U.S. patent application number 14/074093 was filed with the patent office on 2014-03-06 for real time learning and self improvement educational system and method.
The applicant listed for this patent is Erwin Ernest Sniedzins. Invention is credited to Erwin Ernest Sniedzins.
Application Number | 20140065596 14/074093 |
Document ID | / |
Family ID | 49681460 |
Filed Date | 2014-03-06 |
United States Patent
Application |
20140065596 |
Kind Code |
A1 |
Sniedzins; Erwin Ernest |
March 6, 2014 |
REAL TIME LEARNING AND SELF IMPROVEMENT EDUCATIONAL SYSTEM AND
METHOD
Abstract
A computer-implemented method of generating learning exercises
is provided. The method comprises receiving text, processing the
text using linguistic parsers to generate linguistic
characteristics of the text, storing the linguistic characteristics
in a data file, retrieving user information comprising a user
knowledge level and user goals, using the stored linguistic
characteristics and the user information to generate the learning
exercises based on a parametrical model, receiving responses to the
learning exercises from the user, and updating the user information
based on the responses to the learning exercises. The linguistic
characteristics comprise words of the text and relationships
between the words.
Inventors: |
Sniedzins; Erwin Ernest;
(Toronto, CA) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
Sniedzins; Erwin Ernest |
Toronto |
|
CA |
|
|
Family ID: |
49681460 |
Appl. No.: |
14/074093 |
Filed: |
November 7, 2013 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
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13183945 |
Jul 15, 2011 |
8602793 |
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14074093 |
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11824871 |
Jul 5, 2007 |
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13183945 |
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60807028 |
Jul 11, 2006 |
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Current U.S.
Class: |
434/362 |
Current CPC
Class: |
G09B 19/00 20130101;
G09B 7/00 20130101; G09B 5/00 20130101 |
Class at
Publication: |
434/362 |
International
Class: |
G09B 7/00 20060101
G09B007/00 |
Claims
1. A computer-implemented method of generating learning exercises,
the method comprising: receiving text; processing the text using
linguistic parsers to generate linguistic characteristics of the
text, the linguistic characteristics comprising words of the text
and relationships between the words; storing the linguistic
characteristics in a data file; retrieving user information
comprising a user knowledge level and user goals; using the stored
linguistic characteristics and the user information to generate the
learning exercises based on a parametrical model; receiving
responses to the learning exercises from the user; and updating the
user information based on the responses to the learning
exercises.
2. The computer-implemented method of claim 1, further comprising:
using the stored linguistic characteristics and the updated user
information to generate subsequent learning exercises based on the
parametrical model; receiving subsequent responses to the
subsequent learning exercises; and updating the updated user
information based on the subsequent responses.
3. The computer-implemented method of claim 1, wherein the
relationships between the words comprise syntactic links, semantic
schemes, grammar rules and/or keyword structures.
4. The computer-implemented method of claim 1, wherein the user
information further comprises user personal information and/or user
learned information.
5. The computer-implemented method of claim 1, wherein the
linguistic parsers comprise a syntactic parser, a semantic parser,
a grammar extractor and/or a marquee summarizer.
6. The computer-implemented method of claim 1, wherein the
processing further comprises extracting sentences from the text and
processing the sentences using the linguistic parsers.
7. The computer-implemented method of claim 1, wherein parameters
of the parametrical model comprise subject, category, learning
technology and/or educational mode.
8. The computer-implemented method of claim 1, further comprising,
prior to retrieving the user information, administering a user
placement test to determine the user knowledge level.
9. The computer-implemented method of claim 6, wherein the
processing further comprises highlighting parts of speech, grammar
patterns, and/or keywords in the sentences according to a color
coding scheme.
10. A non-transitory computer-readable medium having stored thereon
instructions to generate learning exercises, the instructions, when
executed by a processor, cause the processor to: receive text;
process the text using linguistic parsers to generate linguistic
characteristics of the text, the linguistic characteristics
comprising words of the text and relationships between the words;
store the linguistic characteristics in a data file; retrieve user
information comprising a user knowledge level and user goals; use
the stored linguistic characteristics and the user information to
generate the learning exercises based on a parametrical model;
receive responses to the learning exercises from the user; and
update the user information based on the responses to the learning
exercises.
11. The non-transitory computer-readable medium of claim 10,
wherein the instructions further cause the processor to: use the
stored linguistic characteristics and the updated user information
to generate subsequent learning exercises based on the parametrical
model; receive subsequent responses to the subsequent learning
exercises; and update the updated user information based on the
subsequent responses.
12. The non-transitory computer-readable medium of claim 10,
wherein the relationships between the words comprise syntactic
links, semantic schemes, grammar rules and/or keyword
structures.
13. The non-transitory computer-readable medium of claim 10,
wherein the user information further comprises user personal
information and/or user learned information.
14. The non-transitory computer-readable medium of claim 10,
wherein the linguistic parsers comprise a syntactic parser, a
semantic parser, a grammar extractor and/or a marquee
summarizer.
15. The non-transitory computer-readable medium of claim 10,
wherein process the text further comprises extract sentences from
the text and process the sentences using the linguistic
parsers.
16. The non-transitory computer-readable medium of claim 10,
wherein parameters of the parametrical model comprise subject,
category, learning technology and/or educational mode.
17. The non-transitory computer-readable medium of claim 10,
wherein the instructions further cause the processor to, prior to
retrieving the user information, administer a user placement test
to determine the user knowledge level.
18. The non-transitory computer-readable medium of claim 15,
wherein process the text further comprises highlight parts of
speech, grammar patterns, and/or keywords in the sentences
according to a color coding scheme.
Description
RELATED APPLICATIONS
[0001] This application is a continuation of U.S. patent
application Ser. No. 13/183,945 filed Jul. 15, 2011, which is a
continuation-in-part of U.S. patent application Ser. No. 11/824,871
filed Jul. 5, 2007, both of which are incorporated herein by
reference. This application claims the benefit of U.S. Patent
Application No. 60/807,028, filed on Jul. 11, 2006, which is
incorporated herein by reference.
TECHNICAL FIELD
[0002] Embodiments relate to systems and methods for education,
self-learning and self improvement. In particular, embodiments
involve real time learning and self improvement educational system
and methods
BACKGROUND
[0003] The School of Information Management and Systems at the
University of California at Berkeley concluded that the world
generates up to two (2) exabytes.sup.(13) of information per year
and that, "This world's total yearly production of information
amounts to about 250 megabytes for each man, woman, and child on
earth. It is clear that we are all drowning in a sea of
information. The challenge is to learn to swim in that sea rather
than drown in it. Better understanding and better tools are
desperately needed if we are to take full advantage of the
ever-increasing supply of information."
[0004] An individual's enjoyment of and success in life is to a
large extent determined by his or her skills and knowledge to
handle and learn the information that is presented to them each
day. A person's skill set and knowledge base are often viewed as
assets of great value and importance to learn new information. The
time an individual spends on self improvement and education in his
or her lifetime is ever increasing. To this end many people
continually search for new and more efficient ways of adding to
their skill set and knowledge base. In addition, a person's ability
to effectively improve his or herself, through for example, adding
to his or her skill set or knowledge base, in part, determines that
person's level of confidence and disposition.
[0005] An individual's skills and knowledge influence the ability
of that person to succeed in school, to practice a career of his or
her choice, to succeed in the practice of a given career and his or
her ability to switch careers as he or she desires. Modern
economies are to a large extent based on skills and knowledge and
therefore it is not surprising that an individual's success is, in
part, determined by the set of skills and knowledge that the
individual can offer to a potential employer or client. Thus, in
hopes of achieving financial success and the enjoyment of one's
career, people often expend large amounts of energy and money on
improving their knowledge base and skills set.
[0006] In addition, a person's skills set and knowledge base
influence that person's ability to enjoy life in general. For
example, these assets can influence a person's ability to travel
and live in different countries, to communicate with people of
various backgrounds, to enjoy art and literature from various parts
of the world and to simply understand the world around them.
[0007] Thus, a person's skill set and knowledge base, as well as
his or her ability to add to these two assets, impact on his or her
life in many ways. This includes but is not limited to his or her
financial success, confidence, and enjoyment of life in
general.
[0008] With the emergence of the Internet and digital media, there
is a wealth of knowledge and information available to a great
number of people who have access to a computing device. However,
the information available on the Internet is often dispersed over
many web sites. Similarly, the information on digital media may be
dispersed over a great number of files. Furthermore, the sheer
amount of new information available, about 250 Megabytes per year,
may be so daunting as to discourage individuals from attempting to
learn it and turn it into their own skills or knowledge. Moreover,
even if an individual locates relevant information, organizes it,
and is determined to learn it, he or she may still have difficulty
learning or applying it in an effective manner.
[0009] Thus, there is a need for a system and method that can
efficiently and effectively aid individuals in reducing the amount
of information presented, educating and improving themselves
through learning information that they can turn into skills and
knowledge faster and easier than traditional (passive) methods.
THE PRIOR ART
[0010] One element of the prior art is shown in US published
pending patent application 2004/0153509 to Alcorn (the Alcorn
reference).
[0011] It is submitted that the Alcorn reference simply discloses
an interactive computer system which allows for user based
selective access to pre-defined course materials. As such it is
submitted that the Alcorn reference has nothing whatsoever to do
with the subject matter of the present invention as disclosed. It
is acknowledged that in every complex system a user-based access
structure to previously prepared materials is commonplace.
[0012] The system of the present invention seeks out its raw
material primarily from textual subject matter provided or obtained
on line which is then processed by the operation of the system of
the present invention. This is referred to as unstructured content.
Only optionally does the system of the present invention seek out
structured or pre-prepared content for combination with the
unstructured content.
[0013] The following extracts from the Alcorn reference are
noted.
[0014] Alcorn [0015] The present invention relates generally to
systems and methods for the exchange of information between
instructors and students in an educational context. More
specifically, the present invention relates to systems and methods
in which an educational instructor interacts with one or more
non-collocated students by transmitting course lectures, textbooks,
literature, and other course materials, receiving student questions
and input, and conducting participatory class discussions using an
electronic network. . . . [0016] In addition, the present invention
relates to systems and methods that may be used by system users at
various levels for the distribution and use of information over a
network. . . . [0017] Therefore, it is a general object of the
present invention to provide a system and methods that allow users
to interact with a computer network-based education support system
through means of a simplified, easy-to-use user interface. . . .
[0018] A further object of the invention is to provide a system
that allows multiple types of users to access the features of the
system as a function of their predefined role within the framework
of the system, such as, a student, a teacher, or an administrator.
[0019] It is a further object of the invention to provide such a
system that integrates with the education platform so that there
will be provided therein value added services and control such as
calendar, task, contact and communication functions. . . . [0020]
An even still further object of the present invention is to provide
a system and method that is accessible according to the access
level of the system user. . . .
SUMMARY OF THE INVENTION
[0020] [0021] In accordance with these and other objects, provided
is a system for providing to a community of users access to a
plurality of on-line courses, comprising a plurality of user
computers and a server computer in communication with each of the
user computers over a network that includes LANs, MANs, WANs, the
Internet, intranet, and/or the WWW. Each user computer is
associated with a user of the system having predefined
characteristics indicative of a predetermined access level to the
system. Each level of access to data files is associated with a
course, and a level of control over data files associated with a
course. The preferred server computer is capable of storing data
files associated with a course assigning a level of access to each
file, determining an access level of a user requesting access to a
file, and allowing access to a file associated with a course as a
function of the access level of the user. Accordingly, the level of
access preferably is associated with the ability of a user to
access the file. [0022] . . .Also, the user may be provided with an
access level to enable creation of a student file associated with a
file for which the student user is able to read. The file that the
student is able to read may be an assessment file created by the
instructor user, and the student file created by the student user
is a response to the assessment file. The assessment file may be a
plurality of examination questions selected by the instructor user
to assess the learning level of the student user. The examination
questions may be selected by the instructor user from a
predetermined pool of available examination questions. The
examination questions also may be created by the instructor user
substantially at the time of the creation of the assessment file
and optionally added to the pool. The student file may be reviewed
by the instructor user and assigned a grade, which would be made
available on-line to the student user. The instructor user may
collate the grades obtained from reviewing a number of student
files, and the collated grades may be made available on-line to all
student users associated with the course. [0023] A user may be
required to enter a logon sequence into a user computer in order to
be provided with access to course files associated with that user.
The user is then provided with access to all courses with which the
he/she is associated after entry of the logon sequence. The user is
provided with a web page that may include a plurality of course
hyperlinks. These course hyperlinks preferably will be associated
with each course that the user has been enrolled either as an
instructor or as a student. Selection of a course hyperlink will
provide the user with a web page associated with the selected
course. This web page will have content hyperlinks and buttons to
various content areas associated with the course. The content
hyperlinks and/or buttons may include, for example, an announcement
area hyperlink, a course information hyperlink, a staff information
hyperlink, a course documents hyperlink, an assignments hyperlink,
a communications hyperlink, and a student tools hyperlink. [0024]
The present invention also includes a method for providing on-line
education that further may include the steps of establishing a
course to be offered on-line, offering the course to be taken
on-line to a group of student users, and providing access over the
network to the course files to student users who have enrolled in
the course. The establishment of the course includes an instructor
user generating a set of course files for use in teaching the
course, then transferring the course files to a server computer for
storage. The stored files will be accessible by a predefined
community of student users having access to the server computer
over a network. (emphasis added)
[0025] Another element of the prior art is shown in US published
pending patent application 2007/0026375, the Dewey reference. This
reference shows a different approach from Alcorn whereby an student
electronic workbook is generated programatically. Variability is
introduced by the generation of student materials on demand by the
use of "standardized web page templates and obtains its content
from a relational database of reusable web page components
comprising multi-media study materials and practise exercises for
all students" {para 0013}. Thus, the Dewey system seeks to adapt a
common library of pre-prepared study materials and tests to
individual users by introducing a layer of web page templates as a
basis for "variability" which the present applicant would call
user-customization.
[0026] The following extracts from the Dewey reference are noted.
[0027] Data components are stored in a server-side data storage and
retrieval means of a type widely known in the art. The data storage
and retrieval means in the depicted embodiment includes a
multi-media bi-lingual dictionary (68), a glossary of English
language grammar terms and their definitions (66), a library of
graphical images (74), a library of playable sounds (76), a library
of electronic books and stories for reading practice (70), a
library of educational games and puzzles for reinforcement of
subject matter (80), a library of reusable workbook web page
templates (72), a library of client-side scripts for web page
interactivity (78), and a record of current and completed study and
practice assignments (56) with each record cross-referenced to its
corresponding web page template (72). . . . [0028] . . . and a
server-side script processing method (28) that interprets and
executes programmatic instructions encoded in certain files stored
in the data storage and retrieval means (22) and generates web
pages formatted with Hypertext Markup Language (HTML) for the web
page server method (26), which web pages also contain embedded
client-side scripts for execution in the client-side student
computer (10) when the web page is delivered. . . . [0029] The
student may click on an electronic glossary of grammar terms menu
item to look up a term in the electronic grammar (66). The student
may type in the term to be looked up using a keyboard, or the
student my select the desired term from on-screen text by using a
pointing and selecting device. The invention will retrieve and
display the desired term and its definition, and an example of
correct usage from the data storage and retrieval means.
[0030] As can be seen the focus of the Dewey system is the
processing of pre-organized course materials organized into
libraries and resources both server-side and client-side so as to
adapt to each individual student so as to provide immediate access
by choice, thus alleged "variability".
[0031] Nothing in Dewey even suggests its application to the online
system of the present invention which processes input textual data
from any source in such a way that exercises and other functions
can be programmatically generated in huge numbers depending upon
the amount of input textual data (ie indefinite), presented to the
user and carried out on line, all in real time, with or without
optional structured course materials.
[0032] The prior art statement in Dewey assists in this matter
since it points out those advances made by Dewey and described, as
noted below: [0033] U.S. Pat. No. 6,793,129 by Wood, et al. (2004)
discloses an electronic portable study aid apparatus with the
ability to download instructional materials from a server. However,
the Wood patent is not a complete and comprehensive study aid and
practice aid, since it is missing coordinated collateral reference
materials. The Wood patent makes no provision for retained data
storage of student inputs and makes no provision for the automated
generation and assembly of the downloadable materials. [0034] U.S.
Pat. No. 6,146,148 by Stuppy (2000) discloses a method for
generating and delivering an electronic student workbook. The
present invention is an improvement on the Stuppy patent. The
electronic student workbook disclosed in the Stuppy patent is
structured as a teaching aid and contains lesson teaching material
and testing exercises for a student to perform. A teacher oversees
the delivery of teaching material and oversees student responses to
testing exercises. The present invention is an improvement because
it is structured as a self-help study aid and practice aid and
contains self-help lesson review material and practice exercises
for a student to perform without the aid of a teacher or tutor. A
programmatic method oversees the delivery of study material and
oversees student responses to practice exercises. [0035] The
electronic student workbook disclosed in the Stuppy patent is
generated by selecting materials from a library of pre-authored
teaching lesson materials and testing exercise materials, according
to a profile of student skill gaps. The electronic workbook in the
present invention is an improvement because it is generated
programmatically from standardized web page templates and obtains
its content from a relational database of reusable web page
components comprising multi-media study materials and practice
exercises suitable for all students. [0036] The present invention
is a further improvement because it integrates the content and use
of electronic student workbook pages with the content and use of
various online collateral study-aid and practice-aid reference
materials which all draw their content from a common database--to
assure that such contents are always fresh and always consistent.
[0037] U.S. Pat. Nos. 6,592,379 and 6,666,687 and 6,733,295 and
6,749,434 by Stuppy disclose continued improvements in the basic
design first disclosed in the '148 patent. They do not, however,
depart from the core principle of a teacher-student interaction and
workbook containing teaching and testing materials based on a
student skill profile. The present disclosed invention does not
require or include a teacher. The present invention delivers
standardized and graduated self-study and practice materials, not
skill-gap specific teaching and testing materials. [0038] U.S. Pat.
No. 6,898,411 by Ziv-el, et al. discloses a method and system for
online teaching using teachers, teacher computers, student
computers, and web page based electronic workbooks in which web
pages are retrieved according to their uniform resource locators
(URL). The present invention is an improvement because the web
pages in the present invention are generated on-demand and are not
pre-stored and retrieved according to static URLs.
[0039] The Dewey disclosure speaks of on-demand generation but it
should be noted that what is being done is the use of web-page
formats to deliver standardized and graduated self-study and
practise materials to the student as the course continues.
[0040] Nowhere in Dewey is there any suggestion that raw and
unstructured textual data might be engaged in real time by an
educational system, the subject of the present invention.
[0041] Paragraphs 0029 and 0061 of the Dewey reference storage and
retrieval. These paragraphs are set out here for convenience.
[0042] Data components are stored in a server-side data storage and
retrieval means of a type widely known in the art. The data storage
and retrieval means in the depicted embodiment includes a
multi-media bi-lingual dictionary (68), a glossary of English
language grammar terms and their definitions (66), a library of
graphical images (74), a library of playable sounds (76), a library
of electronic books and stories for reading practice (70), a
library of educational games and puzzles for reinforcement of
subject matter (80), a library of reusable workbook web page
templates (72), a library of client-side scripts for web page
interactivity (78), and a record of current and completed study and
practice assignments (56) with each record cross-referenced to its
corresponding web page template (72). [0043] The student may click
on an electronic dictionary menu item to look up a word in the
electronic dictionary (68). The student may type in the word to be
looked up using a keyboard, or the student my select the desired
word from on-screen text by using a pointing and selecting device.
The invention will retrieve and display the desired word and its
definition, an example of correct usage, the correct port of
speech, an illustration of the word when such an illustration
exists in the data storage and retrieval means, and a translation
of the word into another language when such a translation exists in
the data storage and retrieval means. (emphasis added)
[0044] Dewey provides nothing more than user access to electronic
dictionaries on request in the manner of a variety of prepared
comparative sources of information such as illustrations, examples
and translations.
[0045] In the present invention the user is provided with a wide
variety of presentations at the subject matter level to both
unstructured and optional structured information inter-related with
user learning level, results and life style choices. This is an
entirely different user experience as the present invention seeks
to educate based upon broad meaning derived by the system from
textual input.
[0046] The prior art focuses entirely upon prepared materials
(herein referred to as `structured` materials), often referred to
as course materials, solely, and then seeks to adapt to the student
user on an ongoing basis. This approach is limited to the
pre-prepared structured materials, thus limiting the results
available.
[0047] Another element of the prior art is shown in US published
pending patent application 2005/0227216 to Gupta (the Gupta
reference). In the Gupta reference the disclosed invention is
described as a distributed management system for pre-configured
data. Variability is provided by the user's choice of logic
functions and devices at hand. The Gupta reference provides as
follows: [0048] A system is provided for managing academic and
social life for students and includes a central server connected to
a wide-area network and storing a repertoire of logic functions for
use by students in managing academic activities, and a plurality of
computerized appliances associated with individual students, the
computerized appliances connectable to the wide-area network. In
one embodiment, students may download logic from the central
server, and execute the logic to configure and manage activities
related to classes and studies in a college or university, and may
interact with other students through the central server. [0049] In
a preferred embodiment, the wide-area network is the Internet
network. In one embodiment, the computerized appliances associated
with individual students connect to the wide-area network through a
wireless system. Also in one embodiment, the central server
includes a suite of logic functions for teachers to interact with
the central server and student users. [0050] In a preferred
embodiment the system enables integrating and managing academic and
social activities from a single point of control and further
includes, in one embodiment, one or more distributed servers
connected to the wide-area-network, the servers adapted for
network-based academic learning and for communication with the
central server over the network; one or more content servers
connected to the wide-area-network, the content servers adapted to
provide Web-based content and services and adapted for
communication with the central server; and one or more instances of
software distributed to network-capable computing devices, the
software instances adapted for managing aspects of academic and
social activities in conjunction with the main server for the
operators of the computing devices. . . . [0051] In one embodiment,
the instances of software are student interfaces and the operators
are students. In another embodiment, the instances of software are
faculty interfaces and the operators are members of a faculty. In a
preferred embodiment, the instances of software are a mix of
student and faculty interfaces and the operators are a mix of
students and faculty. [0052] In one embodiment, the at least one
server adapter includes software for interfacing, for data
migration, for data abstraction, and for XML data generation. In
this embodiment, the central server further includes software for
generating application templates, for building applications, and
for integrating third-party content into end-user display data.
[0053] In a preferred embodiment, the network-capable computing
devices are one or a combination of laptop computers, desktop
computers, cellular telephones, and personal digital assistants.
Also in a preferred embodiment, the central server further includes
data storage volumes personalized and allocated for use by the
server and by the operators of the computing devices. [0054]
According to another aspect of the present invention, a graphics
user interface is provided for controlling and for managing aspects
of academic and social interaction of the interface operator. The
interface includes a registration and configuration component for
enabling registration and configuration of the interface to receive
data from and to enable interaction with a central server; a
networking component for enabling navigation on a data network; a
messaging component for receiving and sending message content over
the network; a processing component for enabling document
generation, data manipulation, and mathematical calculations based
on rule; and a file sharing component for enabling data sharing
between interface operators according to file permissions and
rules. . . . [0055] In a preferred embodiment, the processing
component includes a utility for taking class notes. In a preferred
aspect of this embodiment, the utility further includes automatic
file naming and archiving capabilities related to course
description. In one embodiment, the file-sharing component is
integrated with the messaging component. In this embodiment, the
file-sharing component includes sharing of calendar data according
to file permissions.
[0056] Also cited are US published pending patent applications
2007/0166684 published 2007 Jul 19 USPPA to Walker and 2003/0068604
published 2003 Apr. 10 to Krasney. Neither of these references add
to prior art as described above.
[0057] The prior art describes a need for a substantial range of
student learning exercises of a wide variety in type, content and
number. These are implemented to teach new material, support and
assess the student's interactions with the learning process.
Expensive and time consuming teacher involvement is to be avoided.
Unfortunately, with all prior art systems variability is provided
by choices from fixed material arranged beforehand, herein referred
to as structured content and not generated in real time. This
highly limits the number of choices any such system can present as
well as the structured material being presented. For example,
authoring tools are disclosed in U.S. Pat. No. 6,018,617, U.S. Pat.
No. 6,704,741, U.S. Pat. No. 6,259,890, and others. In other
examples, a computer-based test is generated from a database of
exercises created through other means (e.g., an authoring toot or
automatic means). Such computer-based testing systems are disclosed
in GB237362B, U.S. Pat. No. 5,565,316, US2004234936A, and others.
In still other examples exercises are sought to be created
completely automatically. Automatic generation for structured
content in respect of textual material are reported as described by
Brown et al. (Automatic Question Generation for Vocabulary
Assessment, Proc. of the Conf. on Human Language Technology, 2005),
Mostow et al. (Using Automated Questions to Assess Reading
Comprehension, Cognition and Learning Vol. 2, 2004), Coniam (A
Preliminary Inquiry Into Using Corpus Word Frequency Data in the
Automatic Generation of English Language Cloze Tests, CALICO
Journal Vol. 14 Nos. 2-4, 1997), Aist (Towards Automatic
Glossarization, Int. J. of Artificial Intelligence in Education
Vol. 12, 2001), Hoshino and Nakagawa (A Real-Time Multiple-Choice
Generation for Language Testing, Proc. of the 2nd workshop on
Building Educational Applications using NLP, 2005), Sumita et al.
(Measuring Non-Native Speakers' Proficiency of English Using a Test
with Automatically-Generated Fill-In-The-Blank Questions, Proc. of
the 2nd Workshop on Building Educational Applications using NLP,
2005), Mitkov and Ha (Computer-Aided Generation of Multiple-Choice
Tests, Proc. of the Workshop on Building Educational Applications
using NLP, 2003), and in inventions as disclosed in U.S. Pat. No.
6,341,959, in Japanese 26,126,242 A, in Japanese 27,094,055 A2).
These methods focus on a target word or concept as input and then
use a two step process each depending on the type of exercise
required. Examples are multiple-choice exercises, fill-in-the-blank
questions and comprehension exercises generated by selecting a
sentence from the reading text that contains the target word or
concept and restructuring the sentence into a question.
[0058] Such exercises are valuable for learning and student
interactions but are based upon the selected textual material
itself and then work complex algorithms to produce suitable
exercises. Difficulty levels are adjusted by changing parameters.
In Nagy et al. (Learning Words From Context, Reading Research
Quarterly Vol. 20, 1985) difficulty is adjusted by varying the
closeness of the distractors to the correct answer on several
levels. In Mostow et al. (Using Automated Questions to Assess
Reading Comprehension, Cognition and Learning Vol. 2, 2004), and in
Aist (Towards Automatic Glossarization, Int. J. of Artificial
Intelligence in Education Vol. 12, 2001), for example, use is made
of varying the familiarity of the distractors and varying the
difficulty of the question stem. These do not control the level of
difficulty depending on the ability of any particular student as
exercises either have a predetermined level of difficulty, or worse
an unpredictable level depending on a random selection. That is, no
method uses any model of the user's current knowledge other than
individual manual control in personalized exercises.
[0059] One reported method for controlling the difficulty is shown
in Computer Adaptive Testing (CAT) (Wainer et al., Computerized
adaptive testing: A primer, Hillsdale, N.J.: Lawrence Erlbaum
Associates, 1990; van der Linden &t Hambleton (eds.), Handbook
of Modern Item Response Theory, London: Springer Verlag, 1997). In
CAT, a test item is selected for a particular student using a
statistical method to personalize the test to individual students;
however, test items must be generated beforehand and calibrated by
trialing each one on many real students, not created on demand.
[0060] Further, systems and methods have been developed to seek out
the content of textual materials in the form of individual
extractors and parsers such as are referred to, for instance,
in
[0061] In order to maintain learning success without boredom or
excessive guessing the system must be interactive on a real time
basis and directly related to the individual students request for
specific learning content and to that students actual ability
scores and test summaries.
[0062] In the past efforts to improve information value without
data overload have resulted in parsers and extractors of various
types being used, such as, for example as described in United
States Patent Application 2003/0130837 to Batchilo et al., Jul. 10,
2003, and in U.S. Pat. No. 5,774,845 to Ando, et al., Jun. 30, 1998
for an Information extraction processor, and the prior art items
cited in each of these documents. Purportedly, keyword and
structural analysis are used to improve performance and enhance
summaries of text information.
[0063] For example, in a textual string of 2000-4000 characters it
is expected that there will be about 1-750 unique words, namely
about .about.50% from all words. Each of these words can have
synonyms, antonyms, parts, part of, types, type of, relatives and
the like adding about 1-100 additional words. Such a grouping would
be expected to have about 1-50 sentences (sentN) with about 1-24
tenses, 1-10 word levels, 0-30 key words, 1-10 parts of speech. For
each sentence it is expected from further linguistic analysis that
there would be about 1-.about.3 subjects+predicates (Sub+predN),
0-.about.20 preposition phrases and about 0.8 Infinitives or
gerunds. Other phrases presented by syntactic and semantic links
would be expected to be about 1-.about.100. Thus, it can be seen
that much meaning can be found and separately extracted from
textual strings.
[0064] Given the high cost of educators and the variety of students
and teaming styles there has developed a growing need for automated
learning systems and methods which improve the relevancy and
transparency of learning with automated exercise generation as part
of a learning system without the need for pre-prepared structured
learning materials such as pre-prepared lessons and tests, whether
they be complete units or assembled into a whole from individual
completed parts created beforehand and stored for retrieval. Such
automated learning systems are needed to be flexible to both the
user, the user environment such as user history and the presence of
any learning institution along with the subject of interest.
[0065] Structured materials are known to seek to present useful
information taken by educators from the huge base of textual data
which is available on any subject of interest in a fashion which
can be relatively simply understood at a learning level and
learned.
[0066] Prior art systems have failed to produce flexible learning
systems which operate automatically without the need for
pre-prepared (structured) materials and provide the high degree of
variability necessary to maintain and advance the Learning process
of a variety of different users and types of users using
unstructured material.
OBJECTS OF THE INVENTION
[0067] It is an object of the Invention to provide an automated
learning system by which the user may select a subject area of
interest, as by a search or Learning request, and be presented with
a wide variety of options for learning, measuring advancement and
testing.
[0068] It is an object of the invention to provide an automated
learning system which processes input textual material to extract
elements of meaning by parsers and extractors and formulates a data
structure including the textual material and links identifying the
extracted elements.
[0069] It is a further object of the invention in response to a
learning request to process the data structure against updating
user information so as to identify the user level and goats against
the data structure and present the user with a variety of selected
automatically generated exercises and tests optionally obtained
from parametrical models of functionality for a variety of types of
exercises and tests.
[0070] It is a still further object of the invention to adapt and
update said user information based upon user learning performance
in the system.
[0071] It is yet a further object of the invention to provide a
learning system with an essentially unlimited number and variety of
learning exercises and tests from even small amounts of
unstructured materials.
SUMMARY OF THE INVENTION
[0072] The invention provides a computer-implemented system,
apparatus and a computer program for generating learning exercises
and providing an improved learning method operating in real time in
respect of a input stream of textual material with a real time
content processing block; including an input data module operable
upon a user learning content request to search for real time
content and adapted to separate textual parts of said real time
content into an input data set, dictionaries of linguistic patterns
comprising computer based rules for linguistic patterns analysis, a
content processing module adapted to extract and maintain sets of
linguistic identification links in said input data set based upon
said dictionaries of linguistic patterns, and, combine said input
data set and said linguistic identification links into a different
data set, and means to communicate said different data set, a user
life management block including a processing module for user data
including user object data, user life management data, a user
personal data set of user learned textual information, and, a
return module to provide user information relevant to said user
learning content request; a system management block providing
communication between parts of the system including a learning
management module operable upon a said user real time learning
content request to retrieve said different data set and generate a
request to said user life management block to return said relevant
user information, combine said retrieved user information with said
different data set, extract an unlearned data set, determine a user
level, to provide an exercise response based upon either said
different data set, said unlearned data set, said retrieved user
information and said user level, an exercise generation block
including an exercise generator module operating upon the exercise
response, and one or more of a data set of dictionaries, a data set
of picture dictionaries, a media library, a music library, a data
set of exercise types and functions, and as means to generate and
provide exercises in real time; with a graphics user interface
module operable to automatically present textual content as an
exercise and collect user responses.
BRIEF DESCRIPTION OF THE DRAWINGS
[0073] Embodiments are described in further detail below by way of
example only, with reference to the accompanying drawings, in
which.
[0074] FIG. 1 is a block diagram of the Real Time Learning and Self
Improvement Education System and Method, according to one
embodiment; showing Dictionaries and Linguistic Patterns 101.
[0075] FIG. 2 is a block diagram of the content processing module
of FIG. 1;
[0076] FIG. 3 is a flow chart illustrating the steps taken by the
learning management module of FIG. 1;
[0077] FIG. 4 is a representative illustration of the steps taken
by exercise generator module of FIG. 1 identifying elements N1
through N5.
[0078] FIG. 5 is a block diagram illustrating the components of the
text interaction exercises module of FIG. 1;
[0079] FIG. 6 is a schematic diagram illustrating the Generated
Exercise Model of the exercise generator module of FIG. 1;
[0080] FIG. 7 is a representative illustration of the steps taken
by the learning technology selection module of the learning
management module of FIG. 1;
[0081] FIG. 8 is a schematic diagram illustrating the collection of
learning technology components of the exercise generator module of
FIG. 1;
[0082] FIG. 9 is a representative illustration of the steps taken
by the Syntality component of FIG. 8;
[0083] FIG. 10 is a flow chart that illustrates the steps taken by
the basic essay writing module of FIG. 5;
[0084] FIG. 11 is a flow chart that illustrates the steps taken by
the life management module of FIG. 1;
[0085] FIG. 12 is a schematic diagram that illustrates the
components and functionality of the personal action success
strategy (PASS) module of FIG. 1;
[0086] FIG. 13 is a flow chart illustrating the steps taken
according to the typical life goals for online learning process of
FIG. 12;
[0087] FIG. 14 is a flow chart illustrating the steps taken
according to the 60 seconds exercises pop ups procedure of FIG.
12;
[0088] FIG. 15 is a flow chart illustrating the steps taken
according to the Real time learning Modeling of FIG. 1;
[0089] FIG. 16 is a flow chart illustrating the steps taken
according to the Real time learning content strategy procedure of
FIG. 15;
[0090] FIG. 17 is a flow chart illustrating the steps taken
according to the Transformation of information to interactive
content procedure of FIG. 15;
DETAILED DESCRIPTION
[0091] FIG. 1 illustrates an embodiment of a Real Time Learning
(RTL) and Self Improvement Educational System [hereinafter "RTSL
system" or "real time self-learning system"] 10. Real time self
learning system 10 takes any textual information from any digitized
document such as MS/HTML/XML and helps the user turn it into his or
her knowledge in an efficient manner and may thereby assist a
company to save or make money by creating documents, spreadsheets,
etc. from the user's acquired knowledge. The system will
automatically test the user on the textual information they want to
learn and provide automatic feedback scores and marks on the
interacted information they wanted to learn.
[0092] In one embodiment, real time self learning system 10 is a
Computer Aided Total Educational Learning System (CATEL) that helps
users to handle any data up to 30% or better faster and easier by
providing:
a) summarization and evaluation of any textual INFORMATION
presented in real time (RT) for fast and easy INPUT analysis. b)
internal PROCESSING and learning of this Real Time (RT) data
presented through visual, mnemonic and phonic interactivities. c)
OUTPUT summarization and automatic tests that are created to
evaluate the acquired level of knowledge from the same RT inputted
data presented or interacted with. d) continuous FEED BACK through
scores and marks on the interactive output results from this same
RT data for motivation and continuous knowledge improvement
opportunities.
[0093] The total Real Time Self-Learning (RTSL) system 10 provides
an online Learning process (please refer to Appendix 1: System
Aspects for further details) with:
1. Internet searches for real time user's content; 2. Generating
exercises & tests in real time along with structured teacher
prepared lessons (structured content); 3. Effective Learning
technologies utilizing visuals, mnemonics and phonics tools; 4. A
control system via a user's Personal Action Success Strategy (PASS)
module and a Life Management System.
[0094] Referring to FIG. 1, the RTSL system 10 comprises real time
content processing block 12. Main Management System block 14,
Exercise Generation block 16, Life Management and User block 18,
and the Presentation block 20.
[0095] Real time content processing block 12 includes: Input Data,
an Input Content Module (100), a Dictionary and Linguistic Patterns
Database (101), a Content processing Module (102).
[0096] Content processing module (102) processes input data and
extracts from its textual part, a different data set that provides
text meaning, style, grammar, keywords and others links. The format
of the content processing module output is XML.
[0097] Main Management System block 14 includes: a Learning
Management Module (107), and a Content Management Database (106).
Learning Management Module (107) is the main management module that
links together the functionality of the system modules. In the
Figures this linking is depicted arrow connections for processing
flow in either direction to link together the module functionality.
Structured Content Management Module (110) and Content Management
Database (106) are responsible to deliver a collection of
structured content to users and to allow teachers to create or edit
existing structured content in content management database
(106).
[0098] Structured Content Management Module 110 may interface with
content resources from other systems. A format of import data is
SCORM. Structured Content Management module 110 is the interface
module between existing systems and the real time system of the
invention. This module provides structured content data by
importing them from another system.
[0099] Exercise Generation block 16 includes: Exercise Generator
Module (113), Dictionaries (112), Picture Dictionaries (114), Media
Library (115), Music Library (116), and Text and Visual images
Interactions (118) and Real Time Learning (120). Exercise generator
module (113) synthesizes the process of creation of exercises from
any digitized text. Text Interaction Exercises module (118)
represents content dependent and content independent activities
including output Essay Writing (118.001). Presentation layer or
Graphics user interface modules (117) deliver the learning process
to the user for multi module implementation.
[0100] Life Management and User block 18 includes: User Object
(103), Life Management Module (104), Personal Picture Dictionary
(105), Personal Action Success Strategy (PASS) (108), Life
Management Database (109), Remote Learning Reminder Module (111),
and User's personal database (119). Life Management Module (104)
ties together personal user information, user knowledge level, user
goals and user life with textual content to be learned in real time
or with structured content prepared by teachers. PASS (Personal
Action Success Strategy) (108) module includes; a time management
system, goal setting, performance monitoring and tracking. Remote
Learning Reminder Module (111) supports PASS (108).
[0101] The Presentation block 20 includes the Graphics User
Interface Modules (117).
[0102] Using the graphic user interface module (117) a user can
select different learning opportunities from any textual content
presented from any digital device or communication channel such as
the Internet, LAN, CD-ROM, etc. Depending on the type of
information that the user wants to learn in the learning process
the system will:
1. provide online learning processes by searching for real time
content and preparing the different data set; 2. provide learning
processes by selection of a course or lessons prepared with
structured content; 3. manipulate the user's data from user's life
management database: [0103] To provide scheduled user content to
the learning process, [0104] To continue to follow the personal
scheduler to learn something, [0105] To create a personal learning
scheduler by time management system from PASS, [0106] use a
goal-setting system to create new goals or modify existing goals,
[0107] use a motivation system, [0108] create plans or to see own
achievements by score estimation system.
Real Time Content Learning Process
[0109] The real time content learning process begins from a user's
wide world or local search of content to learn. It could be the
User's favorite topic, any information from an Internet search,
news, business information, fiction, letters, etc. Input data
module (100) gets control and provides data input and primarily
filtration and processing.
[0110] Content processing module (102) provides a transformation of
any textual information to the data structures in a different data
set that describe such text extracts as sentences, key words,
linguistic structures, grammar structures, parts of speech, word
characteristics, and language text level and others. Output from
(102) is data in XML (content).
[0111] In this system all data structures are supported by a
serialization mechanism and can be transformed into XML.
[0112] Learning Management Module (107) will process this different
data setfrom (102) to create another XML package (content+user
information) for the exercise generator module. The package
contains information: real time content description and data from
user database including coefficients that represents knowledge
level of the user, their skills and achievements.
[0113] The exercise generator module (113) processes the XML
package (content+user Info) from (107) and generates XML package
(exercises) that describes list of exercises to learn real time
user content. XML package (exercises) will be saved to the user's
personal database (119). Life Management Module (104) is
responsible for the management of a user's personal information and
the calculation of the special parameters by the Personal Action
Success Strategy (PASS) (108) technique parameters. These
parameters are used to personalize the process of exercises in real
time generated by the real time exercise generator module (113).
The exercise generator module (113) uses collections of
dictionaries, Graphics. Media and Music Libraries and Real Time
Learning (120) to create exercises or tests from current
information that the user wants or needs to learn.
[0114] Presentation layer (117) implements exercise descriptions in
template approach by UI controls. A user can execute the generated
exercises and the result will be saved by the life Management
module (106) in the User's personal database (119).
Structured Content Learning Process
[0115] The structured content learning process opportunity begins
from a user search in Content Management Database (106). The search
can be provided by a subject, language level, grammar rules, skills
needs (listening, pronunciation, speaking) or keywords. Learning
Management Module (107) will process the search result, will create
XML package with requested content and will transfer data to
presentation layer (117).
User Life Management Control System (109)
[0116] The user life management control system starts to work after
user registration process will be finished. User's Object Data
Module (103) will include user personal membership information
(name, age, address, phone number, sex, etc) and special personal
information (birthday date, how many years user wants to live, wake
up time, bedtime, etc.). After registration process each user will
have possibilities to save information about their knowledge from
the PASS placement test, describe their attitude for their life
goals and achievements and other information.
[0117] All this information will be used by Life Management Module
(104) and saved in User's life management database (109) and user's
personal database (119). In addition, each user will have a
Personal selected images or Pictures for associated words and
Personal vocabulary dictionary builder (105).
[0118] When a user applies Life management control system, he can
reach three main functions of PASS (108): [0119] Time Management
System (108.001); [0120] Goal Setting System (108.002); [0121]
Performance Tracking System (108.003)
[0122] These functions allow the user to organize their life to
provide effective learning or self-improving process. Remote
Learning Reminder Module (111) helps to bring learning process to
the multimodal environment.
Real Time Content Processing Block 12
[0123] Reference is made to FIG. 1. Real time content processing
block 12 provides data input process and builds object structures
to describe data extracted from the input stream. Content
processing block 12 comprises: Input Data, Input Content Module
(100), Dictionary and Linguistic Patterns database (101), and
Content processing Module (102).
[0124] The learning content in this system may comprise any
information that a student will use for a learning process. In real
time in a web application the learning content can be gotten as a
result of a "SEARCH" function. For example, it can be web page
content. In case of a desktop application the learning content
could be any textual information that is loaded inside the
application by copy/paste operation or open file operation.
[0125] Reference is next made to FIG. 2, format processing module
(102.002) gets the input stream as Unicode string and extracts tags
(including HTML tags), special symbols, format symbols, short cuts,
abbreviations etc. A result of processing module (102.002) contains
two parts.
[0126] One of them represents a textual part of input and second
one represents a media part of information. The textual part is
data structure that describes input stream as string collection,
where each string has a descriptor. The descriptor represents the
string content. Here are some string examples: [0127] ASCII symbols
string: text string [0128] HTML tags: <image
src="picture.jpg">some text . . . </image>; [0129]
Abbreviation: U.S., P.M. [0130] Short cuts: n., pl. adj. v.
[0131] Another part of the module result is a list of files that
includes pictures, sound, video clips and other media content.
[0132] This embodiment of the different data set structure
describes the textual learning content and is called the Content
Description Structure (CDS).
[0133] The Sentences Extractor module (102.005) continues to
process the textual part of inputted information. This module uses
the original algorithm, based on token analyzing approach. The
result will add data to the CDS as a descriptor, which contains a
sentence number and marks for words that correspond to the first
and last words in a sentence.
[0134] A Marquee Summarizer module (102.006) uses the CDS as a
container for input and output data. The module puts more
descriptors into the CDS to define some words and phrases in
sentences as keywords or summarization items.
[0135] This module can use three embodiments that are implemented
by: [0136] An approach, which is simple and fast, that uses three
parameters for keyword extracting; [0137] An approach that is based
on a 17 parameters model; [0138] An approach, which uses semantic
schemes to extract keywords and main text idea
[0139] Syntactic Parser module (102.008) takes data from the CDS
and processes it by sentences. Results for each sentence describe
syntactic links between words in a sentence. A Descriptor with
references to these syntactic schemes will be added to the CDS.
[0140] Input data to the Semantic Parser module (102.008) are the
syntactic schemes for each sentence that were built by Syntactic
Parser module (102.008). Output data is a semantic scheme that
represents semantic links inside the text. A basic skeleton for the
semantic scheme is built as extracting subjects, direct objects and
predicates for each sentence in the text. The subjects and the
direct objects are edges and the predicates are links between these
edges. This graph can have loops, cycles and trailing edges.
[0141] More additionally graph dressed edges and links will be
added by extracting indirect objects, gerund phrases, infinitive
phrases, and others. A semantic description of the text presents by
chains, that extracting from semantic graph by reasoning rules. In
structure, the CDS will get descriptors that rules the Semantic
Parser module (102.008) output.
[0142] A Grammar Extractor module (102.009) takes syntactic links
and interprets them as grammar rules. Each grammar rule is
presented as a combination of syntactic links and time description.
This module processes each sentence separately and creates for each
sentence descriptor to grammar rules.
[0143] Therefore, all modules, which contains Content Processing
module (102), processes the text part of input stream to build
schemes and creates descriptors in the CDS.
[0144] Final XML document contains descriptors for [0145] Syntactic
Links (102.010); [0146] Semantic Schemes (102.011); [0147] Grammar
Rules (102,012); [0148] Sentences description (102.013); [0149]
Keywords and Summarization (102.014);
Main System Management Block 14
[0150] Reference is again made to FIG. 1. Main System Management
block 14 is used as a main control processor. It provides for
communication between all parts of a system. It is the main
management module that links together all system functional
modules. Main System Management block 14 comprises Learning
Management Module (107) and Content Management Database (106).
[0151] Main System Management module provides communications
between: Content processing Module (102), Life Management Module
(104), Exercise generator module (113), and Presentation block
(117).
[0152] Reference is now made to FIG. 3. Input data of Learning
Management Module (107) includes two parts. First part is presented
as Content Description (CD), (107.001). Second part is the User
information (107.002) that was transferred from Life Management
module (104). The Content description (107.001) is CDS structured.
It contains descriptor for type of content. The learning content
can include real time learning content, real time user scheduled
learning content, structured learning content.
[0153] According to content type the Analyze Content Description
module (107.003) creates a request to get a data structure, which
we will call URDS--User Request Description Structure. It contains:
[0154] type of content; [0155] processing mode (edit, show,
execute, print, . . . ); [0156] reference to content data as CDS.
In the case of structured and scheduled content contains the
descriptor for learning the content that user has choose; [0157]
reference to User Descriptor (UD), which represents user knowledge
reflection for current content (unknown words, subject score,
integrated text score, etc); [0158] reference to Exercise
Description structure (EDS); [0159] reference to user Object (103);
[0160] reference to PASS object (108.107);
[0161] The next step is module (107.004). This creates a new branch
in the processing road. In this case the structured content type
requested from (107.003) will provide database query for getting
structured content from database (module 107.005) and transferring
the structured content to Presentation Layer (module 107.009). In
that case, when the request is to a scheduled content, module
(107.005) makes a fork to module (107.007), which is responsible to
get data to describe scheduler for current content. When user needs
to work with Real time learning content, the module (107.008)
provides all data in that case. The module (107.010) will transfer
the data to Exercise Generator Module (113). Module (107.013) is
data block that represents URDS data structure.
[0162] Step (107.011)--"Send Structured content to Presentation
Layer" occurs after step (107.006). This process is presented by
serialization of data structure in XML format that uses a common
interface between Presentation Layer and Executable Layer. Such
interface makes Presentation Layer independent from those modules
which provide data for it.
Exercise Generation Block
[0163] Exercise Generation block 16 of FIG. 1 comprises: Exercise
Generator Module (113), Dictionaries and Picture Dictionaries
(112), Media and Music Libraries (115,116), Text Interactions
(118). Exercise Generation block 16 provides all data for
implementation of exercises.
[0164] Reference is now made to FIG. 4. Exercise Generator Module
(113) input data contains URDS that includes all data about user
request and chosen learning content. A parametrical model (113.002)
of functionality for the exercises is the main based platform for
exercise generation. This model has been constructed to bring a
formal approach for an exercise generating process. It allows the
creation of a scalable, reliable and increased mechanism
process.
[0165] Exercise Generator Module (113) consistently processes URDS
data according to the model parameters. The number of processing
modules equals the number of parameters, which the model includes
as follows: [0166] "Select Subject" module (113.003); (1) [0167]
"Select Categories" module (113.005); (3) [0168] "Make a decision
about a Learning Technology" module (113.007); (4) [0169] "Define
Educational mode" module (113.009); (5) [0170] "Select
functionality type" module (113.011); (6) [0171] "Generate input
data" module (113.013); (7) [0172] "Describe an exercise output"
module (113.015); (8) [0173] "Select Implementation Components"
module (113.017); (9)
[0174] All modules make selections and requests to the database
under control of URDS, paying attention to individual features of
the user and the achievements and goal settings. The building of
the user model starts when the Subject is selected (113.003). This
procedure depends only on the UD (User Description) and if the
system is processing structured learning content. For Real time
Learning Content the Subject is selected only from the users CD
(Content Description). Scheduled content assumes the use of both CD
and UD. Next step (113.005) will select category or main framework
to build the exercises. Step (113.007) provides duties to select a
learning technology. And so on.
[0175] For example, a user makes a request to learn a subject (1)
as "Clouds". The content for learning a topic is selected by the
user. This means the content is real time content (2)--user's
choice. Accordingly, the user's achievements for category (3)
exercises were selected as multiple choices, filing forms, visual
choices and finding rules. Learning technologies (4) selection was
Syntality's; visualization, associations, mind webbing and then
note taking. Educational or teaching mode (5) was as explanation
and training mode. The created approach (6) was selected as that
one which needs to generate only input data. The input data (7) for
exercises in this case has to be gotten from databases. To learn a
terminology of the subject it will get synonyms and rhymes for
keywords from Dictionary and Thesaurus. To learn meaning, sense and
communication links inside the subject may get questions and
answers from sources such as the online Wikipedia encyclopedia and
book repository. This information will be used to create
comprehension test as multiple choices. The information selection
from all database collections provided by "search" function were
search parameters includes estimations of user's skills and user's
knowledge level such as, language level, personal dictionary size,
comprehension level, goal settings, favorite topic, preferences,
etc. For this example, the output creation (8) will include
personal dictionary creation (association links, Syntality
pictures, notes, learning score for new words) and note taking
pages for a summarization of new knowledge.
[0176] An output of module (113) is an object that we will call
EDS--Exercise Description Structure. Each component of EDS
describes one exercise. The content of EDS is defined accordingly
as model (113.002). XML implementation of EDS was made under SCORM.
A reference of EDS will be included in the URDS.
[0177] Reference is now made to FIG. 5. Text Interaction Exercises
module (118) works independently from Exercise Generator Module
(113) data. It includes two parts.
[0178] First one contains modules which don't use real time
content. These modules are called content independent activities.
They are responsible for the creation of data or information. In
other words these modules make synthesis of a text or visual
information. These modules are: [0179] Essay Writing (118.001)
[0180] Root, prefix and suffix activities (118.002) [0181] Others
(118.003)--karaoke, phonemes training, visual patterns gallery, and
much more.
[0182] Second part contains modules that use real time content for
there activities.
[0183] These modules are responsible as an analysis and synthesis
of information presented. They are: [0184] Speed Reading modules
(118.010) provides eyes training exercises and speed reading tests.
[0185] Note Taking modules (118.011) provides text content meaning
extraction preparation procedures and note taking format writing
procedures. [0186] Text to Visual module (118.012) provides a
pictureliation (to transform or to show a text as pictures or
animated clips) of text by different sets of words.
[0187] All these modules use the current real time textual content
as input data.
Generated Exercise Modeling (113.002)
[0188] Reference is made to FIG. 6. Generated Exercise Model
(113.002) is a parametrical model that is the algorithm base for
Exercise Generator module (113). Generated Exercise Model (113.002)
provides such unique opportunities for self-teaching and
self-learning of any textual content in real time, with real time
motivation levels/status, with life long skills training, different
learning technologies as modern data processing techniques. In
order to build such kind of system, it is preferable to apply
online processing mechanism. For online processing, the system has
to be armed by automated procedures.
[0189] A process of online exercise creation is an entire automated
process based on parametrical model. An exercise represents main
learning cell or a chunk of data in the learning process.
[0190] The modeling process is the method that system uses to
determine the classification of exercises. This model (113.000)
includes eight parameters to describe all components and
functionality of the exercises. There are: [0191] Content type;
[0192] Subject; [0193] Category; [0194] Learning technology; [0195]
Educational mode; [0196] Exercise output type [0197] Generation
functionality mode; [0198] Implementation mode;
[0199] The parameters allow a building of a formal procedure for
exercises synthesis. Formal approach significantly simplified an
exercises creation.
[0200] By categories exercise templates are presented such as:
multiple choices, visual choices, filling forms, selection forms,
action and logical games, matching patterns, find rules, puzzles,
quizzes, chances and guessing, process simulation, analysis and
synthesis, explorer format, discovery environment, art studio, text
editor, explorer, etc. One of these is known as cloze training,
sometimes referred to as `fill in the blanks`.
[0201] By subject exercises are presented as: grammar exercises,
reading comprehension. writing and essay writing, listening and
pronunciation, learn words and vocabulary builder with any subject
matter as; (Clouds, Ancient Egypt, history, geography, biology,
etc.). One of these is types of exercises is Subjects and
Predicates.
[0202] Application of the preferred embodiment to the example of
2000-4000 textual input characters, by simple math, shows results
based upon the number of data combinations and the number of
different template data. With a doze training template the
calculation will show how many exercises are expected to be
available for generation with this template and with this exercise
subject and a 5 unit multiple choice format. The formula used is:
Max Number of
exercises=((sentN+Sub+predN)!/(((sentN+Sub+predN)-5)!*5!), where 5
is the number of multiple choices in each question. The maximum
number of exercises available in this
calculation=(50+20)!/(((50+20)-5)!X5!)=90345024. The N elements are
set out on FIG. 4, for examples, as N1={Select Subjects 113.003},
N2={Select Categories 113.005}, N3={Make a Decision about a
Learning Technologies 113.007}, N4={Define Education Mode 1113.009}
and N5={Generate Input Data for Exercises 113.001}.
[0203] In FIG. 4 element 113.002 (Parametrical model of exercise
functionality) is the data from FIG. 6 and includes the number of
parameters (referred to as P), particularly subject, category,
learning technology, teaching mode, output creations, functionality
implementation mode. Each of these parameters has a value. The
number of values for each parameter is Nm. This shows that the
number of possible combinations (C) is C=N1*N2*N3*N4 . . . *Nm.
[0204] Element 113.001 (Content Description Structure CDS) is the
output from the from the Content Processing Module shown in FIG. 2
and contains syntactic links (sentences and Links inside sentences,
among others), semantic links (semantic schemes subjects,
predicates, Internal and external links), grammar rules) tenses,
parts of speech, preposition phrases, among others) and the keyword
structure (keywords with coefficients and highlights).
[0205] Element 113.007 (Make a Decision about learning
Technologies) refers to FIG. 7 where block 113.007.006 uses block
113.007.005). This block is presented in FIG. 8 as a list of
learning technologies where each one includes a list of algorithms
and approaches. This list of learning technologies increases the
combinatorial nature of the preferred embodiment of invention for a
huge variety of variations.
[0206] Notionally, If one user operating the system in this single
form would spend 1 minute for each exercise, completing all
available exercises would take 300 years.
[0207] It can be seen that with this preferred embodiment the
number of actual exercises available to the user is essentially
endless, especially with larger input textual streams and the
variety of exercise functionalities.
[0208] By learning Technology exercises are presented using
Learning Formats such as: Syntality exercises that include
different memory techniques; Note Taking approach; Mind Webbing;
Multi-Sensory Learning, Speed Learning; Personal Action Access
Strategy (PASS); Multi Modal Learning Environment, etc.
[0209] By educational mode exercises are presented through;
information presentation mode, by explanation mode,
teaching/training mode and knowledge testing mode. Each mode
assumes to present the same information by different shape and way.
Explanation mode assumes to use a lot of additional information to
describe correct actions or data. In explanation mode score system
doesn't use all estimation observations. The Training mode uses
estimation for listening and full mode estimation calculation, but
with minimum explanation of information. The test mode does not
assume any support during an exercise performance. In the test mode
a user can only see the final achievement score.
[0210] By way of representation learning content exercises can be
presented by Real Time Learning content, Scheduled Real Time
Learning content and Structured Learning Content. Real Time
Learning content is any information from anywhere. Such kind of
content assumes a necessity to use content processing module to
extract data that can be capable to describe meaning of content,
classify features of the content and to get information to generate
exercises from this content.
[0211] Scheduled Real Time Learning content presented by generated
prepared data structures, which were created as combination of Real
Time Learning content and scheduling data.
[0212] Structured Learning Content assumes the existence of a suite
of services called by some a "Learning Management System" and by
others a "Learning Content Management System", and formerly called
a "Computer Managed Instruction" system.
[0213] By way of generation of exercises that is presented by an
approach which uses input data and functionality for an exercise.
First group includes exercises that don't need any input data and
the functionality is done as external functionality of template.
Second group needs to generate only input data. The functionality
already exists as template functionality. Third group needs to
generate both input data and functionality.
[0214] By student creation or exercise output it is possible to
have different kind of user creations as output or result of
learning exercises. It can be answering of questions, different art
collections, compositions, text, etc.
[0215] By an implementation approach, all exercises are presented
as collections of forms.
[0216] The forms can include items that are implemented as
components; as a text, a picture, HTML tag, Browser object, server
control, flash movie, media component, entire application, service,
etc. Defined compositions of items in forms are called
templates.
Learning Technology Selection Module (113.007)
[0217] Exercise generator module (113) includes Learning Technology
selection module (113.007), which provides information for building
of the parametrical model for exercise generation processes. It
provides selection and description of learning technology.
Description of learning technology is part of Exercise Description
Structure (EDS).
[0218] Reference is made to FIG. 7. The Learning Technology
selection module (113.007) contains following procedures and
collections: [0219] Create search criteria procedure (113.007.003);
[0220] Search the Learning Components procedure (113.007.006);
[0221] Components tune procedure (113.007.008); [0222] Components
collection (113.007.005)
[0223] URDS includes all data about user's request and chosen
learning content presented in this module by the Content descriptor
(CD) (113.007.001) and User descriptor (UD) (113.007.002). CD
(113.007.001) includes data, which it extracts from input text, as:
[0224] main text subject; [0225] main text framework or internal
scheme; [0226] text vocabulary level; [0227] keywords or main idea;
[0228] tenses; [0229] place, time, relative and viewpoint adverbs
scheme; [0230] etc.
[0231] UD (113.007.002) includes data, which is gotten from user
current achievements. It is: [0232] known and unknown words in
input text; [0233] user score by language subjects or by subjects;
[0234] user vocabulary score by words; [0235] history for user's
creations: [0236] etc.
[0237] A comparison of CO and UD produces an efficiency estimation
data, where current input text and user learning/training
requirement will be analyzed together in order to answer the
question: "how useful is the current text for current user skills
improvement?" or "what kind of user's skills can be improved by
exercises using current text?".
[0238] Select Learning Technologies Module (113.007) provides
interpretation of UD and CD. The main function here is to create a
request for a component collection to get learning technology
description correspondence to CD and UD.
[0239] After working of the procedure (113.007.005) a list of
components (113.007.006) represents the learning technology to be
found. Next step (113.007.008) will specify properties of
components to get accessories and decoration options for
components.
Collection of Components, Learning Technologies (113.007.005)
[0240] Reference is made to FIG. 8, which illustrates Collection of
Learning Technology Components (113.007.005). Collection of
Learning Technology Components (113.007.005) is included within
exercise generator module (113) and provides collections of
components that are used as learning technology representatives in
the RTL system 10. Collection of Learning Technology Components
(113.007.005) contains a collection of components. Each component
in this collection presents object-oriented model of a learning
technology. They would implement the following learning
technologies: [0241] Learning Forms or Pages, [0242] Syntality
exercises that includes different memory techniques, [0243] Note
Taking approach, [0244] Multi-Sensory Learning, [0245] Multi Modal
Learning Environment, [0246] Writing Technologies, [0247] Mind
Webbing, [0248] Speed Learning.
[0249] The Learning Forms (113.007.005.001) approach presents
learning formats, which is described by HTML pages format. It is an
analog of teacher assignment or a sheet of paper as traditional
learning chunk. This approach can implement such exercises as
reading, writing, spelling, listening, etc.
[0250] Reference is now made to FIG. 9. Syntality (113.007.005.003)
is a collection of memory techniques, which use different kinds of
associations for memorization of data. All techniques are based on
creating links between well known objects or elements, called
carrying components, and unknown objects, called carried
components. Carrying components and Carried components can be
anything, including real objects and components of virtual world or
part of dreaming. The components can have any nature: sound and
music, drawing and painting, solid and liquid, etc. A creation of a
correspondence (association link) between carrying and carried
components is called mnemonics creations. First author of modern
mnemonics was Giordano Bruno (1548-1600) an Italian philosopher, an
expert on the art of memory, he wrote books about mnemonic
technique.
[0251] All techniques have a difference which depends on the system
organization of the carrying components. To develop mnemonic
technique we need to define an alphabet (113.007.005.003.002). This
alphabet is presented as an ordered collection of carrying
components. Collections of alphabets (113.007.005.003.003) are
provided by the system. Some of them contain:
1. Real Word Components. This alphabet includes graphics that
represent images of real word objects. Such kind of alphabet for
mnemonics becomes valuable and useful when user needs to get
training: how to use the real word environment around him to
improve memory skills. By using this type of alphabet the user will
have two benefits. User will not only improve their memory skills
but they will be able to pay greater attention to their personal
environment that has meaning from ecology view point. Also user can
learn how to estimate their own position in this real word place
through personal associations. 2. Virtual Word Associations. This
alphabet includes graphics that are images to magic, unreal things
or any contrivance and make ups. Only user imagination can restrict
list of graphics that are in this alphabet. This alphabet put
together in one training process a personal user imagination to
create own personal world and personal user organization skills to
create control inside personal world. In this case, memory training
comes through an utilization of the user personality by a
combination of so important skills as organization and
communication skills. 3. Systematical symbols set. Such kind of
alphabet represents numerical or alphabet symbols. 4. Sound, Rhythm
and Music Associations. Such kind of alphabetical symbols can be
used for remembering of words when mnemonics techniques are used in
such categories exercises as games, karaoke, singing, etc.
[0252] Also each alphabet should include for mnemonics sets a
system of a conformity, a system of an accord, a system of
patterns, a combination of all these systems. The step
(113.007.005.003.004) presents a process that provides
correspondence between selected alphabet elements and environment
collection. Depending on the education purposes Syntality can get
different locations or positions for alphabet symbols in different
environments. The step (113.007.005.003.005) provides operations
that have to be implemented in presentation layer for this learning
technique. Set of operations, which can be used, are; create,
remove, update, select, get and set properties. The property of
alphabetical elements includes a position, a size, a color, a
shape, a style, a mode, etc.
[0253] One of the important properties of the alphabetical elements
is property that describes links between this element and other
alphabetical elements from one hand, and between this element and
elements which user can create and which are called carried
components.
[0254] Therefore, module (113.007, 005.003) is generating the
Syntality as mnemonics learning technique. Input data
(113.007.005.003.001), as request to create the Syntality, will get
from CD (113.007.001) and UD (113.007.002). The specification
(113.007.005.003.007) of the Syntality contains hierarchical
structure (the environment) filled by alphabetical elements and
description of internal and external links to these elements.
[0255] Note Taking approach (113.007.005.005) Note Taking approach
includes a collection of procedures, which provides information and
processing support for Note Taking activity.
[0256] The information includes: [0257] summary with keyword
highlights; [0258] graphical chunk of page with drawing keywords
position; [0259] correspondences between number of sentences and
keywords; [0260] pictures for keywords; [0261] Processing support
procedure includes: [0262] formatted Note Taking Page maker; [0263]
few print support procedures; [0264] customization of user Note
Taking format procedure; [0265] others;
[0266] Multi-Sensory Learning (113.007.005.009) is a set of all
possible ways of the actions using all communications systems
(senses) of the person. Through human's various sensations such as
visual, auditory, touch (tactile), movement (kinetic), and
reasoning capability, people can accomplish learning process in the
modals of hearing, speaking, reading, and writing.
[0267] Multi-Sensory Learning technology is based on a framework
with content management mechanism for learning content structure,
description, representation, retrieval, reuse, revise, retain,
exchange and sharing possibilities.
[0268] Multi Modal Learning Environment (113.007.005.011). These
procedures includes interface modules that provides data formatted
for different devices such as smart devices, wireless, web browser,
messengers, desktop computer, also for simple players and iPOD
devices.
[0269] Mind Webbing module (113.007.005.013). implemented as
structure and graph data editor. II allows user to create different
links between words and pictures. Each link can be named. For each
link a user can create notes or comments. As result, mind webbing
picture will be a graph with words and pictures as nodes and named
links will make connection between nodes.
[0270] Speed Learning module (113.007.005.015). contains four
independent parts: [0271] Skim information support module. It
provides very fast and light reading. User can apply skim
procedure, when user needs to find only purpose of information.
This module implements such functions as:--speed reading template
for scrolling text with different speed and text fonts and
colors:--template to visual keywords and summary. [0272] Scan
information support module. It provides possibilities for quick
organized reading to find interpretative meaning of information.
This module includes previous module actions and adds more
templates with main text characters and comprehension reading test
template. [0273] Study information support module. It provides most
active slowest reading to find deep analysis of information. Main
menu for this module includes following items: Reading--scrolling
text, switch eyes; Visual information: Visual keywords, Visual main
characters, Text to Visual; Content information: Who and what, What
is happened, Past-Present-Future, Details; Summary; [0274] Eye
movement and Speed Training. It provides templates for eye training
such as scrolling, running, flashing text, switching eyes and
flashing word templates. Also Schulte's tables templates includes
in this part to train eyes without any text. Speed reading skills
can be tested by special test templates.
[0275] Reference is now made to FIG. 5. Essay writing module
(118.001) provides an easy and effective way to quickly write
essays. Essay writing module (118.001) comprises three parts
according to a user's writing skills [0276] Basic essay writing
(118.001.001) [0277] Grammar pattern essay writing and style
improvement editor (118.001.002). [0278] User's visualization and
key words editor (118.001.003). [0279] Speech to text
[0280] Basic essay writing module (118.001.001) provides writers
from beginners to University level structured templates for mental
outputs. It is implemented as a Wizard that systematically notes
one (1) word at a time to simplify the initial writing output
process. Grammar pattern essay writing and style improvement editor
(118.001.002) is responsible to provide support for the user to
write advanced grammar structure sentences. More than 150 grammar
patterns help to create grammar correct sentence. The base approach
helps to create few sentences with different grammar patterns for
the same idea and than select that pattern which corresponds and
expresses the idea better. In addition, this module supports
functions, which help to improve text by selecting similar words
from the dictionary.
[0281] The visualization and key word editor (118.001.003) helps to
edit unstructured thoughts from the user's brain to the selected
templates quickly The user makes plans and order, to generate
sentences by thought fragments.
Basic Essay Writing Module (118.001.001)
[0282] Basic essay writing module (118.001.001) aids a user to
master essay writing skills from beginners to writer blocks and to
make the writing process as unstressed, easy and comfortable as
possible for the writer. By creating these templates it will allow
a user to write a 50 word essay in 15 minutes or a 100 word essay
in 30 minutes or a 500 word essay in 50 minutes, etc. and get a
much higher mark with much less stress.
[0283] Basic essay writing module (118.001.001) utilizes a
procedure that is implemented like a wizard dialog. All steps of
the wizard are equipped for speech to text and for text to speech
tools.
[0284] Writing is one of the major outputs from your Brain.
Speaking is another. Now that you have learned "something" it means
you have some knowledge. You now have to tell a teacher or a parent
or a Boss, etc. via a sentence, paragraph or story of what "You
know". This means that you will have to describe the "Vision" or
idea that you have in your Brain. How do you do this? There are
about 180 rules for proper grammar and essay writing in English. Do
you know all of them and how and when to use these rules. Most
people do not and so we have structured Writing Skills to be able
to take you level by level into constructing grammatically correct
sentences with correct spelling faster then before. All the grammar
rules and examples are at your "Clicktip".
[0285] In reading, you are creating a picture in your mind of what
the "author" wants you to see and understand. Do you have to read
every word to see and understand what the author was trying to tell
you? Research has shown that you only need to know about 10% of the
words in an article to give you a good understanding of what the
author was trying to tell you.
[0286] Now in writing you have a picture in your mind and now want
to tell a reader what you are seeing, thinking, feeling and doing
so that they can begin to see and understand the vision or picture
in your mind.
[0287] Therefore, if the teacher wants you to create a 100 word
essay then all you need is to "Visualize" (see the picture in your
mind) of the story you want to write about. Then in a relaxed
position, you look into your Brain for only -10 key ideas or words
from the vision or picture in your Brain. You type or speak these
words into the computer. Never mind what order or if the word is
good or not. Do not worry about spelling the software will help you
to make sure the word is spelt correctly. All you need to do is to
learn how to just quickly type or "speak" the key words from the
picture in your mind. Your writing grammar level will be tied into
your vocabulary level. The greater your vocabulary the more
creative your sentence structure can be made.
[0288] Reference is now made to FIG. 10, which in a flow chart that
illustrates the steps taken by Basic Essay Writing module
118.001.001. Define number of words in Essay and select number of
main words (118.001.001.001)
[0289] How many words do you want in your Essay? Look at the drop
down menu and "Click" on the amount of words you need to write in
your essay. The Essay Wizard will help you to create an essay with
the number of words that your teacher or you want.
[0290] Title: The first thing you have to do is to get a Title=T
for your story. What is the story going to be called?
[0291] Catch Focus time (118.001.001.002)
[0292] Turn on the accelerated music that will play in the
background LOOK inside your brain and (Visualize) THE OBJECTS,
IMAGES OR IDEAS that are PART OF your Story floating through your
Mind. Pick an object and type or "say" the word into the computer.
If you have a microphone and or speech recognition software "say"
the word. It will appear in the "keyword" section or main body of
your Essay.
[0293] Create Key Words (118.001.001.003)
[0294] If your Essay is 50 words long then you will have to come up
with "5" key words or objects from the story that you have in your
Mind. This is 10% of your story and will form the base or
foundation for your Essay.
[0295] Make an order to the Key Words (118.001.001.004)
[0296] Now look at your Words and place them in the "order that you
want to tell your Story". Since 10% of your 50 word essay is 5
words then "Drag and Drop" the words you have created into the Body
of your Essay; [0297] B1. [0298] B2. [0299] B3. [0300] B4. [0301]
B5.
[0302] Once you have organized the 5 words in the right order Click
on "Finish".
[0303] The computer will then "freeze" the order that you have
placed the words. This will become your "main body (B)" of your
Essay.
[0304] Create words for an introduction and for a summary
(118.001.001.005)
[0305] You will now have to provide two more words for the
Introduction (In). This is were you tell someone what your Story is
about;
In1.
In2.
[0306] Now place these 2 words in the order that you want to tell
the beginning of your Story. Now Click on "Finish"
[0307] You will now have to provide 2 more words for the "Ending
(En)" or "Summary". This is were you tell everyone the "Ending" or
"Conclusion";
En1.
En2.
[0308] Create the sentence for one Key word (118.001.001.006)
[0309] You have now taken a small part of your story out of your
imagination, out of your Brain and placed them into the Essay
Wizard. You have reduced the "Big Picture or Story" in your Brain
into small pieces. You will now have to take the small pieces and
recreate the picture or ideas that you had in your Mind.
[0310] Now Click on "Finish" to begin the process of building an
essay from these few "key words" from the images in your mind.
[0311] See how easy it was for you to come up with just 5 key words
from the pictures you created in your mind! All you have to do is
just practice relaxing and learn how to "see" or "visualize" the
images or ideas in your mind that you want to write about.
[0312] Now you will have to take the first word and create a
grammatically correct sentence around this word. The sentence
cannot be longer then 8 words. That right. Your sentence is
preferably shorter then nine words Why? So that you can concentrate
on creating JUST one (1) idea around each word. The shorter the
sentence the better.
[0313] Once you have created your basic essay, The Essay Wizard
will help you to add MORE Creativity around your words and
sentences. Do not worry about being creative right now. Just
construct a grammatically correct sentence. The Wizard will help
you with each sentence.
Check Spelling and Grammar for the Sentence (118.001.001.007)
[0314] Once you have completed your grammatically correct essay,
The Essay Wizard will work with you to create a more interesting
essay by showing you other words that you can use to replace the
initial words, etc. Let us begin to construct a grammatically
correct sentence.
TABLE-US-00001 B1 = "a word from your idea or picture in your
brain" Subject - Verb - Object Example =;;;;;;;;;;; /////////
""""""""" !!!!!!! etc
[0315] Once you and the Essay Wizard have constructed a
grammatically correct sentence, the computer will "flash" to let
you know. You can now click on "Finish". The sentence will then
drop down to the Basic Essay Box and the next word that you have
put in order of writing priority "B2" will appear from the box
beside the basic essay writing section.
TABLE-US-00002 B2 = "the 2nd word of your idea or vision in your
brain" Subject - Verb - Adjective Example = ;;;;;;;;;;; /////////
""""""""" !!!!!!! etc
[0316] As sentences for each word is created? (118.001.001.008)
[0317] You will have to repeat this process for the Introduction
and then the Ending.
[0318] The text improvements (118.001.001.009)
[0319] Once you have created grammatically corrected sentences with
the help of the Essay Wizard, your Basic Essay will be a "READ
ONLY". It is important that the basic essay stay the same so that
you can refer to it at any time. The Essay Wizard will
automatically create a Duplicate of your Basic Essay. You will now
be able to "Improve" the creativity of your Essay utilizing the
Wizard in the Duplicate to help you with selecting words and
pictures that are more descriptive, etc.
TABLE-US-00003 BASIC ESSAY (Is now Read ONLY) DUPLICATE ESSAY In1
In1 In2 In2 B1 B1 B2 B2 B3 B3 B4 B4 B5 B5 En1 En1 En2 En2
[0320] Reading and Overview of an Essay (118.001.001.010)
[0321] When your Essay is ready, an application will process it and
provide statistical information about your Essay as level of text,
list of grammar rules, key words, etc.
Life Management and User Block 18
[0322] Life Management and User Block 18 brings the components of
planning, estimation, motivation, goal management and time
management, performance tracking into the learning process. It
allows keeping the learning process as proved, customized,
multifunctional, cognitive optimal process. All learning process
properties define a learning efficiency and success.
[0323] As shown in FIG. 1, Life Management and User block 18
contains:
1. User Object (103);
2. Life Management Module (104);
3. Personal Picture & Personal (Vocabulary) Dictionary
(105);
4. Personal Action Success Strategy (PASS) (108);
5. User Life Management Database (109);
[0324] 6. Remote Learning reminder Module (111); 7. User's personal
database (119)
[0325] Reference is now made to FIG. 11. Life management module
(104) gets control from Learning Management Module (107)
[0326] It can happened in two cases--set or get data. In the case
of "Set" data the request works when a user has finished an
exercise or any process that was developed with some results that
needs to be saved.
[0327] This module can save the following data: [0328] A score
estimation data in User object (103); [0329] All user creation as
text (notes, essay, words, sentences, etc) and graphics (pictures,
clips, patterns, etc); [0330] Personal Vocabulary Builder
dictionary creations (105); [0331] Information for PASS systems as
Performed Task Result from Goal system, Estimation for Performance
tracking system, properties for Time Management system and
estimation of motivation and efficiency. [0332] In the case of
"Get" data, the module (107) requested data as URDS. It happened,
when user requested new content and the system has to generate
exercises to this content.
[0333] Module (104.005) starts to analyze input data which is
presented by content descriptor (107.001). The result of analysis
is transferred to PASS (108) as request to build URDS for current
content (CD). Module (104.006) will prepare information for PASS
that will present a scheduler to execute the current content
exercises. Module (104.007) will try to analyze goals and tasks in
Goal Management system that will be correlated to current content.
PASS object will get from the PASS system and add to URDS. The
module 104.009 will transfer URDS to Learning Management Module
(107).
User Object (103)
[0334] User Object (103) saves and operates with user description
data. User Object (103) comprises methods and data to process user
information.
[0335] User information presented by the following properties and
fields: [0336] membership info--user id, login, password; [0337]
personal info--age, bedtime, wake time, birthday date, address,
phones, company name, etc; [0338] Score and knowledge achieved
statistics--language level; integrated score by skills, subjects,
problem solving; motivation coefficient, efficiency estimation,
vocabulary integrated score, user exercise history reference;
[0339] An instance of this object is created when a user logged
into the system. Reference to this object is saved in URDS--User
Request Description Structure. The procedure 104.001 changes object
properties.
Life Management Module (104)
[0340] Life Management Module (104) provides data interface between
Learning Management Module (107) and User Life Management part of
system (103-106,111). The Life Management Module (104) creates a
URDS--User Request Description Structure that includes data about
user and current content. This module presents procedures that
implement "set" and "get" requests.
[0341] Learning Management Module requests URDS in order to analyze
content description and user information. The URDS includes all
information that will make control to real time exercise creation.
Also this data is used for structured content. Life Management
module presented by two branches that serve two kinds of requests:
GET and SET.
[0342] Main function of request GET is formation and filling of
URDS fields. This request will be served by following procedures.
Analyze of the content data from request (104.005) and define
personal setting to create scheduler for real time content in PASS
object (104.006). To get data about scheduler, life management
module makes request to PASS module (108). Next request to PASS
module (108) will be get data that describe goals and tasks, which
correspond to current content (104.007). Procedure (104.008) will
finish forming of URDS.
[0343] Main function of request SET is saving all data, which an
user developed and created. User's development presented by four
kinds of data: [0344] A score that user has got to exercise and
tests performance (104.001). The data will be saved to User Object
(103); [0345] User's creations such as essays, visual patterns,
graphics, note taking, and any others will be saved (104.002) in
personal database (119); [0346] Data for User personal vocabulary
dictionary is third kind of data that an user's creation can save
(104.003) in User Personal Dictionary (105); [0347] User life
management data or PASS responsibility data (104.004): 1. Performed
a Task Result from Goal system; 2. Estimation for Performance
tracking system;
3. Properties for Time Management,
[0348] 4. Estimations of motivation and efficiency. 5.
Personal Picture and Vocabulary Builder Dictionary (105)
[0349] Personal Picture and Vocabulary Dictionary (105), is a
container to save personal dictionary data for each user. Personal
Picture and Vocabulary Dictionary (105) may include a collection of
articles. Each article contains following data or references:
[0350] word as text; [0351] Reference to definition, synonyms and
other data from Common Picture and Vocabulary Dictionary (112);
[0352] Reference to a picture for word or a graphics (movie clip)
from Common Picture Dictionary (112); [0353] reference to an user
created picture; [0354] reference to an associations pictures;
[0355] four numbers that are scores for word training result by
pronunciation, listening, spelling, meaning; [0356] update
information date; [0357] create information date;
Personal Action Success Strategy (PASS) (108)
[0358] Personal Action Success Strategy (PASS) module (108)
implements a life management approach as background and based layer
of learning process. The main functions of PASS module (108) are:
[0359] support a goal system as life motivation technology; [0360]
create scheduler for an effective way to reach goals; [0361]
provide multimodal support to the learning process; [0362] provide
full score estimation process; [0363] create a recommendation for
user learning process organization by current estimations.
[0364] Reference is now made to FIG. 12. PASS module (108)
Contains:
[0365] Personal Action Success Strategy includes strategies
presents by subsystems: [0366] Time Management System (108.001)
[0367] Goal Setting System (108.002) [0368] Performance Tracking
(108.003) and tactics presented by Applied Functions (108.009).
[0369] PASS parametrical model or PASS object (108.007) is data
interface for implementation strategy methods to a tactical
approach. PASS is collection of systems that implements a
technology to life management. Learning process is part of the
user's life. PASS presents functions that guarantee effective
management and organization of the learning process.
[0370] An exercise in the system presented by functional
parametrical model (113) and PASS parametrical model (108). PASS
parametrical model (108) contains: [0371] Goal--each action in the
system has to be defined as part of a goal. It means all activities
will bring estimations to goal achievement and life will be defined
as a set of goals. [0372] Task--in order to reach a goal it is
preferable to do some actions that we will call tasks. A set of
tasks define a goal. Each task describes by task name, goal name,
start time, end time, duration, task type. The task can have
appointments for tasks performed. Task performance is a set of
appointments. An appointment is a moment of time when part of task
performance can start. Each appointment has data that describes
action as, for example, start application, fair event, send
message, etc. [0373] Goal and task estimations--this estimation
includes few numbers to describe goal and task success. Main
parameters from this are: motivation, efficiency and progress and
score.
[0374] The motivation is equal to a relation between the tasks that
user did to all tasks which were accessible to performance in that
moment. The efficiency shows how fast the score is changed. The
progress is value in percentage which shows how many percentages of
a task was done. The score is value in percentage which shows a
relation between correct and wrong answers.
[0375] Performance tracking (108.003) gets input data from Learning
Management Module (107). Also Performance Tracking can get control
by events from Scheduler Manager (108.001.003).
[0376] Reference is now made to FIG. 13. Typical life goals for
Online Learning process (108.009.001) is practical approach to make
connection between any user activity and typical life goals. A
connection between the goal and user activity allow increasing an
efficiency and motivation level of activity. Procedure
(108.009.001.001) gets learning content and user information as
URDS control block. PASS includes list of typical life goals that
usually are applied to most people for life. Procedure
(108.009.001.002) will find correspondence between goals and
knowledge that user will get after learning current content. If
such kind of goal exist (108.009.001.003), procedure
(108.009.001.004) will create scheduler to learn current content
and provide performance tracking for it. Performance tracking data
will record (108.009.001.005) to PASS object and as reference in
URDS will transfer to Remote Learning Reminder Module (111) that is
responsible to launch exercises and reminders.
[0377] Reference is now made to FIG. 14. Set times and duration of
a user's learned data are presented as short preset timed refresher
Pop-Up exercises module (108.009.002) from seconds to minutes.
First step (108.009.002.001) gets current learned content and
procedure (108.009.002.002) defines types of exercises that will be
launched by Pop-up reminders.
[0378] In the system, this procedure can be implemented as dialog
procedure, visual or sound or combinations. Next step
(108.009.002.003) specifies number of exercises and procedure
(108.009.002.004) that can generate these timed pop up reminder
exercises. Scheduler creator (108.009.002.005) is next step in this
module.
[0379] In the final procedure performance tracking will be recorded
(108.009.002.006) in PASS object for current content. Reference to
PASS object is in URDS. This data will be used for controlling and
launching timed. (15, 30 or 60-seconds, etc.) pop up exercises by
Remote Learning Reminder Module (111).
[0380] It will be appreciated that while the embodiments of the
real time learning and self improvement educational system 10 have
been described in the context of various methods including methods
for scaling multicolor and multilayer overlay images, it should be
understood that it is equally applicable to other types of images.
The system, processes and methods described are capable of being
distributed in a computer program product comprising a computer
readable medium that bears computer usable instructions for one or
more processors. The medium may be provided in various forms,
including one or more diskettes, compact disks, tapes, chips, wire
line transmissions, satellite transmissions, internet transmission
or downloading, magnetic and electronic storage media, digital and
analog signals, and the like. The computer useable instructions may
also be in various forms, including compiled and non-compiled
code.
[0381] It should be understood that various modifications can be
made to the embodiments described and illustrated herein, without
departing from the embodiments.
Real Time Learning Model (120) FIG. 15
[0382] The modeling of real time learning process includes Content
or Data (120.01), Strategies (120.02) as base ideas for
organization of effective learning process and Tactics (120.03) as
implementation of these ideas to the learning process.
[0383] Each learning process starts from preparing learning content
or data (120.01).
[0384] The structure and meaning of data (120.01) defines a choice
of strategy and tactics to organize the learning process. If the
data presents facts (120.01.03.01), learning strategies can be
present as remembering procedures: [0385] Associate: Relate ideas
to each other. [0386] Visualize: Try to see pictures in your mind
as you read. [0387] Concentrate: Have a specific purpose,
associating, and visualizing will help you to do this. [0388]
Repeat: Keep telling yourself important points and associate
details to these points.
[0389] If data (120.01.00.00) is presented as concepts, there are
additional approaches that corresponded to presented content. These
are concept mapping, analogies, different kind of classification,
etc. Therefore, for each kind of data the system has to provide
special strategies of learning. If the content that the student has
to learn is presented as textual information (120.01.01), the model
(120.00.00) includes Extractor procedure (120.01.02) to break down
the flow of textual content to Facts (120.01.03.01), Concepts
(120.01.03.02), Rules (120.01.03.03), Procedures (120.01.03.04),
Principles (120.01.03.05) and Problem-Solving (120.01.03.06).
[0390] The next part that the model includes is strategies
(120.02.00.00)--how to transform information to knowledge by the
most effective method: [0391] Real Time Learning Content strategy
(120.02.01) provides opportunity to generate in real time learning,
any content requested by the student, until the student is
satisfied or has enough content. [0392] Transformation of textual
information to interactive learning content (120.02.02) is
presented by text processing procedures that will extract data from
text and deliver this data to the interactive learning template.
[0393] Usually learning content has information density higher than
any other content. Next strategy (120.02.03) presents
transformation through reduction of data This strategy is describes
as heuristic procedure that can measure information in each
sentence as new (unknown) information or known information. So each
sentence will have a number, called information density, which
estimates how much new information is in this sentence. All
sentences will be sorted by this number to make text
reductions.
[0394] From the understanding of an information point of view, the
information, transferred by the text, assumes presence at the
reader of any primary knowledge, addressing to which, the author of
text designs semantic meaning of text. Accordingly, by way of a
maintenance to distinguish a theme (that is in advance known to
participants of the communications) and rheme (new information that
the author of the text adds in a theme). The author of the text
provides mention of a theme in the heading, and the text devotes
itself to various rhemes. In some text the focus is on an
unenlightened audience whereby the theme provides some detail. Only
by a Reading or a learning approach; with a focus on creating parts
of text, with enough information on the theme, will the reader be
able to provide the knowledge that is missing from the theme to
understanding the text. [0395] Structured Text Extractions:
(keywords, summary, main characters, etc.) (120.02.04). This
strategy has two aspects: 1.--adaptation to reader understanding
process and 2.--attention catcher. Both aspects are implemented by
highlighting different words in a text. By these extractions the
reader will build an image of the maintenance of a separate
fragment of the text, keeping an image of the text as the whole.
This process is arranged similarly to understanding of the text by
the person. In other words, repeated understanding of the several
first fragments of the text is built and then the assumption of the
possible complete maintenance is done. After that the maintenance
is completed by the analysis of each followed fragment and the
completed maintenance influences the already constructed images of
the first fragments. [0396] Text Complexity estimation (120.02.05)
presents strategy based on a calculations of three complexity
coefficients: dictionary level, grammar and style complexity,
theme-theme structural coefficient.
[0397] Third part of the model presents Tactics (120.03.00) as set
of scenarios where described strategies were implemented. It
includes: [0398] Interactive Reading scenario (120.03.01), [0399]
Multi-sensory and multi-module templates (120.03.02), [0400] Skim,
Scan, Study scenarios (120.03.03), [0401] Game Templates and
Edutainment (120.03.04).
Real Time Learning Content Strategy (120.02.01) FIG. 16
[0402] Real Time Learning Content Strategy (120.02.01) contains two
parts: Teacher prepared learning content strategy (120.02.01.01)
and Real Time Content Strategy (120.02.01.02).
[0403] First approach describes a procedure how a teacher prepares
learning content. A source of this approach is information
(120.02.01.01.01) in different formats as text, picture, sound,
other Medias, which is received from different places as books.
internet pages, movies, other lessons, etc.
[0404] Process of learning content creation (120.02.01.01.02) is
analysis and synthesis of information, where a teacher should
write, read, select, collect, classify, systematic, modify, etc.
This process should be finished before a student will start
learning. The information selected to be learning content, will be
formatted (120.02.01.01.03) in learning templates according to
requirements or trademarks. As a result of this process teacher
will have lessons and exercises that are equipped by timing and
scoring parameters, Prepared lessons can be used (120.02.01.01.04)
by students to learn. After applying teacher's lessons student
should get knowledge (120.02.01.01.05).
[0405] In this strategy the process of content creation and process
of using this learning content are separated by time. Any student
will have the same lessons that are not depended on their personal
priorities, features and skills.
[0406] The second strategy (120.02.01.02) presents an approach on
how to provide real time learning content before and during student
learning content
[0407] A source (120.02.01.02.01) for real time learning content
can be any electronic formatted document as digitized book (may be
PDF format), internet pages, pictures, movie clips and databases
with lessons and exercises.
[0408] Next step (120.02.01.02.02) is when a teacher prepares
selected content as an exercise for a student's consumption which
in now provided by (120.02.01.01.02) with a click of a button
thereby automatically generating exercises by the system (113)
within 10 seconds for a students consumption without having to
spend minutes or hours of the teacher's time to create these
student exercises.
[0409] When Learning Content (120.02.01.0.203) will be ready,
process of learning (120.02.01.02.04) can be started. Next step
(120.02.01.02.05) presents estimations that will be made allowed
organizing loop and go to prepare more new learning content in real
time. The estimations could be done by exercise scoring or testing
mechanism or by student desire. If request to generate more
exercises was made, student or teacher has possibilities to select
(120.02.01.02.06) that learning format or templates which
corresponded better to this particularly learning content or
learning goal. Block (120.02.01.02.07) will generate more learning
formats and template extractions based on information from block
(120.02.01.02.08) in real time. These extractions create numerous
opportunities for student to have many lessons and exercises that
they may want or need to learn to get better scores. Block
(120.02.01.02.05) presents student knowledge that will provide a
looping process of generating as many exercises as student really
needs to gain the confidence that they know or to obtain an
acceptable mark.
Transformation of Information into Interactive Content (120.02.02)
FIG. 17
[0410] This strategy deals with situations when students have to
read and learn content, data or textual information (120.02.02.01).
It a student has to read a plain text the efficiency of this
process could be estimated by efficiencies of reading techniques
(normal reading, speed reading) that would be applied.
[0411] If we will add different behavior for communications with
plain text, we will get interactive learning content.
Therefore,
Interactive learning content=Text extractions+behavior.
[0412] The main goal to this transformation is to build attention
or concentration catchers that will bring more efficiency to the
learning process.
[0413] To transform plain text to interactive content will begin
from text processing procedures (120.02.02.02). The more
extractions that the system can do from the text the more
interaction templates can be created. The extractions can be
presented as keywords (120.02.02.03), summary (120.02.02.04),
grammar constructions (120.02.02.06) and others (120.02.02.07).
Next step (120.02.02.08) includes a selection of interactive
templates. These templates can be as: [0414] A plain text with
highlights of different words. These words can be keywords,
different parts of speech, subjects and predicates, unknown words
for student, etc. This format will provide concentration on the
words which are the most important in the current context.
[0415] A test with multiple choice answers. Here each question is
presented as one sentence from the text with missed keywords. This
format will engage student's attention to provide more
concentration for each sentence.
[0416] Mind webbing format. It allowed for eliminating differences
between two processes: how to understand something and how to write
about this.
[0417] Games format. It again can be attention or concentration
catcher.
[0418] Next block (120.02.02.09) includes procedures that can
transform plain and text extractions to selected learning format so
learning interactive content (120.02.02.10) will be made much
easier and faster.
[0419] Variations of the above described learning system modules
and blocks, structures and components will be apparent to those
skilled in the art and such variations are considered to be within
the scope of the present invention. Thus modifications and
alterations can be used in the learning system of the present
invention without departing from the scope of the invention.
APPENDIX 1
System Aspects
[0420] 1. Real time content. The embodiments are directed to
methods and apparatus of a system that provides opportunities to
learn any subject or language, which is presented by Real time
digitized content to be learned. Learning efficiency in this case
will be raised substantially since the user can increase their
motivation scores. It is mostly important for learning of a textual
subject and especially a language as second language, where a user
needs a new language to improve their education or to acquire a new
specialty. This new language can be in a subject field such as
medicine, engineering, hospitality, hair design, etc. 2. Online
generated exercises. Another aspect of the embodiments is presented
by real time generated exercises to provide opportunity to learn
language or a subject by not only the content presented by the
teacher for a student to learn, but by user's favorite topic of
text, news paper articles, fictions, user guide for modern
technical device, traffics rules or instructions "How to use . . .
". It means a user will learn language grammar, new words, idioms,
rhymes, synonyms, etc. and a user will learn new subject by reading
comprehension, writing, note talking, mind webbing, games, etc.
Such kind of apparatus allows improvements not only in knowledge
but in personal skills too. 3. Advanced Learning Technologies.
Another aspect of the embodiments is presented by generated
exercises that contain different media environments for
implementation, including competition formats, visual material and
graphics, game formats, karaoke formats, etc.
[0421] "Thus, it can be argued that play in humans is a tool
leading to perceptual, conceptual, intellectual and language
development, the basic building blocks required for the formulation
of higher cognitive functions."
[0422] Any learning process cannot exist separately. It is part of
life. Humans spend a lot of time in their life to learn some thing
new. An efficiency of this process defines important things as life
success, health, etc.
4. Personal Action Success Strategy. Another aspect of the
embodiments is presented by a Life Management system and PASS
(personal action success strategy) that provides functions of the
learning process as planning the personal scheduler,
personalization of real time learning content, remote learning by
multimodal presentation layer, including wireless devices and
MP3/iPODs.
APPENDIX 2
Implementation of the Model that Represent Exercise
Functionality
1. Learning Content
[0423] 1. Real time Learning Content 2. Scheduled Real time
Learning Content
3. Structures Learning Content
2. Subjects
1. Language Learning Subjects
Grammar,
[0424] Reading, reading comprehension and writing Listening and
pronunciation
Vocabulary
[0425] Language skills
2. Natural Subjects
[0426] Geography, biology, physics, . . .
3. Industrial Subjects
[0427] Industrial specialties knowledge
4. Others
3. Category
[0428] 1. multiple choices template, 2. binary choices, 3. visual
choices, 4. filling forms, 5. selection forms, 6. action and
logical games, 7. matching patterns, 8. finding rules, 9. puzzles,
10. quizzes, 11. chances and guessing, 12. process simulation, 13.
analysis and synthesis, 14. explorer format, 15. discovery
environment, 16. art studio, 17. text editor, 18. explorer,
etc.
4. Learning Technologies
1. Learning Forms,
[0429] 2. Syntality exercises that includes many different memory
techniques, 3. Note Taking approach,
4. Mind Webbing.
5. Multi-Sensory Learning,
6. Speed Learning,
7. Multi Modal Learning Environment
5. Education Mode
[0430] 1. Information presentation mode, 2. Explanation mode, 3.
Teaching/Training mode, 4. Knowledge testing mode
6 Output Creations
[0431] 1. Answers to questions, 2. Art, graphics, pictures, 3.
Collections, compositions, associations, 4. Text, essay, notes, 5.
Reports, marks,
7. Implementation
[0432] 1. Server components; 2. HTML tag, 3. Browser object, 4.
User control 5. Flash movie, 6. Media component, 7. Entire
application,
8. Message,
APPENDIX 3
Abbreviations
CDS--Content Description Structure,
URDS--User Request Description Structure,
[0433] EDS--Exercise description structure, SCORM--Sharable Content
Object Reference Model, standard for Content Management System.
CD--Content descriptor, UD--User descriptor;
APPENDIX 4
Glossary
[0434] 1. Syntality--is a collection of memory techniques, which
uses different kind of associations for memorization of data. It is
defined as, "Accelerated learning by actively doing!" Research has
shown that when a person. "Reads, Hears, Sees, Says and Does
(Interact) then they will be able to remember up to 90% of the
information presented." Consequently, Syntality attempts to engage
all of the Users six (6) senses through mnemonics, associations,
memory pegs, music, sounds, visuals, summarizations, etc. to help
them to turn information (data) presented into knowledge that they
need to learn. 2. Sharable Content Object Reference Model
(SCORM.TM.) is a collection of standards and specifications for
web-based e-learning. It defines how client side content and a host
system called the Run-time Environment may communicate with each
other, as well as how content may be packaged into a transferable
ZIP file. SCORM 2004 introduces a complex idea called
sequencing--rules that specify the order a learner may experience
content objects in. The standard uses XML and it is based on the
results of work done by AICC, IMS, IEEE, and Ariadne. 3. Real Time
Self-Learning (RTSL) Content. "An application in which data is
received and immediately processed for knowledge consumption". It
is any information which can be requested to transform to
educational learning content online. This information can be
presented by text files, HTML pages, etc. 4. Personal Action
Success Strategy (PASS). It is a measured approach to provide
effective ways to get desirable skills to achieve personal goals.
It includes procedures to organize and use such information as:
daily/weekly activity goals; Goal planning sheet; your plans of
action; master dream list; accomplishments sheet; aids to
visualization; long-range goals sheet; etc. 5. Scheduled User
Content. It is that learning content which user used to create
scheduler of lessons. It means, if user create lessons scheduler to
learn some content then this content will be called scheduled. 6.
Structured Content. It is any content that will be transformed into
exercises, lessons or courses that has been pre-organized. 7. Time
management system. It is a way of tracking appointments and things
to do that have been organized into minutes per daily activity and
hours per year goals, against a stated life span, that monitors
interactive information to knowledge outputs. 8. Goal setting
system. It is system that operates with the content that the user
has obtained to interact with is called "goals". Operations are
create, add, update, remove, add Property, estimate achievement or
progress, etc. 9. Motivation system. It is system that provides
directions for making decisions and self development. This system
use as input data from goal system. 10. Score estimation system. It
is a system that can measure, registry and keep or save numbers
that describe result of process, which is called "score". 11.
Performance Tracking System. It is a system to launch computer
processes according to a scheduler. The systems responsibilities
are to start and to end processes in define time and register all
events when they appear between the beginning and ending of the
process. 12. Multimodal learning environment. It is an environment
that includes many different devices that will be used in the
learning process. Such devices are computer, messaging systems,
wireless phones, karaoke machines, smart small devices, players,
etc. 13. An exabyte is a billion gigabytes, or 10.sup.18 bytes.
* * * * *