enroll 68 in that course, notwithstanding the recommendation to
avoid a particular course.
[0089] In this embodiment, the user may also be presented with the
option to obtain more information about why a particular course was
not recommended. For instance, one or more hyperlinks 67 may be
provided that may allow the user to better understand why the
recommendation engine 80 thinks this course may be a bad fit for
the user. This may allow the user to better understand the
recommendation, which can be useful when determining whether to
override the recommendation to avoid a particular course.
[0090] Turning now to FIG. 5, illustrated therein is a flow chart
of an exemplary method 100 for automatically recommending courses
for selection by a user according to one embodiment.
[0091] At step 102, the method 100 includes identifying historical
information about the user. This may include, for example,
identifying historical performance information about how the
student has done in their courses (i.e., overall grade
information), and/or more granular information about particular
modules or assessments within certain courses (i.e., how the user
did in a geometry module or an algebra module in a math
course).
[0092] At step 104, the method 100 includes identifying user
motivations associated with that user and which will be used to
influence the recommendation engine.
[0093] In particular, at step 106 the recommendation engine can
receive explicit user inputs about their motivations. This could
include prompting the user to provide information about personal or
academic interests, employment goals, identifying social
relationship and so on.
[0094] On the other hand, at step 108 the recommendation engine may
also identify inferred motivations. These inferred motivations
could be determined based on other information, such as patterns of
behavior for the user, identified social relationships with other
users (i.e., the user's "friends" in lab group), or other
attributes that may be relevant to that particular user.
[0095] At step 110, the method 100 then generates course
recommendations. These course recommendations could include
recommended courses to enroll in (as shown in FIG. 3), but also
potentially courses to avoid (as shown in FIG. 4). In some cases,
these recommendations may be provided as one or more lists of one
or more courses.
[0096] At step 112, the method receives an input from the user
about the one or more recommended courses. For example, if the user
decides to enroll in a certain course (i.e., using the controls 59
to select that course) then the method 100 proceeds to step 114 and
the user will be enrolled in the course.
[0097] On the other hand, the user may choose to disregard the
recommended course (i.e., by actively ignoring the course
recommendation or taking another action). In such cases the method
100 may proceed to step 116 in which case the recommended courses
are updated. The method 100 may then return to step 110 and
generate a new list of recommended courses.
[0098] It should be understood that various modifications can be
made to the embodiments described and illustrated herein, without
departing from the embodiments, the general scope of which is
defined in the appended claims.
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