U.S. patent application number 17/200218 was filed with the patent office on 2022-09-15 for cognitive generation of learning path framework.
The applicant listed for this patent is International Business Machines Corporation. Invention is credited to Harish Bharti, Rajesh Kumar Saxena, Rakesh Shinde, Sandeep Sukhija.
Application Number | 20220292998 17/200218 |
Document ID | / |
Family ID | 1000005478884 |
Filed Date | 2022-09-15 |
United States Patent
Application |
20220292998 |
Kind Code |
A1 |
Saxena; Rajesh Kumar ; et
al. |
September 15, 2022 |
COGNITIVE GENERATION OF LEARNING PATH FRAMEWORK
Abstract
An approach to generating a learning path framework may be
provided. A Cognitive Bot may monitor the knowledge stream of a
subject matter expert (SME) to glean insights from the activities
and events performed by the SME. The CogBot determine categories
within the subject matter. The CogBot may tune a grade scoring
engine using the insights gleaned from the knowledge stream as a
threshold for the grade scoring module. The knowledge stream of a
second user may be monitored by a CogBot. A grade score of the
subject matter for the second user may be generated by the grade
scoring engine. An expertise level associated with the categories
may be determined. A learning path framework may be generated based
on the generated grade score and expertise level.
Inventors: |
Saxena; Rajesh Kumar;
(Maharashtra, IN) ; Bharti; Harish; (Pune, IN)
; Shinde; Rakesh; (Maharashtra, IN) ; Sukhija;
Sandeep; (Rajasthan, IN) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
International Business Machines Corporation |
Armonk |
NY |
US |
|
|
Family ID: |
1000005478884 |
Appl. No.: |
17/200218 |
Filed: |
March 12, 2021 |
Current U.S.
Class: |
1/1 |
Current CPC
Class: |
G09B 7/04 20130101; G09B
5/125 20130101 |
International
Class: |
G09B 7/04 20060101
G09B007/04; G09B 5/12 20060101 G09B005/12 |
Claims
1. A computer-implemented method for cognitive generation of a
learning path framework, the method comprising: monitoring, by a
processor, a knowledge stream of a first user on a CogBot platform,
wherein the monitoring of the knowledge stream of the first user is
performed by a first cognitive bot ("CogBot"); generating, by a
processor, one or more categories within the subject matter, based
on monitoring of the knowledge stream; tuning, by a processor, a
subject grade-score engine for a subject matter by the first
CogBot, based on the knowledge stream of the first user;
monitoring, by a processor, a knowledge stream of a second user,
wherein the monitoring of the knowledge stream of the second user
is performed by a CogBot; generating, by a processor, a grade-score
for the second user, based on monitoring the knowledge stream of
the second user by the second CogBot; determining, by a processor,
an expertise level of the second user for the generated one or more
categories, based on the generated grade-score; and generating, by
a processor, a learning path framework for the second user, based
on the determined expertise level of the second user.
2. The computer-implemented method of claim 1, further comprising:
authenticating, by a processor, the first user to a CogBot
platform; and authenticating, by a processor, the second user to
the CogBot platform.
3. The computer-implemented method of claim 2, wherein the CogBot
platform is a cloud based personalized learning subscription
service.
4. The computer-implemented method of claim 1, wherein the
knowledge stream is comprised of a plurality of predetermined
activities within a subject matter.
5. The computer-implemented method of claim 1, further comprising:
monitoring, by a processor, the knowledge stream of the second user
during performance of the learning path framework, wherein the
monitoring is performed by the second CogBot; determining, by a
processor, a second expertise level for the generated one or more
categories, based on the knowledge stream of the second user during
performance of the learning path framework; and updating, by a
processor, the learning path framework, based on the determined
second expertise level.
6. The computer-implemented method of claim 1, wherein tuning the
subject grade-score engine includes the first CogBot building
insights based on one or more of the following: the first user's
time spent on an activity, a score of the first user's activity,
and the number of times the first user accesses a topic.
7. The computer-implemented method of claim 1, further comprising:
receiving, by a processor, a topic objective from the second user,
wherein the topic objective is the purpose of the learning path
framework; and aligning, by a processor, the generated one or more
categories based on the topic objective.
8. A computer system for cognitive generation of a learning path
framework, the method comprising: a memory; and a processor in
communication with the memory, the processor being configured to
perform operations comprising: monitor a knowledge stream of a
first user on a CogBot platform, wherein the monitoring of the
knowledge stream of the first user is performed by a first
cognitive bot ("CogBot"); generate one or more categories within
the subject matter, based on monitoring of the knowledge stream;
tune a subject grade-score engine for a subject matter by the first
CogBot, based on the knowledge stream of the first user; monitor a
knowledge stream of a second user, wherein the monitoring of the
knowledge stream of the second user is performed by a CogBot;
generate a grade-score for the second user, based on monitoring the
knowledge stream of the second user by the second CogBot; determine
an expertise level of the second user for the generated one or more
categories, based on the generated grade-score; and generate a
learning path framework for the second user, based on the
determined expertise level of the second user.
9. The computer-system of claim 8, further comprising: authenticate
the first user to a CogBot platform; and authenticate the second
user to the CogBot platform.
10. The computer-system of claim 9, wherein the CogBot platform is
a cloud based personalized learning subscription service.
11. The computer-system of claim 8, wherein the knowledge stream is
comprised of a plurality of predetermined activities within the
subject matter.
12. The computer-system of claim 8, further comprising instructions
to: monitor the knowledge stream of the second user during
performance of the learning path framework, wherein the monitoring
is performed by the second CogBot; determine a second expertise
level for the generated one or more categories, based on the
knowledge stream of the second user during performance of the
learning path framework; and update the learning path framework,
based on the determined second expertise level.
13. The computer-system of claim 8, wherein tuning the subject
grade-score engine includes the first CogBot building insights
based on one or more of the following: the first user's time spent
on an activity, a score of the first user's activity, and the
number of times the first user accesses a topic.
14. The computer-system of claim 8, further comprising: receive a
topic objective from the second user, wherein the topic objective
is the purpose of the learning path framework; and align the
generated one or more categories based on the topic objective.
15. A computer program product for cognitive generation of a
learning path framework having program instructions embodied
therewith, the program instructions executable by a processor to
cause the processors to perform a function, the function
comprising: monitor a knowledge stream of a first user on a CogBot
platform, wherein the monitoring of the knowledge stream of the
first user is performed by a first cognitive bot ("CogBot");
generate one or more categories within the subject matter, based on
monitoring of the knowledge stream; tune a subject grade-score
engine for a subject matter by the first CogBot, based on the
knowledge stream of the first user; monitor a knowledge stream of a
second user, wherein the monitoring of the knowledge stream of the
second user is performed by a CogBot; generate a grade-score for
the second user, based on monitoring the knowledge stream of the
second user by the second CogBot; determine an expertise level of
the second user for the generated one or more categories, based on
the generated grade-score; and generate a learning path framework
for the second user, based on the determined expertise level of the
second user.
16. The computer program product of claim 15, further comprising:
authenticate the first user to a CogBot platform; and authenticate
the second user to the CogBot platform.
17. The computer program product of claim 16, wherein the CogBot
platform is a cloud based personalized learning subscription
service.
18. The computer program product of claim 15, wherein the knowledge
stream is comprised of a plurality of predetermined activities
within the subject matter.
19. The computer program product of claim 15, further comprising
instructions to: monitor the knowledge stream of the second user
during performance of the learning path framework, wherein the
monitoring is performed by the second CogBot; determine a second
expertise level for the generated one or more categories, based on
the knowledge stream of the second user during performance of the
learning path framework; and update the learning path framework,
based on the determined second expertise level.
20. The computer program product of claim 15, wherein tuning the
subject grade-score engine includes the first CogBot building
insights based on one or more of the following: the first user's
time spent on an activity, a score of the first user's activity,
and the number of times the first user accesses a topic.
Description
BACKGROUND OF THE INVENTION
[0001] The present invention relates generally to the field of
adaptive curriculum sequencing, more specifically, cognitive
generation of a learning path framework.
[0002] A mentor or teacher can be a valuable resource when it comes
to being informed on a subject. A learning path framework or
curriculum guide can allow an individual seeking knowledge to have
an efficient and effective method of learning. However, in some
situations many individuals may have a general understanding of
some topics within a specific subject matter. A personalized
learning framework can be obtained through determining the
knowledge of a user for a given subject matter, which can allow for
the generation of a sequence of personalized learning activities to
address the topics an individual is less knowledgeable about, while
spending less or no time on previously mastered topics.
[0003] Artificial intelligence and cognitive services have become
more available to the general public. Analyzing unstructured data
and structured data has allowed for numerous advancements, due to
the ability to detect correlations between multiple data sources.
Cognitive Bots ("CogBots") are bots with the capability to monitor
data streams, classify images and text, and/or understand natural
language within multiple data sources accessed by an individual and
build insights associated from the data sources.
SUMMARY
[0004] Embodiments of the present disclosure include a
computer-implemented method, computer program product, and a system
for cognitive generation of a learning path framework. Embodiments
may include monitoring the knowledge stream of a first user on a
CogBot platform, wherein the monitoring of the first user's
knowledge stream is performed by a cognitive bot. One or more
categories within the subject matter may be generated. The
generated categories may be based on monitoring of the knowledge
stream. A grade-score engine for a subject matter may be tuned by
the CogBot. The tuning of the grade-score engine may be based on
the monitoring of the knowledge stream of the first user. The
knowledge stream of a second user may be monitored by the CogBot. A
grade-score may be generated for the second user, based on
monitoring the second user's knowledge stream by the CogBot. An
expertise level of the second user may be determined for the one or
more categories based on the generated grade-score. A learning path
framework may be generated for the second user based on the
determined expertise levels for the categories.
[0005] The above summary is not intended to describe each
illustrated embodiment of every implementation of the present
disclosure.
BRIEF DESCRIPTION OF THE DRAWINGS
[0006] FIG. 1 is a functional block diagram generally depicting a
learning path framework generation environment, in accordance with
an embodiment of the present invention.
[0007] FIG. 2 is a functional block diagram depicting a learning
path framework generation engine, in accordance with an embodiment
of the present invention.
[0008] FIG. 3 is a flowchart depicting a method for cognitive
generation of a learning path framework, in accordance with an
embodiment of the present invention.
[0009] FIG. 4 is a functional block diagram of an exemplary
computing system within a learning path framework environment, in
accordance with an embodiment of the present invention.
[0010] FIG. 5 is a diagram depicting a cloud computing environment,
in accordance with an embodiment of the present invention.
[0011] FIG. 6 is a functional block diagram depicting abstraction
model layers, in accordance with an embodiment of the present
invention.
[0012] While the embodiments described herein are amenable to
various modifications and alternative forms, specifics thereof have
been shown by way of example in the drawings and will be described
in detail. It should be understood, however, that the particular
embodiments described are not to be taken in a limiting sense. On
the contrary, the intention is to cover all modifications,
equivalents, and alternatives falling within the spirit and scope
of the disclosure.
DETAILED DESCRIPTION
[0013] The embodiments depicted allow for the cognitive generation
of a learning path framework. In an embodiment, a cognitive bot
("CogBot") can monitor the knowledge stream of a first user who is
a subject matter expert ("SME") in a given subject matter. From the
monitoring of the knowledge stream, the CogBot can glean insights
from the activities and events of the SME. The monitoring of the
SME allows for a mastery threshold for the subject matter.
Additionally, the CogBot can develop topic categories within the
subject matter. The CogBot can use the insights to tune a grade
score engine. A second CogBot can monitor the knowledge stream of a
second user. The second user is an individual that is not a SME,
but an individual seeking to increase his or her knowledge in the
subject matter. The second CogBot can generate insights from the
activities performed within the second knowledge stream. The
insights can be used to generate a grade score and an expertise
level for the categories within the subject developed by the first
CogBot. The expertise score can be used to develop a learning path
framework for the second user.
[0014] In some embodiments, the learning path framework can be
updated. Once the learning path framework is generated, the second
user's CogBot can continue to monitor the knowledge stream of the
second user. Insights can be developed by the CogBot based on the
monitoring. The insights can be used by the grade score engine to
generate a second expertise level, for the categories within the
subject matter. The learning path framework can be updated using
the second expertise level.
[0015] FIG. 1 is a functional block diagram depicting a learning
path framework generation environment 100. Learning path framework
generation environment 100 comprises CogBot module 104 and learning
path framework generation engine 106 operational on server 102,
client computer 110A and 110B and network 108.
[0016] Server 102 and client computers 110A and 110B can be a
standalone computing device, a management server, a web server, a
mobile computing device, or any other electronic device or
computing system capable of receiving, sending, and processing
data. In other embodiments, server 102 and client computers 110A
and 110B can represent a server computing system utilizing multiple
computers as a server system. In another embodiment, server 102 and
client computers 110A and 110B can be a laptop computer, a tablet
computer, a netbook computer, a personal computer, a desktop
computer, or any programmable electronic device capable of
communicating with other computing devices within learning path
framework generation environment 100 via network 108.
[0017] In another embodiment, server 102 and client computers 110A
and 110B represent a computing system utilizing clustered computers
and components (e.g., database server computers, application server
computers, etc.) that can act as a single pool of seamless
resources when accessed within learning path framework generation
environment 100. Server 102 and client computers 110A and 110B can
include internal and external hardware components, as depicted and
described in further detail with respect to FIG. 4. It should be
noted, while only server 102 and client computers 110A and 110B are
shown in FIG. 1, multiple computing devices can be present within
learning path framework generation environment 100. In an example,
server 102 and client computers 110A and 110B can be a part of a
cloud server network in which a computing device (not shown)
connected to network 108 can access server 102 and client computers
110A and 110B (e.g. the internet).
[0018] The CogBot Module 104 is a computer module with cognitive or
artificial intelligence capabilities that can monitor the knowledge
stream of a user to develop insights for with a subject matter. A
knowledge stream is the activities and events performed by the user
(which can be monitored in real-time or at predetermined intervals
by the CogBot) associated with a learning mastery objective. CogBot
module 104 can create one or more instances of a CogBot to monitor
one user. CogBot module 104 can be trained in natural language
processing to understand structured and unstructured data
associated with activities and events within the knowledge stream
of a user. In some embodiments, CogBot Module 104 can generate
instances of a CogBot that has been trained with a model capable of
optical character recognition. For example, if a user is reading a
portable document format ("PDF") of an article within the subject
matter that has been scanned, the CogBot can identify the
characters within the PDF and convert the characters into a
computer understandable format (e.g. tokens, word vectors, etc.).
CogBots generated by CogBot module 104 can be configured models,
such as deep learning models including recurrent neural network,
convolutional neural network, and the like. Additionally, in some
embodiments, CogBot module can be configured to understand videos
including the natural language and images associated with the
video. For example, if a user is viewing an instructional video on
advanced kidney surgical procedures, the CogBot can generate a
transcript for the instructional video from which it will develop a
knowledge base. Additionally, the CogBot may be configured with
image recognition to recognize specific structures within the
kidney. In some embodiments, an artificial intelligence model or
cognitive model may be the base model for CogBot module 104, for
example Watson by IBM.RTM..
[0019] In some embodiments, CogBot module 104 can be associated
with a CogBot platform (not shown). A CogBot platform can be a
cloud based service to which a user subscribes. For example, a user
can subscribe to a learning platform that is a CogBot platform. A
CogBot platform can be located on server 102 and can be a
subscription service. A user can be provided with a username and a
password to authenticate the user's CogBot platform session. The
CogBot module 104 can generate a CogBot to monitor the user's
activities within the session. The CogBot platform may be
associated with a specific subject matter (e.g., thermodynamics,
computer programming, chemistry, basket weaving, video games,
baking, etc.) SMEs may be monitored by the CogBot to set a
benchmark for a subject matter. In an embodiment, a user may have a
profile, in which the user inputs their education or certifications
associated with the subject matter. Further, the CogBot can assess
the time spent on certain activities to determine the user's
familiarity with a topic within the subject matter with a learning
objective. In some embodiments, the CogBot can determine if
categories of topics within the subject matter and relationships
between the categories. CogBot module 104 can be configured to
align the categories within the subject matter. In some
embodiments, CogBot module 104 can be configured to understand a
user based on natural language responses to critical thinking
questions. CogBot module 104 can be configured to generate word
representations for previously unknown words associated with the
subject matter. It should be noted, CogBot module 104 can possess
machine learning capabilities in which the knowledge and
understanding derived from monitoring a user's knowledge stream and
gleaning insights continues to build upon previous understanding of
the CogBot module. The understanding derived can include the
learning style and preferences of a user or users, allowing for a
more personalized learning path framework.
[0020] Learning path framework generation engine 106 is a computer
program that can be configured to generate a learning path
framework for a subject matter, based on the cognitive insights
gleaned from the knowledge stream of a user by a CogBot. Learning
path framework generation engine 106 can determine an expertise
level of a topic subject based on a grade score generated from the
insights of one or more CogBots. Further, learning path framework
generation engine 106 can be configured to generate a learning path
framework based on the expertise level determined within a
category. A learning path framework is a curriculum of activities
and events for a less knowledgeable user to follow. The learning
path framework is based on the previous activities of SMEs, which
are used by learning path framework generation engine 106 to
develop a baseline of mastery for the subject matter or categories
within the subject matter. It should be noted, more than one SME
may be used to establish a baseline of knowledge mastery within a
subject matter, as some SMEs may have better understanding of
certain topic categories within the subject matter.
[0021] Network 108 can be a local area network (LAN), a wide area
network (WAN) such as the Internet, or a combination of the two,
and can include wired, wireless, or fiber optic connections. In
general, network 108 can be any combination of connections and
protocols that will support communications between server 102,
client computers 110A and 110B, and other computing devices (not
shown).
[0022] FIG. 2 is a functional block diagram 200 depicting a
learning path framework generation engine 106, in accordance with
an embodiment of the present invention. Operational learning path
framework generation engine 106 includes grade score module 202 and
learning path framework module 204.
[0023] Grade score module 202 is a computer program that can be
configured to determine a grade score of a category for a user
within a subject matter. The grade score is a measure of the user's
expertise level for a given category. In some embodiments, the
grade score can be determined using an implicit topic model, where
each topic being assessed for the user is scored. An implicit score
"S.sub.i" is an implicit score, which can be converted into an
actual grade-score value, thus,
S i = A i .differential. max ( A i ) .times. ( i = { 1 .times.
.times. 3 } ) ##EQU00001##
Where, .differential..sub.max(A.sub.i) represents maximum
information allowed for time (A1), score (A2) or frequency (A3)
respectively. A1 is a topic time grade-score, which is the sum of
all of the user's session times. A2 is a Topic preference
grade-score, which is the user's score for a specific topic
resource. A3 is a Topic frequency of use, which is the number of
accesses made to the topic. Thereafter, the implicit grade-score
category j is calculated as:
j = { 0 .times. ( A 1 ) , w 1 .ltoreq. S i .ltoreq. w 2 1 .times. (
A 2 ) , w 3 .ltoreq. S i .ltoreq. w 4 2 .times. ( A 3 ) , w 5
.ltoreq. S i .ltoreq. w 6 ##EQU00002##
Where, A1, A2 and A3 denote CogBot grade-score categories and w1,
w2, . . . ,w6 denote limiting values. Calculating j associated with
topic score we can substitute, "w1=0", "w2=.alpha.", "w3=.alpha.",
"w4 32 1-.alpha.", "w5=1-.alpha.", and "w6=1". Additionally,
".alpha." is in the interval (0, 0.5).
[0024] Using adaptive reasoning based on CogBot insights, a user
expertise level can be determined by grade score module 202, where
a user's ability estimate includes the level of a latent trait of
the user, demonstrated in an observed polytomous (e.g., where a
user can achieve more than two grade-score categories for a
specific item) grade-score pattern Where in the following: "Fk", is
the specific difficulty, such that, (k=0, 1, . . . ,j, . . . , t)
with F0=0, providing:
.SIGMA..sub.k=0.sup.jF.sub.k=0
The Joint Maximum Likelihood Estimation can be used for estimating
the user's ability that maximizes the likelihood function for a
particular grade-score. P.sub.aij is the probability that the user
a will select the j.sup.th grade-score category of item i, which is
calculated as follow:
P aij = e j .function. ( M - D i ) - .SIGMA. k = 0 j .times. F k h
= 0 t e h .function. ( M - D t ) - .SIGMA. k = 0 h .times. F k
##EQU00003##
where, "M", is the grade-score scales used to measure user's
ability. "D.sub.i", is the item's difficulty, "t+1" and ordinal
grade-score categories 0, 1, . . . ,j, . . . , t, are for the
topics accessed.
[0025] Additionally, the expected score ES can be defined as the
sum of the expected value of the ratings over all individual items,
thus the modelled expected rating sum over all topics is as
follows:
E .times. S = i = 1 l j = 0 t j .times. P aij ##EQU00004##
[0026] While, modelled variance V of the expected score ES at
specific user's ability M is given by the sum of the variances of
the individual items' expected values:
V = i = 1 l [ ( j - 0 t j 2 .times. P aij ) - ( j - 0 t j .times. P
aij ) 2 ] ##EQU00005##
[0027] The initial estimate of user's ability M can be any finite
value and can be obtained as a standard as follows:
M = D m .times. e .times. a .times. n + log .function. ( R - R min
R max - R ) .times. where , ##EQU00006## D m .times. e .times. a
.times. n = 1 l .times. i = 1 l D i ##EQU00006.2##
DMean denotes the average item's difficulty in l items, R is the
raw score, RMin is the minimum possible score, and RMax is the
maximum possible score.
[0028] When the previous equations are iteratively solved, the
final ability can be calculated as follows:
M ' = M + R - E .times. S V ##EQU00007##
[0029] Learning path framework module 204 is a computer program
that can be configured to generate a learning path framework for a
user, based on the grade score determined by grade score module
202. A learning path framework can be a curriculum sequence of
activities and actions to increase a user's mastery or expertise
level in a subject or topic category to that of a SMEs mastery. The
topic category knowledge gap can be determined by calculating the
distance between a SMEs expertise level and the user expertise
level per category of the subject matter, based on the user's
determined grade score and expertise score. In some embodiments, a
learning path can be generated in the following manner: identifying
the maximum information, whereas the item information is the
expected variance of scoring functions based on probability along
the observation ability M, so the expected value E(M) is calculated
as follows:
E .function. ( M ) = j = 0 t j .times. P ij ##EQU00008##
[0030] E(M) denotes the expected score function. M denotes a user's
category knowledge estimated after n preceding topic resources, Pij
represents the probability of a j.sup.th grade-score category for
the user with ability M,
I .function. ( M ) = ( j = 0 t j 2 .times. P i .times. j ) - ( E
.function. ( M ) ) 2 ##EQU00009##
I(M) is the information function as shown above that represents the
information contributed by specific topic access i across the range
of user's ability M. In this instance,. a topic with a higher I(M)
would be more effective in increasing the user's mastery in the
topic.
[0031] FIG. 3 is a flowchart depicting method 300 for cognitive
generation of a learning framework, in accordance with an
embodiment of the present invention.
[0032] At step 302, a CogBot instance from CogBot module 104
monitors the knowledge stream of a user. In some embodiments, the
user is a SME. For example, a SME that has logged into a CogBot
platform will have a CogBot that monitors the user's knowledge
stream. The CogBot platform may be a platform for computer
programming (e.g., Python, JavaScript, C++). The CogBot can be
configured to monitor activities and events in which the user
participates. For example, the user may participate in a seminar
for programming advanced neural networks in Python. In some
embodiments, CogBot module 104 can be configured with an optical
character recognition capability paired with natural language
processing capabilities (e.g. word2vec, BERT, XLNet, BOWs, etc.).
The CogBot can determine insights from the monitoring of the
knowledge stream, developing an understanding of the subject
matter. In some embodiments, the knowledge stream may contain
activities, such as assessments, which the user may complete
further allowing for the CogBot to determine the mastery threshold
level for the subject matter of the user and/or which activities
events correlate to certain categories of the subject. Further, in
some embodiments, the CogBot can determine the difficulty of a
category or activity depending on the time spent on the activity,
the frequency an activity is accessed, and/or the number of
attempts required for a user to obtain a passing score.
[0033] At step 304, the CogBot from CogBot module 104 generates
topic categories for the subject matter. The generated categories
can be based on the insights and understanding determined from
monitoring the user's knowledge stream. For example, the user may
take a series of lectures about autoencoder neural networks while
also reviewing multiple peer reviewed articles on recurrent neural
networks and convolutional neural networks. The CogBot can use
natural language processing capabilities to determine that each of
these falls under the subject matter of neural networks, but that
each one is a distinct architecture, and therefore all are a topic
category within the subject matter of neural networks. Further, in
some embodiments, the CogBot can use image classification to
develop an understanding of the architectures of each type of
neural network referenced above.
[0034] At step 306, the CogBot uses the insights developed from the
user's knowledge stream to tune a grade score module for the
subject matter. Tuning a grade score module 202, may include
setting the base threshold for mastery of a subject matter or
category, based on the insights generated from the CogBot
monitoring the user's knowledge stream. In some embodiments, the
CogBot can assign a numerical value to topics and/or categories.
For example, the CogBot may assign a value to the time spent on a
certain activity or event within a subject matter. Additionally,
the CogBot may assign a value to a grade received in certain
activities (e.g. assessments) or events. Further, the CogBot may
assign a value to the frequency the user accesses an activity or
topic. For example, if a user spends more time on an event within a
category and accesses an activity within an the same category, the
CogBot may assign a higher difficulty value to that category.
[0035] At step 308, a second CogBot instance from CogBot Module 104
can monitor the knowledge stream of a second user. In some
embodiments, a second user can log into a CogBot platform and
perform activities and events associated with a subject matter. The
knowledge stream can have information regarding a learning
objective relating to the subject matter for the second user. For
example, the user may desire to increase his or her knowledge in
long-term short-term memory neural networks. In some embodiments,
the second user may be less knowledgeable than the first user in
the subject matter. The user can complete assessments and events
associated with the subject matter, allowing the second CogBot to
develop insights for the knowledge of the user. In some
embodiments, the CogBot will assign numerical values to the time
spent on an activity, the score of assessments, and the frequency
which the second user accesses each activity or event. For example,
the second user may possess a great deal of knowledge regarding
programming in Java Script but be a novice at programming in
Python. The second user does not spend much time accessing Java
Script activities and receives high scores in assessments
associated with Java Script. The second user spends a great deal of
time completing Python activities and accesses the activities
frequently. Further, a user profile may be within the knowledge
stream, allowing the CogBot to develop a deeper insight into a
user's knowledge of the subject matter.
[0036] At step 310, grade score module 202 can generate a grade
score of the subject matter for the second user, based on the
CogBot monitoring the knowledge stream of the second user. In an
embodiment, the CogBot monitoring the second user's knowledge
stream can send the numerical values associated with the activities
and events to grade score module 202. A grade score is a user's
overall knowledge of the subject matter associated with the
learning objective. Grade Score module 202 can calculate a grade
score for the subject matter based on the numerical values and
generate a grade score against the SME for the subject matter
baseline established during tuning.
[0037] At step 312, grade score module 202 can determine an
expertise level for the generated categories, based on the grade
score and insights determined by the CogBot. In some embodiments,
an expertise level is the second user's mastery of the topic
category within the subject matter. This can be used to determine
the learning path framework. In some embodiments, an expertise
level for one topic categories can be determined based on the time
spent and the scores received for activities and events associated
with the topic. It should be noted an event or activity can be
associated with one or more topic categories and impact one or more
expertise levels. For example, if a user is performing an activity
requiring the user to program a convolutional neural network in
python programming language, the category could include activities
and events with programming in python, convolutional neural
networks, and linear algebra.
[0038] At step 314, a learning path framework is generated by
learning path framework module 204 based on the grade score and
expertise level from grade score module 202. A learning path
framework is a prescribed curriculum and recommendations for the
second user. The learning path framework can also be a
recommendation for a mentor to assist the second user that is less
knowledgeable in the subject matter. In some embodiments, the
mentor may be a subscriber to the CogBot platform, that has
authorized the service to assign a her a protege. The mentor can
assist the second user in completing the learning path framework.
In some embodiments, one or more activities and events can be
prescribed within the framework, including a timeline for
completing the activities and events. For example, an intermediate
difficulty learning module for python programming objects may be
prescribed, followed by a refresher module in linear algebra and
subsequently a module on programming long-short term memory neural
networks programmed in python. In another example, a mentor
competent in convolutional neural networks may be assigned to the
second user along with a prescribed personalized learning
curriculum, due to the second user's familiarity with
autoencoders.
[0039] FIG. 4 depicts computer system 400, an example computer
system representative of servers 102 and 112 or any other computing
device within an embodiment of the invention. Computer system 400
includes communications fabric 412, which provides communications
between computer processor(s) 414, memory 416, persistent storage
418, network adaptor 428, and input/output (I/O) interface(s) 426.
Communications fabric 412 can be implemented with any architecture
designed for passing data and/or control information between
processors (such as microprocessors, communications and network
processors, etc.), system memory, peripheral devices, and any other
hardware components within a system. For example, communications
fabric 412 can be implemented with one or more buses.
[0040] Computer system 400 includes processors 414, cache 422,
memory 416, network adaptor 428, input/output (I/O) interface(s)
426 and communications fabric 412. Communications fabric 412
provides communications between cache 422, memory 416, persistent
storage 418, network adaptor 428, and input/output (I/O)
interface(s) 426. Communications fabric 412 can be implemented with
any architecture designed for passing data and/or control
information between processors (such as microprocessors,
communications and network processors, etc.), system memory,
peripheral devices, and any other hardware components within a
system. For example, communications fabric 412 can be implemented
with one or more buses or a crossbar switch.
[0041] Memory 416 and persistent storage 418 are computer readable
storage media. In this embodiment, memory 416 includes persistent
storage 418, random access memory (RAM) 420, cache 422 and program
module 424. In general, memory 416 can include any suitable
volatile or non-volatile computer readable storage media. Cache 422
is a fast memory that enhances the performance of processors 414 by
holding recently accessed data, and data near recently accessed
data, from memory 416. As will be further depicted and described
below, memory 416 may include at least one of program module 424
that is configured to carry out the functions of embodiments of the
invention.
[0042] The program/utility, having at least one program module 424,
may be stored in memory 416 by way of example, and not limiting, as
well as an operating system, one or more application programs,
other program modules, and program data. Each of the operating
systems, one or more application programs, other program modules,
and program data or some combination thereof, may include an
implementation of a networking environment. Program module 424
generally carries out the functions and/or methodologies of
embodiments of the invention, as described herein.
[0043] Program instructions and data used to practice embodiments
of the present invention may be stored in persistent storage 418
and in memory 416 for execution by one or more of the respective
processors 414 via cache 422. In an embodiment, persistent storage
418 includes a magnetic hard disk drive. Alternatively, or in
addition to a magnetic hard disk drive, persistent storage 418 can
include a solid state hard drive, a semiconductor storage device,
read-only memory (ROM), erasable programmable read-only memory
(EPROM), flash memory, or any other computer readable storage media
that is capable of storing program instructions or digital
information.
[0044] The media used by persistent storage 418 may also be
removable. For example, a removable hard drive may be used for
persistent storage 418. Other examples include optical and magnetic
disks, thumb drives, and smart cards that are inserted into a drive
for transfer onto another computer readable storage medium that is
also part of persistent storage 418.
[0045] Network adaptor 428, in these examples, provides for
communications with other data processing systems or devices. In
these examples, network adaptor 428 includes one or more network
interface cards. Network adaptor 428 may provide communications
through the use of either or both physical and wireless
communications links. Program instructions and data used to
practice embodiments of the present invention may be downloaded to
persistent storage 418 through network adaptor 428.
[0046] I/O interface(s) 426 allows for input and output of data
with other devices that may be connected to each computer system.
For example, I/O interface 426 may provide a connection to external
devices 430 such as a keyboard, keypad, a touch screen, and/or some
other suitable input device. External devices 430 can also include
portable computer readable storage media such as, for example,
thumb drives, portable optical or magnetic disks, and memory cards.
Software and data used to practice embodiments of the present
invention can be stored on such portable computer readable storage
media and can be loaded onto persistent storage 418 via I/O
interface(s) 426. I/O interface(s) 426 also connect to display
432.
[0047] Display 432 provides a mechanism to display data to a user
and may be, for example, a computer monitor or virtual graphical
user interface.
[0048] The components described herein are identified based upon
the application for which they are implemented in a specific
embodiment of the invention. However, it should be appreciated that
any particular component nomenclature herein is used merely for
convenience, and thus the invention should not be limited to use
solely in any specific application identified and/or implied by
such nomenclature.
[0049] The present invention may be a system, a method and/or a
computer program product. The computer program product may include
a computer readable storage medium (or media) having computer
readable program instructions thereon for causing a processor to
carry out aspects of the present invention.
[0050] The computer readable storage medium can be a tangible
device that can retain and store instructions for use by an
instruction execution device. The computer readable storage medium
may be, for example, but is not limited to, an electronic storage
device, a magnetic storage device, an optical storage device, an
electromagnetic storage device, a semiconductor storage device, or
any suitable combination of the foregoing. A non-exhaustive list of
more specific examples of the computer readable storage medium
includes the following: a portable computer diskette, a hard disk,
a random access memory (RAM), a read-only memory (ROM), an erasable
programmable read-only memory (EPROM or Flash memory), a static
random access memory (SRAM), a portable compact disc read-only
memory (CD-ROM), a digital versatile disk (DVD), a memory stick, a
floppy disk, a mechanically encoded device such as punch-cards or
raised structures in a groove having instructions recorded thereon,
and any suitable combination of the foregoing. A computer readable
storage medium, as used herein, is not to be construed as being
transitory signals per se, such as radio waves or other freely
propagating electromagnetic waves, electromagnetic waves
propagating through a waveguide or other transmission media (e.g.,
light pulses passing through a fiber-optic cable), or electrical
signals transmitted through a wire.
[0051] Computer readable program instructions described herein can
be downloaded to respective computing/processing devices from a
computer readable storage medium or to an external computer or
external storage device via a network, for example, the Internet, a
local area network, a wide area network and/or a wireless network.
The network may comprise copper transmission cables, optical
transmission fibers, wireless transmission, routers, firewalls,
switches, gateway computers and/or edge servers. A network adapter
card or network interface in each computing/processing device
receives computer readable program instructions from the network
and forwards the computer readable program instructions for storage
in a computer readable storage medium within the respective
computing/processing device.
[0052] Computer readable program instructions for carrying out
operations of the present invention may be assembler instructions,
instruction-set-architecture (ISA) instructions, machine
instructions, machine dependent instructions, microcode, firmware
instructions, state-setting data, or either source code or object
code written in any combination of one or more programming
languages, including an object oriented programming language such
as Smalltalk, C++ or the like, and conventional procedural
programming languages, such as the "C" programming language or
similar programming languages. The computer readable program
instructions may execute entirely on the user's computer, partly on
the user's computer, as a stand-alone software package, partly on
the user's computer and partly on a remote computer or entirely on
the remote computer or server. In the latter scenario, the remote
computer may be connected to the user's computer through any type
of network, including a local area network (LAN) or a wide area
network (WAN), or the connection may be made to an external
computer (for example, through the Internet using an Internet
Service Provider). In some embodiments, electronic circuitry
including, for example, programmable logic circuitry,
field-programmable gate arrays (FPGA), or programmable logic arrays
(PLA) may execute the computer readable program instructions by
utilizing state information of the computer readable program
instructions to personalize the electronic circuitry, in order to
perform aspects of the present invention.
[0053] Aspects of the present invention are described herein with
reference to flowchart illustrations and/or block diagrams of
methods, apparatus (systems) and computer program products
according to embodiments of the invention. It is understood that
each block of the flowchart illustrations and/or block diagrams,
and combinations of blocks in the flowchart illustrations and/or
block diagrams, can be implemented by computer readable program
instructions.
[0054] These computer readable program instructions may be provided
to a processor of a general purpose computer, special purpose
computer, or other programmable data processing apparatus to
produce a machine, such that the instructions, which execute via
the processor of the computer or other programmable data processing
apparatus, create means for implementing the functions/acts
specified in the flowchart and/or block diagram block or blocks.
These computer readable program instructions may also be stored in
a computer readable storage medium that can direct a computer, a
programmable data processing apparatus, and/or other devices to
function in a particular manner, such that the computer readable
storage medium having instructions stored therein comprises an
article of manufacture including instructions which implement
aspects of the function/act specified in the flowchart and/or block
diagram block or blocks.
[0055] The computer readable program instructions may also be
loaded onto a computer, other programmable data processing
apparatus, or other device to cause a series of operational steps
to be performed on the computer, other programmable apparatus or
other device to produce a computer implemented process, such that
the instructions which execute on the computer, other programmable
apparatus, or other device implement the functions/acts specified
in the flowchart and/or block diagram block or blocks.
[0056] The flowchart and block diagrams in the Figures illustrate
the architecture, functionality and operation of possible
implementations of systems, methods, and computer program products
according to various embodiments of the present invention. In this
regard, each block in the flowchart or block diagrams may represent
a module, segment or portion of instructions, which comprises one
or more executable instructions for implementing the specified
logical function(s). In some alternative implementations, the
functions noted in the block may occur out of the order noted in
the Figures. For example, two blocks shown in succession may, in
fact, be executed substantially concurrently, or the blocks may
sometimes be executed in the reverse order, depending upon the
functionality involved. It will also be noted that each block of
the block diagrams and/or flowchart illustration, and combinations
of blocks in the block diagrams and/or flowchart illustration, can
be implemented by special purpose hardware-based systems that
perform the specified functions or acts or carry out combinations
of special purpose hardware and computer instructions.
[0057] It is to be understood that although this disclosure
includes a detailed description on cloud computing, implementation
of the teachings recited herein are not limited to a cloud
computing environment. Rather, embodiments of the present invention
are capable of being implemented in conjunction with any other type
of computing environment now known or later developed.
[0058] Cloud computing is a model of service delivery for enabling
convenient, on-demand network access to a shared pool of
configurable computing resources (e.g., networks, network
bandwidth, servers, processing, memory, storage, applications,
virtual machines, and services) that can be rapidly provisioned and
released with minimal management effort or interaction with a
provider of the service. This cloud model may include at least five
characteristics, at least three service models, and at least four
deployment models.
[0059] Characteristics are as follows:
[0060] On-demand self-service: a cloud consumer can unilaterally
provision computing capabilities, such as server time and network
storage, as needed automatically without requiring human
interaction with the service's provider.
[0061] Broad network access: capabilities are available over a
network and accessed through standard mechanisms that promote use
by heterogeneous thin or thick client platforms (e.g., mobile
phones, laptops, and PDAs).
[0062] Resource pooling: the provider's computing resources are
pooled to serve multiple consumers using a multi-tenant model, with
different physical and virtual resources dynamically assigned and
reassigned according to demand. There is a sense of location
independence in that the consumer generally has no control or
knowledge over the exact location of the provided resources but may
be able to specify location at a higher level of abstraction (e.g.,
country, state, or datacenter).
[0063] Rapid elasticity: capabilities can be rapidly and
elastically provisioned, in some cases automatically, to quickly
scale out and rapidly released to quickly scale in. To the
consumer, the capabilities available for provisioning often appear
to be unlimited and can be purchased in any quantity at any
time.
[0064] Measured service: cloud systems automatically control and
optimize resource use by leveraging a metering capability at some
level of abstraction appropriate to the type of service (e.g.,
storage, processing, bandwidth, and active user accounts). Resource
usage can be monitored, controlled, and reported, providing
transparency for both the provider and consumer of the utilized
service.
[0065] Service Models are as follows:
[0066] Software as a Service (SaaS): the capability provided to the
consumer is to use the provider's applications running on a cloud
infrastructure. The applications are accessible from various client
devices through a thin client interface such as a web browser
(e.g., web-based e-mail). The consumer does not manage or control
the underlying cloud infrastructure including network, servers,
operating systems, storage, or even individual application
capabilities, with the possible exception of limited user-specific
application configuration settings.
[0067] Platform as a Service (PaaS): the capability provided to the
consumer is to deploy onto the cloud infrastructure
consumer-created or acquired applications created using programming
languages and tools supported by the provider. The consumer does
not manage or control the underlying cloud infrastructure including
networks, servers, operating systems, or storage, but has control
over the deployed applications and possibly application hosting
environment configurations.
[0068] Infrastructure as a Service (IaaS): the capability provided
to the consumer is to provision processing, storage, networks, and
other fundamental computing resources where the consumer is able to
deploy and run arbitrary software, which can include operating
systems and applications. The consumer does not manage or control
the underlying cloud infrastructure but has control over operating
systems, storage, deployed applications, and possibly limited
control of select networking components (e.g., host firewalls).
[0069] Deployment Models are as follows:
[0070] Private cloud: the cloud infrastructure is operated solely
for an organization. It may be managed by the organization or a
third party and may exist on-premises or off-premises.
[0071] Community cloud: the cloud infrastructure is shared by
several organizations and supports a specific community that has
shared concerns (e.g., mission, security requirements, policy, and
compliance considerations). It may be managed by the organizations
or a third party and may exist on-premises or off-premises.
[0072] Public cloud: the cloud infrastructure is made available to
the general public or a large industry group and is owned by an
organization selling cloud services.
[0073] Hybrid cloud: the cloud infrastructure is a composition of
two or more clouds (private, community, or public) that remain
unique entities but are bound together by standardized or
proprietary technology that enables data and application
portability (e.g., cloud bursting for load-balancing between
clouds).
[0074] A cloud computing environment is service oriented with a
focus on statelessness, low coupling, modularity, and semantic
interoperability. At the heart of cloud computing is an
infrastructure that includes a network of interconnected nodes.
[0075] FIG. 5 is a block diagram depicting a cloud computing
environment 50 in accordance with at least one embodiment of the
present invention. Cloud computing environment 50 includes one or
more cloud computing nodes 10 with which local computing devices
used by cloud consumers, such as, for example, personal digital
assistant (PDA) or cellular telephone 54A, desktop computer 54B,
laptop computer 54C, and/or automobile computer system 54N may
communicate. Nodes 10 may communicate with one another. They may be
grouped (not shown) physically or virtually, in one or more
networks, such as Private, Community, Public, or Hybrid clouds as
described hereinabove, or a combination thereof. This allows cloud
computing environment 50 to offer infrastructure, platforms and/or
software as services for which a cloud consumer does not need to
maintain resources on a local computing device. It is understood
that the types of computing devices 54A-N shown in FIG. 5 are
intended to be illustrative only and that computing nodes 10 and
cloud computing environment 50 can communicate with any type of
computerized device over any type of network and/or network
addressable connection (e.g., using a web browser).
[0076] FIG. 6 is a block diagram depicting a set of functional
abstraction model layers provided by cloud computing environment 50
depicted in FIG. 5 in accordance with at least one embodiment of
the present invention. It should be understood in advance that the
components, layers, and functions shown in FIG. 6 are intended to
be illustrative only and embodiments of the invention are not
limited thereto. As depicted, the following layers and
corresponding functions are provided:
[0077] Hardware and software layer 60 includes hardware and
software components. Examples of hardware components include:
mainframes 61; RISC (Reduced Instruction Set Computer) architecture
based servers 62; servers 63; blade servers 64; storage devices 65;
and networks and networking components 66. In some embodiments,
software components include network application server software 67
and database software 68.
[0078] Virtualization layer 70 provides an abstraction layer from
which the following examples of virtual entities may be provided:
virtual servers 71; virtual storage 72; virtual networks 73,
including virtual private networks; virtual applications and
operating systems 74; and virtual clients 75.
[0079] In one example, management layer 80 may provide the
functions described below. Resource provisioning 81 provides
dynamic procurement of computing resources and other resources that
are utilized to perform tasks within the cloud computing
environment. Metering and Pricing 82 provide cost tracking as
resources are utilized within the cloud computing environment, and
billing or invoicing for consumption of these resources. In one
example, these resources may include application software licenses.
Security provides identity verification for cloud consumers and
tasks, as well as protection for data and other resources. User
portal 83 provides access to the cloud computing environment for
consumers and system administrators. Service level management 84
provides cloud computing resource allocation and management such
that required service levels are met. Service Level Agreement (SLA)
planning and fulfillment 85 provide pre-arrangement for, and
procurement of, cloud computing resources for which a future
requirement is anticipated in accordance with an SLA.
[0080] Workloads layer 90 provides examples of functionality for
which the cloud computing environment may be utilized. Examples of
workloads and functions which may be provided from this layer
include: mapping and navigation 91; software development and
lifecycle management 92; virtual classroom education delivery 93;
data analytics processing 94; transaction processing 95; and
learning path framework generation 96.
[0081] The descriptions of the various embodiments of the present
invention have been presented for purposes of illustration but are
not intended to be exhaustive or limited to the embodiments
disclosed. Many modifications and variations will be apparent to
those of ordinary skill in the art without departing from the scope
and spirit of the invention. The terminology used herein was chosen
to best explain the principles of the embodiment, the practical
application or technical improvement over technologies found in the
marketplace, or to enable others of ordinary skill in the art to
understand the embodiments disclosed herein.
* * * * *