WO2014127241A1

WO2014127241A1 - System and method for personalized learning

Info

Publication number: WO2014127241A1
Application number: PCT/US2014/016489
Authority: WO
Inventors: Philip ICE
Original assignee: American Public University Systems, Inc.
Priority date: 2013-02-14
Filing date: 2014-02-14
Publication date: 2014-08-21

Abstract

Embodiments provide a personalized learning management system. A server may perform operations including analyzing information associated with a student to determine a learning modality for the student, analyzing course information for a course offering selected for study by the student, determining a study material type based on the learning modality and providing study materials for the course offering to the student according to the determined study material type based on the learning modality for the student.

Description

TITLE

System and Method for Personalized Learning RELATED APPLICATION

[0001] This application claims the benefit of priority to U.S. Provisional Patent Application No. 61/764,816 entitled "System and Method for Personalized Learning" filed February 14, 2013, the entire contents of which are hereby incorporated by reference.

BACKGROUND

[0002] Record numbers of students are seeking post-secondary credentialing and degrees, however, attrition levels are also at record highs, with some measures indicating that upwards of 57% of students fail to obtain their desired academic goals. Nowhere is this more true than in the online environment, where enrollment rates are 10 times higher than face-to-face programs, but also have attrition rates that are up to 17 times greater than in traditional courses.

[0003] The vast majority of U.S. postsecondary institutions that serve low- income young adults today (i.e. community colleges and public 4-year universities) do so under pressure to produce better learning outcomes and student success outcomes, manage a faculty that increasingly consists of non-tenured and part-time adjuncts, and meet growing demand for their services in the face of flat or declining budgets. At the same time, due to the sluggish recovery and widespread economic malaise, colleges and universities are being asked to produce better outcomes with dramatically fewer public dollars.

[0004] Simultaneously students are increasingly demanding rich, adaptive, mobile- friendly, virtual learning environments that provide access to dynamic, simulation-rich content, that mirror their web experiences in other facets of their life. Faculty members responsible for facilitating learning in these environments also expect to have a greater role in customizing digital learning experiences and courses. They will also expect learning analytics dashboards that provide real time feedback on their students' progress on measurable learning outcomes in courses they facilitate.

Increasingly, faculty and students will depend on a robust academic

"cyberinfrastructure" to provide these core services.

[0005] Students and educators thus share a common objective of achieving a successful learning outcome in an environment in which the resources available for learning are declining. For the common objective to be achieved, use of the available resources must be optimized. One approach to optimize the use of available education resources is by employing a personal learning approach for each student.

[0006] According to the National Educational Technology Plan developed by the U.S. Department of Education, personalized learning is defined as adjusting the pace (individualization), adjusting the approach (differentiation), and connecting to the learners interests and experiences. Personalization is thus broader than

individualization or differentiation in that it affords the learner a degree of choice about what is learned, when it is learned and how it is learned. While personalization also takes into account the pace at which the learner is progressing, it further attempts to account for the potential of the learner, his or her aptitudes and the way that a particular individual learns. Under the personalization approach, learning objectives are different for each learner. By allocating resources on a personalized basis, each students may achieve the course objectives but by a different path.

[0007] The optimal personalized learning approach for a specific individual cannot be determined at the start of a learning process because the optimal approach is revealed by the process itself. The challenge is to observe the learning process of each member of a large student body and to discern how that process may be optimized on an individual student basis. SUMMARY

[0008] The systems, methods and devices in the various embodiments disclosed herein facilitate granular provisioning of content within designated areas of a page view according to a selected content, material, or asset level within a page or view. An embodiment method of providing personalized learning may therefore include analyzing, in a server, information from a student learning profile associated with a student to determine a learning modality for the student; storing the student learning profile in a database maintained in a data storage element; analyzing, in the server, course information stored in the database for a course offering selected for study by the student; determining, in the server, from the analyzed course information and the determined learning modality for the student, a study material type among a plurality of available study material types associated with a plurality of learning modalities; selecting study materials for the course offering based on the determined study material type; selecting one or more asset levels associated with the selected study materials; and providing the selected study materials for the course offering to the student according to the selected one or more asset levels.

[0009] A further embodiment method may include providing study materials for the course offering to the student according to a determined one of the plurality of study material types based on the determined learning modality for the student by providing, by the server, the selected study materials to one or more designated areas within a view accessible to the student. In a further embodiment method, the one or more designated areas may correspond to the one or more asset levels associated with the selected study materials. In a further embodiment method, the content of one or more designated areas may be configured to be changed through provisioning according changes in the selected study materials. In a further embodiment method, the one or more designated areas may include one or more dynamic content provisioning blocks that allow for granular provisioning of content at a selected content, material, or asset level within a page or view. [0010] A further embodiment method may include initializing the student learning profile by receiving, in the server, and storing in the student learning profile, one or more of intake data and demographic data for the student. A further embodiment method may include receiving, in the server, and storing in the database statistical data associated with the student. In a further embodiment method, the statistical data may include one or more of data indicative of performance of the student in the course offering based on the study materials provided according to the determined one of the plurality of study material types, data indicative of the amount of time spent by the student with the provided study materials, and data indicative of the amount of time spent by the student accessing the provided study materials with an access device type.

[0011] In a further embodiment method, analyzing information from a student learning profile associated with a student to determine a learning modality for the student may include offering, by the server, study materials for the course offering of a first study material type and a second study material type according to a respective first learning modality and a second learning modality; tracking, in the server, an amount of time spent by the student with the offered study materials of the first study material type; tracking, in the server, an amount of time spent by the student with the offered study material of the second study material type; tracking, in the server, a performance result of the student in the course offering; comparing, in the server, the tracked amount of time of the first study material type, the tracked amount of time of the second study material type, and the performance result to determine the learning modality type for the student; and updating, in the server, the learning modality maintained in the student learning profile for the student based on the comparison. In a further embodiment method, analyzing information from a student learning profile associated with a student to determine a learning modality for the student may include tracking, in the server, an amount of time spent by the student with the provided study materials of the determined study material type for the course offering; tracking, in the server, a performance result for the student for the course offering based on the amount of time spent by the student with the provided study materials of the determined study material type; comparing, in the server, the tracked amount of time and the performance result with stored amounts of time and performance results for one or more other students based on study materials of the determined study material type; and updating, in the server, the learning modality maintained in the student learning profile for the student based on the comparison.

[0012] Further embodiments may be provided including an embodiment system having a server processor configured with processor executable instructions to perform operations including the above described embodiment methods.

BRIEF DESCRIPTION OF THE DRAWINGS

[0013] The accompanying drawings, which are incorporated herein and constitute part of this specification, illustrate exemplary embodiments of the invention, and together with the general description given above and the detailed description given below, serve to explain the features of the invention.

[0014] FIG. 1 A is a system block diagram illustrating a various system components suitable for use in various embodiments.

[0015] FIG. IB is a system block diagram further illustrating various system

components suitable for use in various embodiments.

[0016] FIG. 2A is a functional block diagram illustrating functional components and interrelationships of a learning management system in some embodiments.

[0017] FIG. 2B is a functional block diagram further illustrating functional

components and interrelationships of a learning management system in some embodiments.

[0018] FIG. 2C is a functional block diagram illustrating functional components for recommending course content in some embodiments. [0019] FIG. 3A is a process flow diagram illustrating an embodiment method for analyzing the relative performance of students based on study material modalities.

[00201 FIG- 3B is a process flow diagram illustrating a further embodiment method for analyzing the relative performance of students based on study material modalities.

[0021] FIG. 3C is a process flow diagram illustrating a further embodiment method for provisioning of content at selected asset level within student page view.

[0022] FIG. 4 is a component block diagram illustrating a terminal device suitable for use in various embodiments.

[0023] FIG. 5 is a component block diagram illustrating a server suitable for use in various embodiments.

DETAILED DESCRIPTION

[0024] The various embodiments will be described in detail with reference to the accompanying drawings. Wherever possible, the same reference numbers will be used throughout the drawings to refer to the same or like parts. References made to particular examples and implementations are for illustrative purposes, and are not intended to limit the scope of the invention or the claims.

[0025] The terms "terminal device," "computing device," "receiver device," and "wireless device" are used interchangeably herein to refer to any one or all of a variety of personal computing devices, including but not limited to personal computers, laptop computers, personal mobile television receivers (e.g., multicast, broadcast, unicast related devices), cellular telephones, automobile mobile television receivers, personal data assistants (PDA's), palm-top computers, wireless electronic mail receivers, multimedia Internet enabled cellular telephones, and similar personal electronic devices which include a programmable processor and memory and telecommunications receiver circuitry for receiving and processing broadcast transmissions. [0026] The words "server", "LMS server" and "web server" are used interchangeably herein to refer to hardware, an application or group of applications, and a combination or combinations of hardware and application software capable of receiving messages and requests in connection with a learning management system as described herein. Messages and requests may be Hypertext Transfer Protocol (HTTP) messages or requests or other standard or proprietary messages or requests. An appropriate response may be an HTTP response, or other response, such as providing a Hypertext Markup Language (HTML) file or other file or information. A server may include middleware or an application portion, such as a J2EE® server, an ASP® server, a PHP module, a PERL interpreter, or similar functionality. A web server may also include a data storage portion, such as a database management system (DBMS) or local file store. A web server may be implemented within a conventional server, but in the various embodiments a web server is also implemented within a terminal device.

[0027] As used herein, "personalized learning" encompasses the administration and provisioning of educational materials that are keyed to the learning style of a particular individual in a particular kind of educational environment such as lecture, laboratory, interaction with other students and teachers, and other activities incident to educational activities.

[0028] As used herein, a "learning management system" (LMS) encompasses software applications for the administration, documentation, tracking, reporting and delivery of education courses or training programs.

[0029] As used herein, a "student information system" (SIS) encompasses a software application for education establishments to manage student data.

[0030] Embodiments are directed to a learning management system (LMS) that may include a personalized learning monitor. The personalized learning monitor may monitor various measures of the progress of each student of a student body to identify a personalized learning pathway for each student. By providing a personalized learning pathway, the embodiments increase the likelihood that the student will remain engaged, maintain academic momentum and complete the credentialing process.

[0031] In an embodiment, a personalized learning monitor may be a hardware device, a software module, a system, or some combination of the foregoing that utilizes software applications to dynamically provide a student with optimal content and experiences in real-time in order to increase the likelihood of success for the student while also helping institutions fulfill their mission of serving all students in an equitable fashion. Students may receive not only personalized content but real-time feedback on progress and achievement that allows for rapid remediation.

[0032] In another embodiment, the personalized learning monitor may produce effectiveness measures and institutional initiatives to ensure student retention, progression, and completion goals are being met. Faculty members may be

empowered with a holistic and detailed view of learners at a program level, a course level and content level with intervention capabilities and alerts. Further, instructional designers may monitor and evaluate efficacy of various course design strategies.

[0033] Various embodiments illustrated herein may utilize multivariate statistical analysis in order to analyze various information variables associated with the students, their actions during the course, their outcomes for sections of the course and for the entire course, in order to maximize the chances for student success.

[0034] Many factors are associated with the success that an individual student achieves in studying and successfully learning any particular subject matter. Students may learn best according to their unique learning modality, which may be one or a combination of preferred ways for the student to assimilate subject matter. For example some students may learn more effectively through visual examples of subject matter. Other students may learn more effectively by reading text that is organized to convey the subject matter. Other students may learn more effectively by listening to presentations that convey the subject matter. Some students may learn more effectively through a combination of approaches. The application of multivariate statistical analysis including, without limitation, principal axes factor analysis, principal component analysis, regression analysis, transformation analysis, eigenvalue decomposition, and other analysis methods, allows the personalized learning monitor to track a student and record not only what subject matter is reviewed by any particular student but also how long a student spends with e-learning materials including the type of learning materials being reviewed.

[0035] A portion of the multivariate statistical analysis may be concentrated on student statistics. For example, the normal types of statistics may be obtained from the student including age, sex, prior educational background, etc. In addition however the personalized learning monitor may keep track of the various factors associated with the interaction of the student with the institution, its courses, and educational materials that are presented to students. The factors associated with institutional interaction may fall into several different categories, for example and without limitation, factors of interaction may include the major declared by the student, the courses within that major selected by the student, the type of interaction required within each of the courses associated with that major and those selected by the student if a choice is available, the success of the students in the various courses and required interactions, and other information regarding how specific students go about their learning experience within each course of their associated major.

[0036] In general, most major areas of study require certain prerequisites in order to be admitted or in order to be successful in the area of study. For example, areas of study that are more scientific, engineering, or technical in nature may require more math prerequisites. Areas of study that are more sociological, cultural, literary in nature or require other social sensitivities may require more history and so on. In some embodiments, an initial analysis operation may be performed to review the Student Learning Profile. During, for example, initial registration, the Student Learning Profile may contain only basic information about the student but may contain sufficient information to guide the initial analysis operation. For example, the Student Learning Profile may contain information about previous coursework at a different institution, or may include secondary school, undergraduate, or other institutional transcript information. The operation may make determinations of the kinds of courses that are required for the student based on the declared major. The operation may determine a level of knowledge that may be lacking or deficient based upon the transcript information of the student, and the success of students with similar background information who have already completed that particular major.

Comparing the profile of those who have been successful in completing the major area of study to those who are entering the area of study may result in the personalized learning monitor recommending certain remedial educational action on the part of the newly entering students.

[0037] The present disclosure describes functional requirements that are carried out once a learning management system is operational. Initial training and testing of the learning management system will require the ingestion of a relatively large data set. In some embodiments, system initialization may be accomplished through extracting data from a small sample of student interactions, and then using the distribution obtained from this set to extrapolate a larger representative data set. The larger representative data set may be used to determine benchmark indicators for delivering a set of content that is most highly correlated with success in view of the sample metrics. Once the initial models are developed, they may be tested for accuracy with live student data and refined, iteratively, as necessary as the scope of operations of the learning management system increases.

[0038] All major areas of study require a student to take multiple courses over a period of time to complete, for example, a degree program or certificate program. Each of the courses for an area of study requires a level of effort on the part of the students in order to be successful. By evaluating statistics associated with past successful students, the type of materials they looked at, how long they spent with different types of materials, and other statistical factors noted above, recommendations may be made to the students who are taking each individual course within that major area of study. While such a preliminary analysis may help prospective students, it is based upon statistical analysis of the students who came before them. The analysis may not be useful for analyzing a Student Learning Profile for a new or prospective student.

However, after a sufficient amount of data is collected on a sufficient number of students, individualized recommendations associated with the major area of study can be made. The recommendations may be based upon aggregate statistics associated with the success of students who had similar initial profiles to a particular prospective student. For example, the system will be able to analyze what successful students, who were visual learners, did in order to achieve successful grades in a particular course or area of study.

[0039] If a course of study and courses within that course of study have been established for some time, it is likely that the personalized learning monitor will have statistics on the students who have already taken those courses. The statistics will reveal information regarding students that succeeded, and students that failed in their academic endeavors. Over a period of time, the personalized learning monitor may keep track of the types of materials that were reviewed by both successful and unsuccessful students to determine the degree to which the style of learning of individual students (e.g., learning modalities such as visual, textual or other) was a major determining factor in the success of the student. Other factors may also be considered such as prior educational background, age, experience, or any number of other factors in determining the likelihood of success. The importance of these factors relative to one another may be accomplished through multivariate statistical analysis.

[0040] In some embodiments, another area of statistical analysis that may relate to the type of interaction required to be successful in a major area of study. For example, and without limitation, certain courses may require that analytical reports be prepared that do not necessarily rely upon numeric data. Other courses may require mathematical analysis of data. Depending upon the type of course and the type of data, statistics may be developed that indicate that the "average" successful student spends a certain amount of time reviewing the course materials. It is generally self-evident that students who spend more time reviewing the course material have higher grades than those who spend less amount of time reviewing course materials. However, in some embodiments, detailed statistics may be developed for students that may show, with some degree of precision, how much time a particular student with a particular profile may expect to spend with the course materials in order to achieve a specific grade for a course.

[0041] In some embodiments, as a student progresses in a particular course or section of a course, the student behavior with respect to materials may be continuously reviewed and analyzed. While different students may learn at different rates, suggestions may be presented to students during the progress of a course concerning what materials should be concentrated on to a greater extent in order to maximize the chances for success.

[0042] Different students may have different learning styles and may go about their learning within the courses of their major in a different way leading to a different learning experience. Thus, in some embodiments, the courses for a particular major or area of study may be assembled by a particular professor to include a combination of different types of materials. The instructor may assemble material that caters to students with different learning styles or modalities, but which contains the same information for individual sections of the course and the entire course. Since successful course completion generally relies on standards (e.g., standardized tests), all the materials that are developed by a professor should be able to allow students with different learning styles to be able to master the course material to the level required by the standard.

[0043] Students engaged in particular courses may be monitored over a period of time. The Student Learning Profile for each student may therefore be improved with additional individual information collected over time. The Student Learning Profile may include an analysis of the student behavior and performance on a variety of courses. The analysis developed from the Student Learning Profile of a given student may be analyzed to assist the student in identifying the predominant learning style or modality for that particular student. In such a case, the personalized learning monitor may present suggestions to a particular student to review certain types of material that are, perhaps, more visual in nature if the Student Learning Profile reveals that visual modality is the predominant learning style of the student. Conversely, if the student predominant learning modality is textual, the student may be presented with course materials that are more specifically directed to the textual learning style. Over time, the amount of time spent by a particular student with materials of a particular modality type may be monitored and the Student Learning Profile may be updated. In view of the updated information developed in the Student Learning Profile over time, revised suggestions may be made to the student to spend more time on one unit of instruction than another and to use one particular type of educational material (e.g., visual, textual, or other material type) over another in order to maximize success.

[0044] In the various embodiments, a pathway by which a student accesses certain educational materials may be analyzed to determine how the student accesses the educational materials for a particular course or course of study. For example, while visual and textual materials may be presented that cater to particular learning styles of a student, the actual vehicle by which the student reviews his or her materials also becomes important. For example, a married student with family may access a majority of the materials from home on a laptop or desktop computer. A student in a different situation may access materials on a smart phone or tablet computer. Other examples are possible.

[0045] Accordingly, part of the statistical analysis of the personalized learning monitor involves determining what physical devices are used by a particular student to access educational materials. Information is obtained concerning how often individual students access educational materials using desktop computers, laptop computers, tablets, smart phones, and other devices. The information regarding physical devices used for access may be reviewed over time to determine if trends develop for an individual or across a student body. The information may further include how materials are accessed by the physical devices. Trends may be observed for a particular student or group of students concerning access of educational materials according to physical devices, times, modes (full multimedia, text, etc.). Having such information permits an educational institution to ensure that preparation and

formatting of educational materials caters to the observed trends of the student body for reviewing those materials.

[0046] In some embodiments, there may be multiple pathways to educational materials that may be available or offered. For example, the educational institution may allow a sign in from a website or portal accessible over the Internet. Alternatively, access may be provided to materials via direct cell phone access that does not necessarily involve the Internet. By tracking how students access educational materials, the educational institution may ensure that appropriate communications capabilities are available as student body habits for accessing educational materials change.

[0047] Additional statistics may be developed relating to the success of the

presentation of materials over different access devices. For example, if a student is accessing educational information via a smart phone, there are necessarily limits to the amount of information that may be presented on a small screen. Information concerning how much time spent by an individual student and aggregate information for groups of students utilizing different types of access devices may be developed and maintained. An educational institution may thereby determine the success of different presentation media by how much time is spent with that media and the associated presentation devices as an indicator of success of that educational channel or by student success results based on access by particular devices.

[0048] An embodiment learning management system 100 is illustrated in FIG. 1A that may include one or more learning management servers and one or more access terminals. The access terminals may be student-owned devices that may be used by students to connect remotely to the learning management servers. In some

embodiments, the access terminals may be provided by an operator of the learning management server network in connection with a learning center. The learning center may have access terminals connected to the learning management servers or server network, which may be used by students to access course materials. In an

embodiment system 100, one or more terminals 110a, 110b, 110c may connect to a learning management server 140. A mobile terminal 110a may be a cellular

telephone, a smartphone, or any phone that is capable of making a connection to a public network such as the Internet 120 and providing input to and displaying content from a learning management system server 140. The mobile terminal 110a may connect to the learning management server 140 through one of a series of connections. The mobile terminal 110a may connect to a wireless antenna 112 through wireless connection 101a, which may be a WiFi, or other similar wireless connection. The wireless antenna 112 may be coupled to a router 114 or other wireless access device through a connection 112a. The router 114 may be coupled to the Internet 120, generally through an independent service provider (ISP) 116a through a connection 114a. The independent service provider 116a is connected to the Internet 120 through a connection 121a. In some embodiments, the connections 114a and 121a may be broadband, high-speed connections that allow the transfer of multimedia content between the server 140 and the mobile terminal 110a.

[0049] In some embodiments, access terminals may connect through a cellular network. For example, mobile terminal 110a may connect to the server 140 through a cellular connection 102 through a cell tower 118 and associated infrastructure 119. The infrastructure 119 may connect to the Internet 120 through a connection 121b, which may include components that are part of the cellular or public infrastructure such as a public switched telephone network (PSTN). The server 140 may be connected to the Internet 120 through a connection 141a to a service provided 116b, which in turn is connected to the Internet 120 through a connection 121c. In some embodiments, the connection 121c may be a high speed, broadband connection capable of handling large volumes of inbound and outbound traffic and the service provider 116b may be a commercial high capacity service provider. Although the access terminals are illustrated and described as being associated with student access, access terminals of the kind described hereinabove may be applicable to providing access to instructors, administrators or anyone who is required to (e.g., and authorized to) access the server 140.

[0050] The terminal used to connect to the learning management server 140 may be one of a variety of terminal types. For example, a laptop computer terminal 110b may be used to connect to the learning management server 140. The laptop computer terminal 110b may have a wireless communication capability and may connect to the wireless antenna 112 through a wireless connection 101b. Alternatively, the laptop computer terminal 110b may connect to the router 114 through a wired connection 103b. In another example, a desktop computer 110c may be used to connect to the learning management server 140. The desktop computer may have a wireless communication capability and may connect to the wireless antenna 112 through a wireless connection 101c. Alternatively, the desktop computer terminal 110b may connect to the router 114 through a wired connection 103 c. Although three types of access terminals 110a- 110c are illustrated, many other types of computing devices may be used as terminals to connect to the learning management server 140 provided the devices are equipped with a communications capability in order to connect with the server 140. Access terminals may also include local terminals that are directly connected and do not require an Internet connection.

[0051] In some embodiments, one or more terminals may be connected to one or more learning management servers as illustrated in FIG. IB. A Student A may connect to one or more servers 140a-140d of the learning management system from a terminal 113a. For ease of description, the terminal 113a is illustrated as a desktop terminal. However, any terminal may be used that is capable of communicating and interacting with one or more of the learning management servers and displaying content provided by the learning management servers. A Student A may connect from the desktop terminal 113a to one or more servers 140a-140d through the Internet 120. For ease of description a connection 12 Id (and connections 121e-121i) may represent any kind of connection to the Internet 120 such as a wireless connection a wired connection or some combination of a wireless and wired connections (e.g., through a wireless router and an ISP). Similarly Student B may connect from a desktop terminal 113b to one or more of the servers 140a-140d through a connection 121e to the Internet 120. Student n may connect from a desktop terminal 113c to one or more of the servers 140a-140d through connection 12 If to the Internet 120. The servers 140a- 140c may connect to the Internet 120 through connection 12 lg, which may be any kind of connection, including a direct leased connection through a network, a connection through a service provider or other kind of wireless or wired connection. In some embodiments, the learning management server may be accessible through a satellite connection in order to reach remote locations. In some examples, a terminal may be directly connected to one or more of the servers 140a-140d. Such an example is shown in connection with desktop computer terminal 113c over direct connection 12 lj to the server 140d.

[0052] In some embodiments, the servers 140a-140d may operate in connection with data storage elements 143a-143f in a learning management system. Some of the data storage elements, such as data storage elements 140a- 140d may be connected directly to a corresponding one of the servers 140a-140d. In further embodiments some of the data storage elements may be connected directly to the network (e.g., data storage element 143e). In further embodiments, "cloud" storage elements may be used such as data storage element 143f. The data storage elements may be used to store any of the information elements that are used within the system including, but not limited to, student records, analytical results, analytical records, analytical factors, applications, programs, and other elements. When student records (e.g., private or personal information records) are stored, it may be necessary to incorporate data security on the data itself and on the communication channels for accessing the data storage elements.

[0053] In some embodiments, any of the connected data storage elements 143 a- 143 f may be accessed by other servers on the learning management system network. In alternative embodiments, one or more of the data storage elements 140a- 140f (e.g., data storage element 143f) may be a third party repository of educational material to which elements of the learning management system may be incorporated or with which the learning management system may interoperate. The third party repository may also be used to generate collaborative materials as described in greater detail hereinafter. In other alternative embodiments, one or more of the servers 140a- 140d and the data storage elements 143a-143f may be incorporated with, in communication with, connected to, or otherwise interoperable with an existing institutional enterprise deployment for creation and distribution of educational materials.

[0054] A learning management system fulfills various requirements. For example, the learning management system provides an infrastructure (e.g., server, access channels, storage, etc.) that allows for personalized provisioning of learner interactions and experiences. The personalized provisioning may be based on a rich array of statistical analysis provided based on maintaining Student Learning Profiles and other records of the courses, the instructors, the institutional history. The personalized provisioning allows for remediation and enrichment of learning experiences that, in turn, will lead to higher rates of material retention, learning momentum and, ultimately, learning success.

10055] In some embodiments, the learning management system may be based on a centralized architecture 200 illustrated in FIG. 2A. As part of the centralized architecture 200, a learning management system 230 may include functional components, which may be provided through application programming interfaces that call other services. The centralized architecture 200 may be interoperable with student access layers, instructors, content authors, external enterprises, third party content providers, administrators and others. Accordingly, in some embodiments, a learning management system 230 may interoperate with an instructor access layer 210, a content author layer 212, an external enterprise layer 214, and a third party content layer 216. The learning management system 230 may further interoperate with a Student A access layer 220, a Student B access layer 222, up to a Student n access layer 224. By "layer" reference may be made to a group of functional components of the centralized architecture 200 that share the same level of functional characteristics. In addition to the layers, data storage repositories, which may be associated with one or more of the various layers, may be functionally interoperable with the centralized architecture 200. For example, the learning management system 230 may be interoperable with a third party repository 262, such as may be associated with an existing institutional enterprise deployment for creation and distribution of

educational materials. The learning management system 230 may further be

interoperable with a curated repository 264, and a publisher repository 266. By enabling interoperability with existing institutional enterprise repositories, curated repositories, publisher repositories and other repositories and associate layers provides a mechanism for rapid adoption and distribution of the latest course materials and updates to existing course materials. Such materials may include materials that are newly developed or updated to be tailored to the learning modalities as described herein. Interoperation with existing enterprises and third party repositories further allows for the creation of collaborative course content, and development and refinement of existing course content. In some embodiments, secondary repositories of information, such as the third party repository 262, the curated repository 264, and the publisher repository 266, may be integrated in a manner that allows them to be presented and accessed as native objects through the core of the centralized

architecture 200. In some embodiments, replication and deployment of the centralized architecture 200 at the individual institutional level may allow for further

enhancement and customization of the base architecture. By providing the centralized architecture 200, institutions may be provided with the ability to expand beyond common, core offerings to offer other unique courses and learning experiences of their own or of collaborative design.

[0056] In some embodiments, the learning management system 230 may include a personalized learning monitor 232 that delivers content through a content presentation module 236 (including dashboard components and tools available for students). The personalized learning monitor 232 may further receive input from the functional student access layers in a student input module 234. The personalized learning monitor 232 may be configured with a timer capability to assess the amount of time students spend with certain materials. The timer capability may allow the time spent with materials to be assessed in a variety of ways that may be useful to statistical analysis. For example, the timer may record total elapsed study time, total cumulative study time, average study time per study session, and so on. Conclusions may be drawn regarding the affinity for a particular study material type based on, inter alia, a variety of time statistics.

[0057] As the personalized information monitor 232 delivers content and receives and processes input, which may include student access layer study-related input and performance-related input, and otherwise interacts with the various layers of the centralized architecture 200, the information may be used to update a personalized data system (PDS) 234. In some embodiments, data, including statistical data, statistical analysis data, basic data, and other data may be pushed from various layers of the learning management system 230 to a master database associated with the PDS 234. By updating the master database during operation of the learning management system 230, an individualized data profile or student learning profile (SLP) may be developed for each student. In the illustrated example, the PDS 234 may include a student learning profile SLP A 242, a student learning profile SLP B 244, up to a student learning profile SLP n 246. The student learning profiles 242, 244, and 246 may be divided into clusters based on similarities within learner groups, allowing for some degree of standardization in the content production cycle. In some embodiments of the centralized architecture 200 and the learning management system 230, statistical analysis may be conducted in an analytics module 250. In some

embodiments, the analytics module 205 may perform the analytics by applying standardized web analytics tools having multivariate analysis capabilities. In other embodiments, a server processor may be configured to perform the multivariate analysis on the collected data.

[0058] A framework 201 as illustrated in FIG. 2B, may thereby be provided for collecting usage data, tagging content, and providing a means for providing unique content (e.g., by material type) based on predictive modeling for each student or a collection of students. Accordingly, the content may be tagged to provide fundamental reporting of analytical results by the analytics module 250. In some embodiments, data on student usage and access to materials, shown as LMS data 292, external data 294, and SIS data 296, which may be obtained from student learning profile SLP n 246, some of which may be collected by the personalized content monitor 232, may be passed to the analytics module 250. Analysis results may be propagated to learning management system 230 recommendations module 251 so that material offerings, course offerings, or other recommendations or suggestions may be provided. In alternative embodiments, a secondary repository may store demographic and reporting data that may be utilized for hierarchical and segmentation analysis. Alternatively this information may be stored in connection with the PDS 234 described in connection with FIG. 2A. In some embodiments, for ease of description, the personalized learning monitor is disclosed as performing management functions and operations within the learning management system. The personalized learning manager may facilitate the delivery of personalized content and is distinguishable from an abstraction filter, database abstraction layer, or other mechanism to simply interface with a repository or database. While the personalized learning monitor may access information from data storage elements, other functions are performed such as monitoring student interaction with course materials, monitoring timing, requesting and assessing data from statistical analytics operations and other operations.

[0059] In some embodiments, course content, in the form of content pages from the content repository, which may include all other linked open source or proprietary repositories, may be pushed through a common view layer 290. The recommendations module 251 in the learning management system 230 may provide predictive content provisioning. In some embodiments, student performance data (e.g., grades) may be stored and obtained from the learning management system 230, through the personal learning monitor 232, and the particular one of the Student Learning Profiles 242, 244, and 246 or a separate student performance databases. Student performance data may be fed back to the analytics module 250 to refine statistical correspondence between course offerings, material types, recommendations, etc. and student performance or success. In some embodiments, student progress and other factors may be presented and monitored via a dashboard and a series of individual gauges for different factors or metrics.

[0060] In a central content repository 270, databases associated with learning management system 230 may contain an initial taxonomy (classification) of materials and learning objects. An example of a database having classifications is illustrated in FIG. 2C. The materials and learning objects may be accessed, manipulated and delivered through the use of learning object tagging protocols. In some embodiments, the learning management system 230 may have a personalized content delivery model in which learning materials will be passed from the central content repository 270 to the common view layer 290 (FIG. 2B) where the content may be paired with a variety of other objects, systems, supplementary materials, through application programming interfaces that call other services.

[0061] In one possible implementation of one or more of the various embodiments, logic steps and linkages between components necessary for delivery are described. Course content in a layer 276, including course units in a layer 278, and materials in a layer 280 may be stored in central content repository 270 with various associations, which may evolve over time. The content and materials may be stored in repositories such as data storage elements, based upon standardized taxonomies (e.g., content classifications) shown as taxonomy layer 274. Such taxonomies in the layer 274 may be created by instructional designers to classify learning assets by hierarchies, based upon their relationship to other materials and assets. The specific structure of a taxonomy is determined as the needs for each course are analyzed and content is developed. The taxonomies in layer 274 and the association of courses, materials, learning object to the taxonomies may evolve over time based on ongoing analysis and content development.

[0062] In some embodiments, additional robustness may be provided to the

taxonomies (e.g., classification schemes) in layer 274 through analysis. Taxonomies in layer 274 may be strengthened through latent semantic analysis of content, for purposes of ontological classification that links basic taxonomies with higher level classifications or ontologies shown in layer 272. Such analysis may be conducted using tools such as Common Library and Open Calais, which may be connected to the learning assets and classifications through the common view layer 290 that exposes or presents learning assets for assembly, while protecting the integrity of the stored assets. Asset collections may be exposed in the above described manner, or content and classifications may be authored and asserted directly. By allowing taxonometric and ontological analysis both core content and supplemental content may be aligned according to the ontologies (student/learner categories). By aligning ontologies reusable learning objects may be created that are tailored to specific ontologies.

Additionally, the use of latent semantic analysis allows learning objects, materials and assets to be easily reused. Based upon ontological ordering or classification, the same learning object, material, or asset may be matched to goals and objectives across multiple courses.

[0063] In some embodiment methods, certain operations may be performed in implementing a learning management system. The operations may be performed by a processor that is configured to carry out the operations according to the description provided herein. One such embodiment method 300 is illustrated in FIG. 3A for analyzing the relative performance of students based on the subject matter, type of materials and the relative time with the materials of the particular type. For example, in block 305, Student A may be tracked to determine the amount of time spent with the materials of Type A (e.g., text materials) for Subject A. In block 307, the performance of Student A in a test or evaluation of the degree of learning success for the Subject A may be tracked. The Student A will achieve a certain performance level or grade for a course or a unit of a course in Subject A. Similarly, in a separate series of operations (not shown), Student B may review the material of Type A (e.g., text materials) for the Subject A and may achieve a higher score than Student A. In block 309, a comparison may be performed between the results for the Student A and the Student B for the Subject using the materials of the Type A (e.g., text materials). [0064] In some embodiments, the results for the Student A and the Student B may be normalized. In other words, the results may be evaluated taking into account systematic variables that differ between the Student A and the Student B. In block 311, the results may be normalized for intelligence. In block 313, the results may be normalized for other factors, such a time, number of interruptions, etc. It may also be the goal of normalization to select an appropriate "Student B" candidate based on similar factors, such as intelligence, etc. Based upon the normalized results, in block 315 it may be determined whether Student A received a lower performance in Subject A than Student B. If Student A received a lower grade or a higher grade in the performance evaluation than Student B (block 315 = YES or NO), the evaluation may conclude that Student A processes text information in a different fashion than Student B, taking into account the normalization factors. In block 317, a Student Learning Profile may be updated or, if not available, may be created to take account of the different assimilation or processing of material of Type A for the Student A.

Optionally, the Student Learning Profile for Student B may also be updated, such as by increasing a weight or other factor, indicating that Student B performs

comparatively better when studying materials of Type A. Within an example learning information management system, a personalized learning monitor, which may be a functional module, may perform the monitoring and tracking of information and updating of results. In various embodiments, the personalized learning monitor may keep track of all of the courses that both the Student A and the Student B take, the performance results for all courses and the factors or metrics associated with the type of materials used during preparation.

[00651 By creating and evaluating a student learning profile in the various

embodiments, analysis, such as through the personalized learning monitor, may evaluate the performance of Student A in a course having more visual instructional materials than textual materials. The Student Learning Profile may be updated to reflect the affinity of Student A for courses having visual materials rather than courses where textual materials predominate. A Student Learning Profile may be initialized during initial registration and may include information obtained from the student during the registration or an intake procedure. Basic demographic information may be collected along with more detailed information, such as previous school experience, previous performance, test results for tests administered during the intake procedure such as aptitude tests, personality tests, and other tests. By collecting as much information as possible during the intake procedure a more effective initial Student Learning Profile may be created that will more rapidly provide useful statistics for selecting and provisioning course materials and assets.

[0066] In an embodiment method 301, which is illustrated in FIG. 3B, in block 319, the personalized learning monitor may offer materials of the Type A and the type B for the Subject A to Student A. In block 321, the personalized learning monitor may track the amount of time the Student A spends with material of the Type A for the Subject A as the Student A engages and progresses in particular course. In block 323, the personalized learning monitor may track the amount of time the Student A spends with material of the Type B. In block 325, the personalized information monitor may compare the amount of time the Student A spends with the material of the Type A and the material of the Type B. In block 327, information regarding which type of material, the Type A material or the Type B material the Student A spent more time with may be recorded in the Student Learning Profile. For example, in block 329 if the Student A spends more time with visual materials than with textual materials, the information may be recorded and a correlation may be developed between the predominant material type and the success statistics for the Student A in different courses, in the Student Learning Profile.

[0067] By assessing the predominant modality for students, personalized content may be delivered to students to facilitate success in mastering the course material, as in an embodiment method 302 illustrated in FIG. 3C. In block 331, a request or other indicator may be received, such as by or in a server, from Student A for study materials for Course A. In block 333, the server may receive information regarding Student A from the student learning profile, SLP A, associated with student A. In block 335, the server may receive additional data, such as data from external sources or other sources (e.g., repositories) within the learning management system that may be relevant to Course A. In block 337, the server may receive additional data from the Analytics Module, such as data that may be useful to form recommended content for Course A, particularly as it may relate to Student A. In block 339, the server may analyze or may have previously analyzed the predominant learning modality for the Student A sufficient to prepare material for delivery to Student A's view. In block 341, the server may select the materials and asset class or classes for the materials for delivery or provisioning. In block 343, the server may provision (e.g., deliver) the selected content and/or course materials to a designated area within Student A's view corresponding to the selected asset level or levels for the materials.

[0068] In some embodiments, course content creation and content presentation may follow a standard template model in which dynamic content provisioning blocks, discrete content entry areas, or designated placeholders for provisioning of specific course content may be displayed on a web page or view visible to students,

instructors, content creators or other course material collaborators. The placeholders function as designated areas for the provisioning and display of personalized course content that may be configured according to predominant learning modality for the student and other factors. Taking advantage of the designated areas may allow for granular provisioning of content at a selected content, material, or asset level within a page or view. The content may be locked-down content (e.g., content that does not or cannot be changed), open content (e.g., content that can be manually changed). The designated area may further allow for provisioning of the recommended, selected, suggested content, which may change from student to student, course to course, material type, and so on, based on the analytic factors and other factors.

[0069] Over time, the amount of data tracked in the Student Learning Profile increases. Based upon the data collected over time in the Student Learning Profile of the Student A including the courses taken by the Student A, the success rate for Student A in the different courses, and other factors, the personalized learning monitor may make certain conclusions about Student A. For example, the personalized learning monitor may conclude that the Student A is more likely to succeed in a particular course of instruction when visual materials are provided. Therefore, the Student A, when possible, should be provided with visual instruction materials as opposed to textural materials. Alternatively, the Student A should be provided with a greater proportion of visual materials when visual and textual materials are available. Given the likelihood of other visual learners in a student body, the personalized learning monitor may well suggest more visual materials in addition to textual materials for any given course.

[0070] Use of an embodiment learning management system may yield several different types of results within a student body. For example, and without limitation, the personalized learning monitor may evaluate the Student Learning Profiles of the students (e.g., Student A and Student B) and, based on the knowledge that one student (e.g., Student A) responds more positively to visual materials than to textual materials, present that student (when possible) with more visual materials than textual materials in future courses. Continuing the example, if the Student Learning Profile for another student (e.g., Student B) reveals that the individual responds better to completely different types of material, in future courses, the personalized learning monitor may deliver to that student such different types of materials covering the same subject matter.

[0071 J In various embodiments, the personalized learning monitor may not be simply "student centric" and may encompass and consider the subject matter being taught, academic and certification requirements, and course content in developing the personalized learning materials presented to each student. Using the analyses noted above, the personalized learning monitor may also evaluate courses and course content to determine particular course offerings based on material types or other factors.

[0072] In most courses, evaluation of the success of students is based upon

examinations or evaluation of projects that are turned in by the students. These may be in the forms of quizzes, exams, papers, and other input from the students that is subsequently evaluated and graded in some fashion. Further, most courses have a measure of success by students, usually reflected as a grade, for individual units of instruction within the course, and for the course itself. Given the course structure and grading system used, the personalized learning monitor may keep track of statistics associated with how well a group of students does in a particular unit of instruction within a course, or for the overall course itself. In a typical approach to analyzing group performance, a statistical distribution (e.g., bell curve) may be developed for the group. For example, a statistical distribution of grades associated with a group performance in any examination or graded paper may be developed. During the course of the evaluation of a group grade distribution for a particular unit of instruction, the personalized learning monitor may find that the overall average and standard deviation of grades for a particular instructional unit is lower than another

instructional unit. The statistical analysis of the grades or results, which may be controlled for a variety of demographic and other factors, may indicate to the professor that the particular unit of instruction was not assimilated as successfully as another unit of instruction, (e.g., where a higher grade average and more narrow standard deviation was observed). Other statistical analyses and measures are possible. Thus the statistical analysis may be useful in evaluating the relative difficulty of different course units. It may further be possible to evaluate whether, because of a predominant focus on visual or textual materials, the particular unit would be more or less difficult for particular students depending upon their affinity for the predominant material type.

[0073] In various embodiments, in connection with individualized instruction, information concerning the learning styles (e.g., visual, textual, or other modalities) of the individual students taking course may be factored into the course materials that are presented to the individual students. In the case of Student A and Student B, noted above, the personalized learning monitor may evaluate the Student Learning Profile of each individual student that is developed over a number of courses. The personalized learning monitor may recognize the most effective learning style of each student, and present the materials for a particular course according to the most effective material types for each student, such that the presented material may differ for the different students. Over time, statistical analysis of learning outcomes or results for individual units of instruction within a course may be performed. The results of the statistical analysis may inform an instructor or administrator as to whether or not the course objectives are being met by the materials that have been created. Alternatively, the system may analyze the results and provide adjustments to how the course materials are prepared and delivered.

[0074] In some embodiments, the personalized learning monitor may evaluate the effectiveness of individual instructors in a more accurate fashion. Statistical analysis of course results by instructor may be conducted. Computational controls of the statistical analysis may be performed based on student results as a function of the learning styles of the student and other factors, when analyzing instructor

effectiveness. When learning styles are taken into account in the statistical analysis, the results and success in assimilating information concerning a particular

instructional unit or course might then be used as an index of the effectiveness of the teacher creating or presenting the course or selecting the course materials.

[0075] The various embodiments described above may be implemented with a variety of terminals in the form of personal computing devices, such as a laptop computer 410 as illustrated in FIG. 4. Many laptop computers include a touch pad touch surface 417 that serves as the computer's pointing wireless device, and thus may receive drag, scroll, and flick gestures similar to those implemented on mobile computing wireless devices equipped with a touch screen display and described above. A laptop computer 410 will typically include a processor 411 coupled to volatile memory 412 and a large capacity nonvolatile memory, such as a disk drive 413 or Flash memory. The laptop computer 410 may also include a floppy disc drive 414 and a compact disc (CD) drive 415 coupled to the processor 411. The laptop computer 410 may also include a number of connector ports coupled to the processor 411 for establishing data connections or receiving external memory wireless devices, such as a USB or FireWire® connector sockets, or other network connection circuits for coupling the processor 411 to a network. In a notebook configuration, the computer housing includes the touchpad 417, the keyboard 418, and the display 419 all coupled to the processor 411. The laptop computer 410 may also include a position sensor 425, such as a GPS receiver, coupled to the processor 411. Additionally, the laptop computer 410 may have one or more antenna 408 for sending and receiving electromagnetic radiation that may be connected to one or more a wireless data link and/or cellular telephone transceivers 416 coupled to the processor 411. The cellular telephone transceivers 416 may be configured to communicate via a LTE network as well as a conventional CS network. The laptop computer 410 may also include a camera 426 coupled to the processor 411. Other configurations of the computing wireless device may include a computer mouse or trackball coupled to the processor (e.g., via a USB input) as are well known, which may also be used in conjunction with the various embodiments.

[0076] The various embodiments may also be implemented on any of a variety of commercially available servers, such as the server 500 illustrated in FIG. 5. Such a server 500 typically includes a processor 501 coupled to volatile memory 502 and a large capacity nonvolatile memory, such as a disk drive 503. The server 500 may also include a floppy disc drive, compact disc (CD) or DVD disc drive 504 coupled to the processor 501. The server 500 may also include network access ports 506 coupled to the processor 501 for establishing network interface connections with a network cable 505. The network interface may connect to a network, such as a local area network coupled to other broadcast system computers and servers, the Internet, the public switched telephone network, and/or a cellular data network (e.g., CDMA, TDMA, GSM, PCS, 3G, 4G, LTE, or any other type of cellular data network).

[0077] The processors 411, and 501 may be any programmable microprocessor, microcomputer or multiple processor chip or chips that can be configured by software instructions (applications) and transformed into a special purpose computer to perform a variety of operations and functions, including the functions of the various embodiments described above. In some devices, multiple processors may be provided, such as one processor dedicated to wireless communication functions, one processor dedicated to graphics functions, and one processor dedicated to running other applications. Typically, software applications may be stored in the internal memory 412, 413, 502, and 503 before they are accessed and loaded into the processors 411 and 501. The processors 411 and 501 may include multiple processing cores. The processors 411 and 501 may include internal memory sufficient to store the application software instructions. In many devices the internal memory may be a volatile or nonvolatile memory, such as flash memory, or a mixture of both. For the purposes of this description, a general reference to memory refers to memory accessible by the processors 411 and 501 including internal memory or removable memory plugged into the device and the memory provided within the processor 411 and 501. As discussed above, the various embodiments may be used for delivering a variety of rich media content, and not just Internet web content. Accordingly, the scope of the claims should not be limited to Internet web content delivery and reception unless specifically recited.

[0078] The foregoing method descriptions and the process flow diagrams are provided merely as illustrative examples and are not intended to require or imply that the steps of the various embodiments must be performed in the order presented. As will be appreciated by one of skill in the art the order of steps in the foregoing embodiments may be performed in any order. Words such as "thereafter," "then," "next," etc. are not intended to limit the order of the steps; these words are simply used to guide the reader through the description of the methods. Further, any reference to claim elements in the singular, for example, using the articles "a," "an" or "the" is not to be construed as limiting the element to the singular.

[0079] The various illustrative logical blocks, modules, circuits, and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, computer software, or combinations of both. To clearly illustrate this interchangeability of hardware and software, various illustrative components, blocks, modules, circuits, and steps have been described above generally in terms of their functionality. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the overall system. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.

[0080] The hardware used to implement the various illustrative logics, logical blocks, modules, and circuits described in connection with the embodiments disclosed herein may be implemented or performed with a general purpose processor, a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field

programmable gate array (FPGA) or other programmable logic device, discrete gate or transistor logic, discrete hardware components, or any combination thereof designed to perform the functions described herein. A general-purpose processor may be a microprocessor, but, in the alternative, the processor may be any conventional processor, controller, microcontroller, or state machine. A processor may also be implemented as a combination of computing devices, e.g., a combination of a DSP and a microprocessor, a plurality of microprocessors, one or more microprocessors in conjunction with a DSP core, or any other such configuration. Alternatively, some steps or methods may be performed by circuitry that is specific to a given function.

[0081] In one or more exemplary embodiments, the functions described may be implemented in hardware, software, firmware, or any combination thereof. If implemented in software, the functions may be stored as one or more instructions or code on a non-transitory computer-readable storage medium. The steps of a method or algorithm disclosed herein may be embodied in a processor-executable software module executed, which may reside on a tangible or non-transitory computer-readable storage medium. Non-transitory computer-readable storage media may be any available storage media that may be accessed by a computer. By way of example, and not limitation, such computer-readable media may comprise RAM, ROM, EEPROM, CD-ROM or other optical disk storage, magnetic disk storage or other magnetic storage devices, or any other medium that may be used to carry or store desired program code in the form of instructions or data structures and that may be accessed by a computer. Disk and disc, as used herein, includes compact disc (CD), laser disc, optical disc, digital versatile disc (DVD), floppy disk, and blu-ray disc where disks usually reproduce data magnetically, while discs reproduce data optically with lasers. Combinations of the above also can be included within the scope of non-transitory computer-readable media. Additionally, the operations of a method or algorithm may reside as one or any combination or set of codes and/or instructions on a non- transitory machine readable medium and/or non-transitory computer-readable medium, which may be incorporated into a computer program product.

[0082] The preceding description of the disclosed embodiments is provided to enable any person skilled in the art to make or use the present invention. Various

modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the invention. Thus, the present invention is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the following claims and the principles and novel features disclosed herein.

Claims

CLAIMS What is claimed is:

1. A personalized learning management system comprising:

a data storage element; and

a server coupled to the data storage element, the server having a server processor configured with instructions to perform operations comprising:

analyzing information from a student learning profile associated with a student to determine a learning modality for the student;

storing the student learning profile in a database maintained in the data storage element;

analyzing course information stored in the database for a course offering selected for study by the student;

determining, from the analyzed course information and the determined learning modality for the student, a study material type among a plurality of available study material types associated with a plurality of learning modalities; selecting study materials for the course offering based on the determined study material type;

selecting one or more asset levels associated with the selected study materials; and

providing the selected study materials for the course offering to the student according to the selected one or more asset levels.

2. The personalized learning management system of claim 1, wherein the server processor is configured with instructions to perform operations such that providing study materials for the course offering to the student according to a determined one of the plurality of study material types based on the determined learning modality for the student further comprises:

providing the selected study materials to one or more designated areas within a view accessible to the student.

3. The personalized learning management system of claim 2, wherein: the one or more designated areas correspond to the selected one or more asset levels; and

the content of the one or more designated areas is configured to be changed through provisioning according changes in selected study the materials.

4. The personalized learning management system of claim 3, wherein the one or more designated areas comprise one or more dynamic content provisioning blocks.

5. The personalized learning management system of claim 1, wherein the server processor is configured with instructions to perform operations further comprising: initializing the student learning profile by receiving and storing in the student learning profile, one or more of intake data and demographic data for the student.

6. The personalized learning management system of claim 1, wherein the server processor is configured with instructions to perform operations further comprising:

receiving and storing statistical data associated with the student.

7. The personalized learning management system of claim 6, wherein the statistical data includes one or more of:

data indicative of performance of the student in the course offering based on the study materials provided according to the determined one of the plurality of study material types;

data indicative of the amount of time spent by the student with the provided study materials; and

data indicative of the amount of time spent by the student accessing the provided study materials with an access device type.

8. The personalized learning management system of claim 1, wherein the server processor is configured with instructions to perform operations such that analyzing information from a student learning profile associated with a student to determine a learning modality for the student comprises:

offering study materials for the course offering of a first study material type and a second study material type according to a respective first learning modality and a second learning modality;

tracking an amount of time spent by the student with the offered study materials of the first study material type;

tracking an amount of time spent by the student with the offered study material of the second study material type;

tracking a performance result of the student in the course offering;

comparing the tracked amount of time of the first study material type, the tracked amount of time of the second study material type, and the performance result to determine the learning modality type for the student; and updating the learning modality maintained in the student learning profile for the student based on the comparison.

9. The personalized learning management system of claim 1, wherein the server processor is configured with instructions to perform operations such that analyzing information from a student learning profile associated with a student to determine a learning modality for the student comprises:

tracking an amount of time spent by the student with the provided study materials of the determined study material type for the course offering;

tracking a performance result for the student for the course offering based on the amount of time spent by the student with the provided study materials of the determined study material type;

comparing the tracked amount of time and the performance result with stored amounts of time and performance results for one or more other students based on study materials of the determined study material type; and

updating the learning modality maintained in the student learning profile for the student based on the comparison.

10. A method of providing personalized learning comprising:

analyzing, in a server, information from a student learning profile associated with a student to determine a learning modality for the student;

storing the student learning profile in a database maintained in a data storage element;

analyzing, in the server, course information stored in the database for a course offering selected for study by the student;

determining, in the server, from the analyzed course information and the determined learning modality for the student, a study material type among a plurality of available study material types associated with a plurality of learning modalities; selecting study materials for the course offering based on the determined study material type;

11. The method of claim 10, wherein providing study materials for the course offering to the student according to a determined one of the plurality of study material types based on the determined learning modality for the student further comprises: providing, by the server, the selected study materials to one or more designated areas within a view accessible to the student

12. The method of claim 11, wherein:

the one or more designated areas correspond to the one or more asset levels associated with the selected study materials; and

the content of one or more designated areas is configured to be changed through provisioning according changes in the selected study materials.

13. The method of claim 12, wherein the one or more designated areas comprise one or more dynamic content provisioning blocks.

14. The method of claim 10, further comprising:

initializing the student learning profile by receiving, in the server, and storing in the student learning profile, one or more of intake data and demographic data for the student.

15. The method of claim 10, further comprising:

receiving, in the server, and storing in the database statistical data associated with the student.

16. The method of claim 15, wherein the statistical data includes one or more of: data indicative of performance of the student in the course offering based on the study materials provided according to the determined one of the plurality of study material types;

17. The method of claim 10, wherein analyzing information from a student learning profile associated with a student to determine a learning modality for the student comprises:

offering, by the server, study materials for the course offering of a first study material type and a second study material type according to a respective first learning modality and a second learning modality;

tracking, in the server, an amount of time spent by the student with the offered study materials of the first study material type; tracking, in the server, an amount of time spent by the student with the offered study material of the second study material type;

tracking, in the sever, a performance result of the student in the course offering; comparing, in the server, the tracked amount of time of the first study material type, the tracked amount of time of the second study material type, and the

performance result to determine the learning modality type for the student; and

updating, in the server, the learning modality maintained in the student learning profile for the student based on the comparison.

18. The method of claim 10, wherein analyzing information from a student learning profile associated with a student to determine a learning modality for the student comprises:

tracking, in the server, an amount of time spent by the student with the provided study materials of the determined study material type for the course offering; tracking, in the server, a performance result for the student for the course offering based on the amount of time spent by the student with the provided study materials of the determined study material type;

comparing, in the server, the tracked amount of time and the performance result with stored amounts of time and performance results for one or more other students based on study materials of the determined study material type; and