Abstract:
An on-demand learning system provides an enhanced leaning environment capable of delivering relevant content on virtually any topic to specific learners. The learning system implements technical features that facilitate curation and subject matter validation of many different types of content. The technical architecture of the learning system also supports intelligent matching of learners to subject matter areas, creation of specific subject matter boards, and resilient maintenance of the boards.

Description:
RELATED APPLICATIONS 
       [0001]    The present patent document claims the benefit of priority to India provisional Patent Application No. 4234/CHE/2014, filed in the Indian Patent Office on Aug. 29, 2014, and titled “On-Demand Learning System,” the content of which is incorporated herein by reference in its entirety. 
       TECHNICAL FIELD 
       [0002]    This disclosure relates to a system architecture that provides on-demand learning. 
       BACKGROUND 
       [0003]    Rapid advances in computer and communication technologies have resulted in data networks that reach virtually every part of the planet. These technologies support unparalleled access to enormous and disparate repositories of information taking many different forms that provide a raw resource for learners who desire to educate themselves on nearly any topic. Technical improvements in system architectures will facilitate finding and validating relevant information, as well as matching the information to specific learners. 
     
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         [0004]      FIG. 1  illustrates a high level view of the environment in which the on-demand learning system operates. 
           [0005]      FIG. 2  illustrates a more detailed view of the on-demand learning system. 
           [0006]      FIG. 3  depicts an exemplary content portal and its constituent content interfaces. 
           [0007]      FIG. 4  depicts the integration of social medial elements within an exemplary content interface. 
           [0008]      FIG. 5  depicts a content portal that was updated to reflect a change in a particular user characteristic. 
           [0009]      FIG. 6  illustrates a detailed view of an on-demand learning system in which content interfaces are matched based on specific goals associated with the user. 
           [0010]      FIG. 7  provides a logical flow for the operation of the on-demand learning system. 
           [0011]      FIG. 8  illustrates a detailed view of an on-demand learning system. 
           [0012]      FIG. 9  depicts an exemplary curator input interface and an exemplary subject matter expert input interface. 
           [0013]      FIG. 10  provides a logical flow for the operation of the on-demand learning system. 
           [0014]      FIG. 11  depicts an exemplary content portal before and after applying filter criteria provided by the content consuming entity. 
           [0015]      FIG. 12  depicts an exemplary curator input interface. 
           [0016]      FIG. 13  depicts an exemplary content portal and its constituent content interfaces and content interface recommendations. 
       
    
    
     DETAILED DESCRIPTION 
       [0017]    The on-demand learning system described below helps shape vast islands of available information into an overall landscape that better serves clients, employees, individuals, and any learner, personally and professionally. The system implements learning boards that provide a view (e.g., through a thin layer aggregator) to relevant content, pointing the learner to that content without requiring cumbersome searching by the learner. The learning boards provide access to content of all types, including as examples: books, videos, publications, network web pages, audio recordings, and other types of content. The system architecture provides a connection of expert ideas, people, and substantive content in a highly visual, curated, and expert validated format, that can drive increases in knowledge and skill competency to far greater degrees than, for instance, raw searches through an Internet search engine or on a media website. 
         [0018]    The learning system may also provide for social integration within the learning boards, for example, by gathering the experiences of peers and colleagues who have visited the same, if not similar, topics. Social integration may be further helpful in that it can promote conversation and discovery, and may lead to knowledge generation and capture. Social integration within the learning boards may provide a spring board for users and colleagues to engage in discussions relating to the learning board. Where the topic involves a new technology or a cutting edge problem, the collaboration that takes place may provide novel incites regarding the problem itself or ideas for potential solutions. The learning boards may also help to focus discussions between peers and colleagues on those points which the expert may have identified when developing the learning board. In this way, the learning system may facilitate a collaborative environment promoting the exchange of information. 
         [0019]    In another aspect, social integration may help to shape the learning board itself. For example, a topic may be started by users and colleagues, and may organically develop in this form. An expert may then visit the topic, and if they believe the discussion is worthwhile they may choose to develop a learning board based on the conversation. Users discussing the topic may automatically be directed to view the learning board once created. The expert may choose to examine the topic on their own or the learning system may provide an indication to visit the topic. The expert may be notified, for example, once the conversation has reached a critical mass, which may be in terms of the number of comments, the number of unique commentators, social media indicators (i.e. likes, or retweets), or other similar metrics. 
         [0020]    Social integration may also provide an expert with active feedback regarding the learning boards, which the expert may use to better curate the content that is presented. By way of example, a conversation may develop regarding a particular topic presented on the learning board. Based on the comments, the expert may be able to determine that the users of the learning board are confused about a particular topic, or perhaps have shown interest in a separate aspect of the topic. Based on these observations, the expert may, in turn, modify the learning board to introduce, modify, or remove content. 
         [0021]    In another aspect, the learning system may support cross-platform integration, harnessing the collective knowledge of experts across multiple industries. This has the potential to provide synergistic benefits and promote inter-business relationships. While the following description discusses examples of a technical nature, the learning system is not so limited and can facilitate education on a broad range of learning topics, for example and without limitation, in the management and legal contexts, or in industry areas such as retail and consumer goods and financial services. 
         [0022]    The description below concerns intelligently building content interfaces and content portals tailored to specific individuals. First, some example structural examples of the content portals and content interfaces are provided, along with the system that may generate the content portals and content interfaces. Then, with the headings below serving as general guideposts, the description addresses how the system may create a content interface with reference to human resources data (for example) and how the content may be filtered. The description also addresses how the content may not only be curated for selection on a content interface, but also validated by a subject matter expert. 
         [0023]    The learning system may provide curated access to content of all types, where the content is presented in such a way so as to convey a particular view point or share a particular perspective on a topic. The learning system may do this by leveraging the knowledge and experience of those in the field. The underlying content presented in the learning system may be aggregated from external sites or contributed from within an organization. With respect to content contributed by the organization, the content may be gathered from various sources. The content, for example, may include materials specifically created for instructional or training programs and seminars. Additionally, the learning system may have the ability to parse through an internal knowledge exchange or prior work product database to identify and/or generate relevant content. The organization may also provide the learning system with access to licensed content available through partnerships with other organizations. 
         [0024]      FIG. 1  illustrates a high-level overview of the operating environment of the on-demand learning system  100 . As depicted, the learning system  100  may communicate with various networks including the private intranet  101  and the public Internet  102 . The private intranet  101 , for example, may represent the network infrastructure associated with the organization operating the learning system. It serves to connect the learning system  100  to the organization servers  103 , allowing the learning system  100  to access data and related data services that the organization servers  103  may provide. For instance, the organization servers  103  may provide the learning system with access to an entity profile database  107  and a content database  108 . 
         [0025]    The learning system  100  may also communicate over the public Internet  102 , which allows the learning system to communicate with external information sources, including the example servers and databases identified in  FIG. 1 . The media and content servers  104  allow the learning system  100  to access certain external audio and video content assets  109   a ,  109   b . The learning system  100 , for example, may access audio or video streams, through the public Internet  102 , that are hosted on sites like Vimeo and YouTube. The external video content assets  109   a  can also take the form of a live audio or video broadcast, for example, a live interactive webinar. The learning system  100  may also interact with public and private servers hosting external electronically published journal, magazine, or instructional audio recording (or podcast) content assets  109   c . The learning system  100  may similarly access external content assets  109   a - c  hosted by partner organization servers  106 . 
         [0026]    In some cases, the organization servers  103  themselves may host internal content assets  119 . These internal content assets  119  may include internal content developed by the organization, for example, instructional videos, presentations, or publications. Such material, for example, may have been developed in conjunction with occupational training provided by the organization. 
         [0027]    Furthermore, while  FIG. 1  illustrates the learning system  100  in indirect communication with the organizational servers  106 , it is further contemplated that the learning system  100  may operate directly with or on the organizational server  106 . 
         [0028]      FIG. 2  illustrates additional details regarding the learning system  100 . As seen in  FIG. 2 , the learning system  100  further comprises a communication interface  202  and content analysis circuitry  201 . In one implementation, the content analysis circuitry  201  of the learning system  100  further comprises a central processing unit (CPU)  210 , a memory  211 , one or more network interfaces  212 , and a data storage  213 . The content analysis circuitry may also optionally include a display  214 . The content analysis circuitry  201  may also be in communication with the content portal storage  110 . The content storage portal  110 , for example and without limitation, may be a part of the content analysis circuitry  201  or it may be associated with organization servers  103 . The communication interface  202 , under the control of the content analysis circuitry  201  is configured to communicate with the entity profile database  107  and the content database  108 . The content analysis circuitry  201  may create and maintain the content portals  230  that are stored, e.g., as part of the content portal storage  110 , which are delivered to the content consuming entity  115  using the communication interface  202 . Any number of content portals  230  may be established for any particular user or content consuming entity  115 . The content portals  230  may present the user with instructional content that is specifically curated and subject matter validated. 
         [0029]    The entity profile database  107  contains information regarding the users or content consuming entities  115  that use the learning system  100 . The learning system  100  may further access additional databases such as a human resources database  220 . The human resources database  220  may be a subset of the entity profile database  107 , or a separate database maintained in or by another department of the organization with which the content consuming entities  115  are associated. The human resources database  220  may include user profile data, for example, including fields that provide the user&#39;s role within the organization and the user&#39;s proficiency level with respect to a particular user skill. The human resources database  220 , and correspondingly the entity profile database  107 , may also contain other relevant user attributes or pertinent user records. For example, the human resources database  220  may include data related to the user&#39;s employment history, such as previous positions, roles, or jobs performed by the user. The human resources database  220  may also track information regarding the user&#39;s career objectives, including target job positions and potential growth paths, or any other characteristic that may inform educational content selection for the employee. The learning system  100 , through the content analysis circuitry  201 , may also be able to gather data from the entity database  107 , and may use this information to adjust the appearance of the content portals  230  presented to the content consuming entities  115 . 
         [0030]    The communication interface  202  of the learning system  100  also communicates with the content database  108 . The content database  108  may store internal content assets  119  or the content database  108  may store a reference to the location of a particular internal content asset  119  or external content asset  109 . For example, the content database  108  may store a hyperlink or some other identification data, link, or pointer providing reference to an internal content asset  119  or external content asset  109 . The identification data may direct the communication interface  202  to the location of the internal content asset  119  or external content assets  109 , where the internal content asset  119  or external content asset  109 , itself, may be stored remotely or locally. For example, internal content assets  119  or external content assets  109  may reside on local data storage  213  or on remote servers, for example, media and content servers  104 . By identifying internal content asset  119  or external content assets  109  by reference, the communication interface  202  may be able to take advantage of other servers storing and hosting the underlying internal content assets  119  and external content assets  109 . In some implementations, the communication interface  202  may act as a thin layer aggregating various internal content assets  119  and external content assets  109 , directly or by reference, into a content interface  225 . 
         [0031]    The communication interface  202  also serves to aggregate internal content assets  119  and external content assets  109  or links to selected internal content assets  119  or external content assets  109  that are organized by topic or subject into specific content interface  225  resources. To that end, internal content assets  119  and external content assets  109  may be characterized by content tags  226  that identify the particular contexts in which the internal content assets  119  or external content assets  109  may be relevant. The content tags  226  may also identify particular skills or roles for which the content interface  225  may be relevant. These content tags  226  may also be stored in content database  108 . In some implementations, the content interfaces  225  themselves may similarly be characterized by content tags  226  that identify the particular contexts and/or particular skills or roles for which the content interface  225  may be relevant. The content interface  225  may automatically inherit the content tags  226  of the constituent internal content assets  119  and external content assets  109 , or may include additional content tags  226  that may be added through the curator input interface, as described below. The content interface  225  may also be associated with an entity or group within the organization to which the content interface  225  may be deployed. In this way, the organization may be able to monitor and track the level of engagement, for a given deployment entity, that content consuming entities  115  have with the different content interfaces  225 , which may be helpful in generating reports and performing other analytics. 
         [0032]    More broadly, the communication interface  202  acts as the gateway through which the system develops the content interfaces  225  by providing the tools to assemble, present, and deliver content in a directed fashion. The content interfaces  225  deliver the knowledge and experience of experts in the field who identify the most relevant internal content assets  119  or external content assets  109  and assemble the content assets in a focused and compelling manner. 
         [0033]    As mentioned, the content interface  225  may be constructed as an aggregation of internal content assets  119  and external content assets  109  that are presented to the content consuming entity  115  in a purposeful way. In one aspect of its operation, the content analysis circuitry  201  generates a content interface  225  based on information stored in the content database  108 . The content analysis circuitry  201  may communicate the content interface  225  to any other entity, local or remote with respect to the system, through, e.g., the public Internet  102  or private intranet  101 . For instance, the content analysis circuitry  201  may direct the communication interface  202  to render and display the content interfaces  225  on display  214 . While the display  214  is illustrated as part of the content analysis circuitry  201 , the communication interface  202  may, additionally or alternatively, render a content interface  225  on a remote display  214  (not illustrated), for example on a personal computer or mobile device operated by the content consuming entity  115 . The communication interface  202  may provide the ability to remotely view content interfaces  225  over the private intranet  101  or the public Internet  102  using the network interfaces  212  of the content analysis circuitry  201 . 
         [0034]    The assembly of a content interface  225  by the content analysis circuitry  201  will be described with reference to the learning system  100  environment of  FIG. 2 , and the exemplary content interface  225  rendered on display  214  illustrated in  FIG. 4 . 
         [0035]    With regards to assembling a content interface  225 , the communication interface  202  through the content analysis circuitry  201  may communicate with the content database  108  to retrieve the information used to create the content interface  225 . For example, the content analysis circuitry  201  may retrieve information regarding the number, size, position and arrangement of individual internal content assets  119  and external content assets  109  within a content interface  225 . With reference to  FIG. 4 , the content analysis circuitry  201  may determine that content asset  409   a  is of certain dimensions and positioned above a similarly size content asset  409   b  and adjacent to content asset  409   c  of different dimensions. In the absence of a defined arrangement, the content analysis circuitry  201  may provide a default order or layout in which the internal content assets  119  and external content assets  109  are to be displayed. 
         [0036]    The content database  108  may also store parameters or attributes regarding the manner in which the internal content assets  119  and external content assets  109  should be rendered, which the content analysis circuitry  201  may use in assembling the content interface  225 . For example, for a given external video content asset  109   a  the content database  108  may store the videos native resolution and various encoding parameters. The content analysis circuitry  201  may utilize this information to properly render and resize the content asset  409   a  when generating the content interface  225 . The content database  108  may also identify alternative versions of the same content asset  409   a , which the content analysis circuitry  201  may utilize in generating the content interface  225 . For example, the content analysis circuitry  201  may select the content asset  409   a  most suitable, e.g., based on the quality of the content consuming entity&#39;s  115  network connection, for rendering the content interface  225  on a remote display  214 . 
         [0037]    The communication interface  202  may also present a content interface  225  with different presentation modes which may provide different internal content assets  119  and external content assets  109  or arrange the internal content assets  119  and external content assets  109  in different ways. For example, the content interface  225  may allow for the presentation of a 10 minute video—that may be identified as the default—but also include a 5 minute presentation or a 60 minute podcast as suitable alternative content assets  409   a , allowing them to be selectively displayed by the content consuming entity  115  through a provided content interaction mechanism  407   a . As another example, the content interface  225  may provide a mobile view, a tablet view, or a desktop view, presenting content assets  409   a - c  that are suitable for the device being used by the content consuming entity  115 . This may, for example, take into account what abilities the content consuming entity  115  device has, including for example its video processing capabilities or the resolution of its display 214 in. As another example, the communication interface  202  may present a content interface  225  to the content consuming entity  115  that allows the content consuming entity  115  to select between different views, for example a high-level view and a comprehensive view. In both instances, the content consuming entity  115  may automatically be presented with a view by default and may be provided with the ability to switch between views using interface interaction mechanism  412 . 
         [0038]    The content database  108  may also store relational information regarding internal content assets  119  and external content assets  109 , which defines how various internal content assets  119  and external content assets  109  interact with one another and how various internal content assets  119  and external content assets  109  interface with the user. The content analysis circuitry  201  may use this relational information in generating content interfaces  225  that better engage the user. For example, the communication interface  202 , under the control of the content analysis circuitry  201 , may generate and render for display  214  a content interface  225  that includes a video content asset  409   a , for example a lecturer giving a presentation. The content interface  225  generated by the content analysis circuitry  201  may also include a slide deck content asset  409   b  and an expert comments content asset  409   c , which itself may be audio, video, or text, that is placed alongside the video content asset  409   a.    
         [0039]    The content analysis circuitry  201 , may also generate content interfaces  225  that allow for dynamic interaction between the internal content assets  119  and external content assets  109 . The content database  108  may, for instance, contain coordination and event information, which the content analysis circuitry  201  may use in generating the content interface  225 . Continuing with the previous example, the content interface  225  may control the playback of the video content asset  409   a , and at pre-defined positions pause the video content asset  409   a . This, in turn, may trigger an action regarding the expert comments content asset  409   c , for example the content interface  225  may begin playing an audio segment recorded by a subject expert that comments on or clarifies a point that was being discussed, or introduces or frames the discussion for a point that is going to be discussed in the video content asset  409   a . The content database  108  may also provide the content analysis circuitry  201  with interaction information between video content asset  409   a  and slide deck content asset  409   b . For example, the slides of slide deck content asset  409   b  may be coordinated to automatically change as the video content asset  409   a  progresses. The slides may also provide an interactive component, where the video content asset  409   a  is paused, and the slides in slide deck content asset  409   b  are advanced while being narrated by expert comments content asset  409   c.    
         [0040]    The content database  108  may also record and store information regarding the association of content interfaces  225  to content consuming entities  115 . For example, a content interface  225  generated by the communication interface  202  may allow for a content consuming entity  115  to “follow” the content interface by utilizing interface interaction mechanism  412 . The content analysis circuitry  201  may record this information in the content database  108 . As another example, a content consuming entity  115  may “endorse” the content interface  225  by utilizing interface interaction mechanism  412 , which may also be recorded in content database  108 . 
         [0041]    The content analysis circuitry  201  may use this association information in assembling the content interface  225 . In the situation where a user has endorsed or is following the content interface  225 , the content interface  225  may present the option to remove the endorsement or “un-follow” the content interface  225  through the use of interface interaction mechanism  412 . The content analysis circuitry  201  may also use this association information to determine additional metrics or information, which the communication interface  202  may present in the content interface  225 . For example, the content analysis circuitry  201  may be able to determine aggregate statistics across all content consuming entities  115 , such as the total number of users that are “following” or have “endorsed” the content interface  225 . The communication interface  202 , under control of the content analysis circuitry  201  may then incorporate this information into the content interface  225 . With reference to  FIG. 4 , the communication interface  202 , through content analysis circuitry  201 , may retrieve the relevant information corresponding to content interface  225  from content database  108 , and may present a content interface  225  having a content interface overview  401 . The content interface overview  401  section may further comprise of a content interface identifier  402  portion and an interface developer information portion, which may constitute the title for the content interface  225  and the thumbnail and photo of the subject matter expert  117  who curated the content interface, respectively. The content interface overview  401  may further include a section providing overall interface statistics, including the number of content consuming entities  115  who are currently following the content interface  225  and the number of content consuming entities  115  who have endorsed the content interface  225 . 
         [0042]    The communication interface  202  along with the content analysis circuitry  201  may also record information and track statistics regarding the content consuming entity&#39;s  115  interaction with a content interface  225 , which may be stored in content database  108 . For example, the communication interface  202  may record which internal content assets  119  and external content assets  109  the user has viewed or interacted with. In some instances, the communication interface  202  may more granularly track the interaction between the content consuming entity  115  and the content interface  225 . For example, the communication interface  202  may record when the user began interacting with the content asset  409   a - c , how long the user spent interacting with the content asset  409   a - c , and whether the user has finished or completed interacting with the content asset  409   a - c . As a more specific example, with reference to a video content asset  409   a , the communication interface  202  may record when the user first viewed the video, when the user last viewed the video, and where the user was when last viewing the video. The communication interface  202  may also observe if the content consuming entity  115  viewed the entire video, or some minimum portion of the video, and accordingly may record in the content database  108  that the content consuming entity  115  has “completed” viewing the video content asset  409 . The communication interface  202  may also generate content interfaces  225  that allow the content consuming entity  115  to manually mark or set a particular content asset  409   a - c  as being “complete,” for example if the content consuming entity  115  had viewed the video outside of the learning system  100 . Furthermore, the communication interface  202  may selectively allow the user to mark a content asset  409   a - c  as being complete, based on attributes of the content asset  409   a - c . For example, the content database  108  may store a mandatory viewing flag associated with the content asset  409   a - c , which the content analysis circuitry  201  may interpret as allowing or precluding the presentation of the option to mark as complete when generating the content interface  225 . 
         [0043]    The content analysis circuitry  201  may also use the recorded interaction information generating and assembling the content interfaces  225 . For example, the content analysis circuitry  201  may determine the number of content assets  409   a - c  that the content consuming entity  115  has interacted with or viewed along with the total number of content assets  409   a - c  in the content interface  225  as a whole. The communication interface  202  may include this information, for example, in a personal interface statistics  405  section, when generating the content interface  225 , thereby providing the content consuming entity  115  with information regarding the progress of the content consuming entity  115 . Additionally, or alternatively, the content analysis circuitry  201  may determine those content assets  409   a - c  which the content consuming entity  115  has “completed” viewing or interacting with, which may include those that the content consuming entity  115  has manually marked as complete. The content analysis circuitry  201  may also determine the users overall progress as a percentage. The progress percentage may, for instance, represent the number of “completed” content assets  409   a - c  out of the total number of content assets  409   a - c.    
         [0044]    The content analysis circuitry  201  may also use the more granular interaction information to determine additional or more detailed progress statistics. For instance, the content analysis circuitry  201  may determine the progress of a content consuming entity  115  with respect to an individual internal content asset  119  or external content asset  109 . As an example, the content analysis circuitry  201  may determine that a content consuming entity  115  viewed 15 minutes of a 30 minute video. The communication interface  202  may generate a content interface  225  that presents a progress bar to the content consuming entity  115  showing a 50% completion percentage. The content analysis circuitry  201  may also calculate the overall progress with respect to the content interface  225  as a whole, by weighting the progress of a content consuming entity  115  with respect to the internal content assets  119  and external content assets  109  in a given content interface  225 . For example, the content analysis circuitry  201  may determine that in addition to viewing 15 minutes of a 30 minute video, the user has “completed” viewing two other 15 minute videos. In this context, the communication interface  202  may generate a content interface  225  that presents a progress bar to the content consuming entity  115  showing a 75% completion percentage. 
         [0045]    The communication interface  202 , utilizing the content analysis circuitry  201 , may also facilitate viewing the content interface  225  in a discontinuous manner. For example, the content analysis circuitry  201  may use the recorded interaction information to determine the state of the content interface  225  when the content consuming entity  115  last viewed it. In a subsequent viewing, the communication interface  202  may generate a content interface  225  for display by the content consuming entity  115  where the internal content assets  119  and external content assets  109  are restored to their prior state. For example, the content analysis circuitry  201  may determine that a content consuming entity  115  began watching a 60 minute video content asset  409   a  but stopped watching the video content asset  409   a  after 13 minutes. As another example, where the content consuming entity  115  may have fast-forwarded through a video content asset  409   a , the content analysis circuitry  201  may determine the last point at which the video content asset  409   a  was playing. The communication interface  202  may then present a content interface  225  where the video content asset  409   a  is adjusted to resume where the content consuming entity  115  had left off. 
         [0046]    As noted earlier, the learning system  100  may also provide for social integration, allowing the content consuming entities  115  to communicate with one another, and allowing the learning system  100  to present more refined content interfaces  225 . The communication interface  202  through the content analysis circuitry  201  may present a content interface  225  integrating different social media. With reference to  FIG. 4 , for example, the communication interface  202  may provide a content interface  225  with a social integration panel  410 , which may include active conversations  412   a  being held by content consuming entities  115 . The communication interface  202  may also provide the content interface  225  with a social media interaction mechanism  411 , which may allow the user to add a comment to an existing conversation  412   a  or generate a new conversation  412   b . The learning system  100  itself may provide this social component, but in addition, or in the alternative, the communication interface  202  through the content analysis circuitry  201  may integrate various social media platforms, like Facebook or Twitter, that may allow for dialog between content consuming entities  115 . 
         [0047]    As a general matter, the content analysis circuitry  201 , may include both static and dynamic content when presenting a content interface  225 . User and interface statistics, for example, may be updated at particular intervals, which may vary based on the type of information. Furthermore, the content analysis circuitry  201  may provide the communication interface  202  with a reference to the location of an internal content asset  119  or external content asset  109 , which may be retrieved from a remote server for rendering. In this way, the communication interface  202  may be seen as a thin layer aggregating the various components into a content interface  225 . 
         [0048]    The content analysis circuitry  201  may also construct and maintain a content portal  230 , which may be rendered on display  214 . Like a content interface  225 , the communication interface  202  may display the content portal  230  locally or remotely. The communication interface  202  may create a content portal  230  for a content consuming entity  115  by linking one or more content interfaces to the content portal  230  for display. The communication interface  202 , through the content analysis circuitry  201 , may identify one or more content interfaces  225 , which are arranged to form a content portal  230 . In constructing the content portal  230 , the communication interface  202  may interact with the entity profile database  107 , content database  108 , and the internal content assets  119  and external content assets  109  themselves. The communication interface  202  may arrange the content interfaces  225  in different ways. 
         [0049]    Building Content Portals Responsive to Entity Profiles 
         [0050]    In one aspect of its operation for building a content portal  230  using appropriate content interfaces  225 , the content analysis circuitry  201  of the learning system  100  retrieves data representing user characteristics from the entity profile database  107 . The user characteristic data, for example, may constitute some or all of the user profile data  221  stored in the human resources database  220 . The content analysis circuitry  201  similarly accesses and retrieves the collection of content tags  226  associated with the internal content assets  119  and external content assets  109 , or content interfaces  225 , stored in the content database  108 . By comparing the user characteristic data with the content tags  226 , the content analysis circuitry  201  is able to match the appropriate content interfaces  225  for the particular user or content consuming entity  115 . For example, the content analysis circuitry  201  may compare the user role, skill and proficiency level of a content consuming entity  115  with the content tags  226  to identify a matching set of content interfaces  225  among the learning interfaces. After identifying the relevant content interfaces  225 , the content analysis circuitry  201  links the matching content interfaces  225  with the content portal  230  associated with the particular user or content consuming entity  115 . 
         [0051]    The assembly of an exemplary content portal  230  by the communication interface  202  will be described with reference to the learning system  100  environment of  FIG. 2 , and the exemplary content portal  230  rendered on display  214  illustrated in  FIG. 3 . The communication interface  202 , under the control of the content analysis circuitry  201 , may retrieve the user characteristic data of a content consuming entity  115  from the entity profile database  107 . The communication interface  202  may generate a content portal  230  based on this characteristic data. With reference to  FIG. 3 , for example, the communication interface  202  may generate and present a content portal  230  for a content consuming entity  115  having a database programmer role with certain skills relating to data analytics, with an overall proficiency level of 6. The communication interface  202  may use this user characteristic data to identify a set of content interfaces  225  to include in a content portal  230 . In determining which content interfaces  225  to include, the content analysis circuitry  201  may access and retrieve the collection of content tags  226  relating to internal content assets  119  and external content assets  109  from the content database  108 . The content analysis circuitry  201  may compare the content tags  226  with the user characteristic data to identify relevant content interfaces  225 . By way of example, the content analysis circuitry  201  may filter the content interfaces  225  down to those relevant for a database programmer, and further filter the results based on the content consuming entity&#39;s  115  known data analytic skills. The content analysis circuitry  201  may then identify those content interfaces relating to data analytics that are appropriate for a user having a proficiency level of 6 or greater, which may identify certain advanced data analytics topics. 
         [0052]    Presenting Multiple Matching Content Interfaces 
         [0053]    As described above, the content analysis circuitry  201 , after identifying the relevant content interfaces  225 , links the matching content interfaces  225  with the content portal  230  to be presented by the communication interface  202  on display  14  for viewing by the content consuming entity  115 . In the linking process the communication interface  202 , under control of the content analysis circuitry  201 , generates and renders a tile or similar likeness of the content interface. With reference to  FIG. 3 , the content interface tiles  301   a - f  may be displayed in a common fashion being similarly dimensioned. The tile may also display information similar to that displayed in the content interface  225  itself. For example, the content interface tiles  301   a - f  may include a thumbnail photograph and content interface identifier. As illustrated in  FIG. 3 , the content interfaces  225  identified may include: High Performance Analytics: Using Big Data, Advanced Data Visualization, Advanced Data Visualization, Hadoop Advanced, Basic D3 Data Visualization, and Advanced D3 Data Visualization. The content interface tiles  301   a - f  may also depict overall interface statistics  303   a - f  and personal interface statistics  302   a - f  similar to the overall interface statistics  404  and personal interface statistics  405  described earlier. Because a comparison of user characteristic data and content tags  226  may match multiple content interfaces  225  to a content consuming entity  115 , the communication interface  202  through the content analysis circuitry  201  may also serve to present the content interface tiles  301   a - f  in order of perceived relevance or importance. For instance, the default ordering of the content interface tiles  301   a - f  may be based on a degree of matching between the comparison of the user characteristic data retrieved from the entity profile database  107  and the content tags  226  of the internal content assets  119  and external content assets  109  contained within a content interface  225 . The content database  108  may include information relating the various learning boards between one another, for example, to form a course on a particular subject. More simply, with reference to  FIG. 3 , Basic D3 Data Visualization may be seen as a pre-requisite to Advanced D3 Data Visualization and the corresponding content interface tiles are placed accordingly. The content interface tiles  301   a - f  may further be adjusted, in part, based on the relative popularity, for instance based on certain overall interface statistics. More specifically with reference to  FIG. 3 , all else being equal, content interface tile  301   b  (Basic PLM Technologies) may be deemed of greater relevance than content interface tile  301   d  (Hadoop Advanced) based on the fact that a greater number of content consuming entities  115  have “completed” the content interface and/or based on the fact that a greater number of content consuming entities  115  are “following” the course. The ordering of content interface tiles  301   a - f  may similarly be affected by the number of content consuming entities  115  who have “endorsed” the content interface  225 . As another example, the ordering of content interface tiles  301   a - f  may be further influenced based on an ongoing dialogue between peers and colleagues relating to the respective content interface  225 . The content analysis circuitry  201  may perform additional analysis on the content of the social dialogue to help in the process, for example, by determining the number of subject matter experts involved in the conversation. 
         [0054]    User Guided Content Filtering 
         [0055]    In another aspect of operation, referring to  FIG. 11 , the learning system  100  may include content filtering circuitry in communication with the communication interface  202 , where the content filtering circuitry may be a sub-component of content analysis circuitry  201 . For instance, the content filtering circuitry may include a memory storing content filtering program instructions configured for execution by the processor to accomplish the filtering functionality described below. The content filtering circuitry through the communication interface  202  may facilitate the presentation of content interfaces  225  having internal content assets  119  and/or external content assets  109  that match filter criteria  1100  identified by the content consuming entity  115 . 
         [0056]    The content analysis circuitry  201  may receive filter criteria  1100  from the content consuming entity  115 , from pre-stored preferences in memory, or in other from other sources. As examples, the filter criteria  1100  may include a preferred media type (e.g., video, podcast, or audio stream), subject, preferred duration, resolution, content (e.g., a preferred presenter, presentation forum, or organization), type of presentation (e.g., a lecture or an interactive program) or any other filter criteria  1100  for the internal content assets  119  and external content assets  109 . The communication interface  202  may receive filter criteria  1100  from the content consuming entity and provide the filter criteria  1100  to the content analysis circuitry  201 . In response the content analysis circuitry  201  may determine which of the matching content interfaces  225  contain internal content assets  119  and/or external content assets  109  that meet the provided filter criteria  1100 . The content filter circuitry may present the filtered matching content interfaces to the content consuming entity  115 , for example, by un-linking those content interfaces  225  that did not match the provided filter criteria from the content portal  230 . 
         [0057]    Maintaining a Content Portal of a User 
         [0058]    In another aspect of operation, referring now to  FIG. 6 , the communication interface  202 , through the content analysis circuitry  201 , may also order the content interface tiles  301   a - j  in a content portal  230  based on certain learning goals attributed with the content consuming entity  115 . As another example, the communication interface  202  may order the content interface tiles  301   a - j  based on a learning plan, which may be optionally adopted by the content consuming entity  115 . The learning plan may represent an independent interest of the user, for example where the content consuming entity  115  is casually interested in learning about how to program video games for his smartphone. The learning plan may, more broadly be intended to present a course like framework for the content consuming entity  115  maintaining an order or progression associated with the content interfaces  225  underlying the content interface tiles  301   a - j . As illustrated in  FIG. 6 , the communication interface  202 , under the control of the content analysis circuitry  201 , may be configured to communicate with a goal database  600  which may include content directives  601  corresponding to a particular content consuming entity  115 . The content directives  601  may, for example, represent a technical development goal or a business development goal set for the content consuming entity  115 . The content analysis circuitry  201  may maintain the content portal  230  by applying the content directives  601  to order the content interface tiles  301   a - j.    
         [0059]    By way of example, the content consuming entity  115  may have the goal of becoming a project manager, which may require competency in particular non-technical skills, such as project lifecycle management or six-sigma optimization techniques. Prior to application of the content directives  601  associated with becoming a project manager, the communication interface  202  may arrange the content interface tiles  301   a - j  based on user characteristic data contained in entity profile database  107 . Assuming that the content consuming entity&#39;s  115  competency is primary technical in nature, the communication interface  202  may present a content portal  230  having technical content interface tiles  301   a - f  being positioned higher up in the arrangement. For example, referring to  FIG. 2 , the communication interface  202  may present a content portal  230  that includes content interface tiles  301   a - f  relating to Java, C++, C#, HTML, SQL, and Hadoop, arranged in that order. However, after application of the content directives  601  by the content analysis circuitry  201 , referring again to  FIG. 6 , the communication interface  202  may adjust the order of the content interface tiles  301   a - j  in the content portal  230  to result in the following order: Six-Sigma, Basic PLM, Advanced PLM, Java, C++ and C#. As noted above, the communication interface  202 , under the control of the content analysis circuitry  201 , may also serve to maintain the content portal  230  by linking or un-linking content interfaces  225  from the content portal  230 . The communication interface  202  un-link an already existing and linked content interface  225  upon a determination by the content analysis circuitry  201  that the content interface  225  no longer constitutes a match for the content consuming entity  115 . Such a situation may occur for example, when a user changes their role or position within the organization. 
         [0060]    The maintenance of the content portal  230  by the communication interface  202  will be described with reference to  FIG. 3  and  FIG. 5 , where  FIG. 5  illustrates the content portal  230  of  FIG. 3  after undergoing maintenance. The communication interface  202  may determine that a change has been made with respect to the user characteristic data stored in entity profile database  107 . The content analysis circuitry  201 , in turn, may obtain a new set of matching content interfaces  225  for the content consuming entity  115 , for example, by comparing the new user characteristic data against the content tags  226  contained in content database  108 . With reference to  FIGS. 3 and 5 , the content consuming entity  115  may originally have been in a managerial position which resulted in a match to a content interface  225  relating to Basic PLM Technologies. The communication interface  202 , accordingly, links the content interface  225  to the content portal  230 , which presents a content interface tile  301   b  to be displayed on display  214 . The content consuming entity  115  may subsequently be promoted to a lead technical role where project lifecycle management skills are no longer needed. Accordingly, the communication interface  202  may un-link the Basic PLM Technologies content interface  225  from the content portal  230 , resulting in the removal of content interface tile  301   b  from display  214 . 
         [0061]    As another example, a user may “complete” a particular content interface  225 , which in turn may result in a linked content interface no longer matching a content consuming entity  115 . The communication interface  202  may then un-link from the content portal  230 . Referring to  FIG. 5 , the content analysis circuitry  201  may determine that the content consuming entity  115  has completed the Basic D3 Data Visualization content interface  225 . As a result, the content analysis circuitry  201  may un-link the content interface  225  from the content portal  230  and remove content interface tile  301   e . Furthermore, the completion of a content interface  225  may itself have an impact on particular aspects of the user characteristic data. For example, after completing Basic D3 Data Visualization, the user&#39;s proficiency level may be adjusted. As described above, the change in user data may result in content interfaces no longer matching a content consuming entity  115 . 
         [0062]    Personalized Content Portals and Content Interface Recommendations 
         [0063]    In another aspect of operation, described with reference to  FIG. 13 , the content analysis circuitry  201  of the system  100  may build a personalized content portal  230 , which may be personalized to a particular content consuming entity  115 . The content analysis circuitry  201  may identify those content interfaces that the content consuming entity  115  is following, and may link the content interfaces to the personalized content portal  230 . In the linking process, the communication interface  202 , under control of the content analysis circuitry  201 , may generate and render a tile, window, or other GUI representation of the content interfaces that the content consuming entity  115  is following. As illustrated in  FIG. 13 , the content interface tiles  1301   a - d  may be displayed with a consistent presentation, e.g., having a common layout. The content interface tiles  1301   a - d  may display a wide array of information, including information regarding the content interface itself, overall interface statistics  1303   a - d , personal interface statistics  1302   a - d , such as that described earlier, or other information. 
         [0064]    The communication interface  202  may also link to recommended content interfaces. In the linking process, the communication interface  202 , under control of the content analysis circuitry  201 , may generate and render a tile, window, or other GUI representation of the recommended content interfaces. The recommended content interfaces may be selected, ordered, and updated as described above, and may look at content tags  226  of the internal content assets  119  and/or external content assets  109 , or content interfaces, to match appropriate content interfaces for the particular user or content consuming entity  115 . The recommended content interfaces  225  may be provided in a recommendation section  1310  in the GUI, for instance. As illustrated in  FIG. 13 , the recommended content interface tiles  1311   a - c  may be displayed with a consistent presentation, e.g., having a common layout. The recommended content interface tiles  1311   a - c  may display a wide array of information, including information regarding the content interface itself, overall content interface statistics, and may include a GUI element to follow the recommended content interface (e.g., a “follow” button  1314   a - c ). The content analysis circuitry  201  may respond to selection of the follow button by designating the content interface for the associated recommended content interface tile as a content interface that the content consuming entity  115  is now following. The recommendation section  1310  may be positioned among the content interfaces that the content consuming entity  115  is already following. As illustrated, for example, the recommendation section  1310  is placed between the third and fourth content interface that the content consuming entity is following. In other implementations, the recommendation section  1310  may be positioned at the top or bottom of the content portal  230 . 
         [0065]    Content Curation and Subject Matter Expert Validation 
         [0066]    Content building circuitry within the system  100  facilitates content curation. The content building circuitry may generate, e.g., a curated content portal  230  that provides a curator input interface into the content database. The curator input interface is configured to receive content interface building directives through the curator input interface. The directives may specify suggested content assets that compose or that are otherwise included on a content interface. Through the curator input interface, the curator may specifically select content that the curator believes is appropriate for any given topic for delivery through any given content interface. 
         [0067]      FIG. 7  provides example logic  700  that the learning system  100  may implement to provide content interfaces. The logic  700  may populate system databases ( 701 ). For instance, the logic  700  may populate the content database  108  with links to internal content assets  119  and external content assets  109  (or with the content assets themselves) that are suggested through the curator input interface, and may also receive from the curator or otherwise obtain or generate content tags  226  for these internal content assets  119  and external content assets  109 . The logic  700  may also populate the entity profile database  107 , including the human resources database  220 . In some instances, the logic  700  may also populate the goal database  600  with content directives  601  or learning plans  602 . 
         [0068]    The logic  700  may also assemble the content interfaces  225  ( 702 ). As described earlier, the content analysis circuitry  201  may generate or assemble a content interface  225  based on information stored in content database  108 . In that regard, the logic  700  may, for example, retrieve relevant association information, interaction information, and social integration information from content database  108 . The content analysis circuitry  201  may then use this information to generate content interfaces  225 , which the communication interface  202  may ultimately present to the content consuming entity  115 . 
         [0069]    The logic  700  may also identify content interfaces that match the content consuming entity  115  ( 703 ). As described earlier, this matching may involve the content analysis circuitry  201  retrieving and comparing user characteristic data from the entity profile database  107  and content tags  226  relating to internal content assets  119  and external content assets  109  from content database  108 . 
         [0070]    The logic  700  may also build the content portal  230  by linking the matching content interfaces  225  ( 704 ). In linking the content interfaces  225 , the content analysis circuitry  201  may generate content interface tiles  301   a - j  corresponding to respective matching content interfaces  225 . The communication interface  202  may then arrange the content interface tiles  301   a - j  in a content portal  230  for presentation to the content consuming entity  115  on display  214 . The initial arrangement of the content interface tiles  301   a - j  may be based on the results of the matching with regards to a corresponding content interface  225 . For example, as described earlier, the content analysis circuitry  201  may determine the degree or quality of the match between the user characteristic data of the content consuming entity  115  with the content tags  226 . 
         [0071]    The logic  700  may re-order the arrangement of the content interface tiles  301   a - j  for presentation to the content consuming entity  115  ( 705 ). This re-ordering may take place, for example, based on certain career goals or based on a learning plan  602  for the content consuming entity  115 . The content analysis circuitry  201  may retrieve content directives  601  or learning plans  602  associated with a content consuming entity  115  that may be stored in the goal database  600  goals. The content analysis circuitry  201  may apply the content directives  601  or learning plan  602  to re-order content interface tiles  301   a - j , which correspond to matching content interfaces  225 . The content analysis circuitry  201 , accordingly, may filter or remove certain content interface tiles  301   a - j  and determine a particular order or hierarchy for other content interface tiles  301   a - j . The communication interface  202  may then adjust the placement of content interface tiles  301   a - j  on content portal  230 , for presentation to the content consuming entity  115  on display  214 . 
         [0072]    The logic  700  may maintain a content portal  230 , including adding and removing certain content interface tiles  301   a - j  based on certain changes in the learning system  100 , for example, based on changes in the user characteristic data contained in entity profile database  107  ( 706 ). As described above, the content analysis circuitry  201  may determine that there has been a change in the user&#39;s role at the organization. In response, the communication interface  202  may un-link one or more content interfaces  225  from a content portal  230 , resulting in the removal of one or more corresponding content interface tiles  301   a - j  from the content portal  230 . The communication interface  202  may also render the content portal  230  on display  214  for a content consuming entity  115 . 
         [0073]    The system  100  may also implement a subject matter expert input interface into the content database. The subject matter expert interface is configured to receive a validation directive from a subject matter expert. When the validation directive is to accept the suggested content asset, then the content building circuitry adds the curator suggested content asset to the available substantive content assets for the content interface. When the validation directive is to reject the curator suggested content asset, then the content building circuitry rejects the suggested content asset without adding the suggested content asset to the available substantive content assets for the content interface. 
         [0074]    In addition, the system  100  may receive a publishing directive from the subject matter expert. Responsive to the publishing directive, the system  100  may then mark the substantive content assets as published or unpublished. The system  100  may then build content interfaces using published content assets. 
         [0075]    In that regard, and with reference to  FIG. 8 , the learning system  100  may include a communication interface  802  and content building circuitry  801 . In one implementation, the content building circuitry  801  of the learning system  100  further comprises a central processing unit (CPU)  810 , a memory  811 , one or more network interfaces  812 , and a data storage  813 . The content building circuitry  801  may also optionally include a display  814  and link scanning circuitry  815 . While the display  814  is illustrated as part of the content building circuitry  801 , the communication interface  802  may, additionally or alternatively, render a content interface  825  on a remote display  814  (not illustrated), for example on a personal computer or mobile device operated by the content consuming entity  115 . The communication interface  202  may provide the ability to remotely view content interfaces  825  over the private intranet  101  or the public Internet  102  using the network interfaces  812  of the content building circuitry  801 . 
         [0076]    In one aspect of operation, the communication interface  802 , under control of the content building circuitry  801 , is configured to communicate with the content database  108 . The content database  108  may contain one or more content interfaces  825  that aggregate available internal content assets  119  and external content assets  109  related to a topic specific area. The content database  108  may also contain content tags  826  that characterize the internal content assets  119  and external content assets  109 . 
         [0077]    As noted above, the content building circuitry  801  may be configured to implement a curator input interface  816  and a subject matter expert input interface  817  in content database  108 . The communication interface  802  may present the curator input interface  816  on display  814 , which may allow the content asset curators  116  to interact with the curator input interface  816  to generate content interface building directives  818 . Similarly, the communication interface  802  may present the subject matter expert input interface  817  on display  814 , which may allow subject matter experts  117  to generate content validation directives  819  and publication directives  820 . 
         [0078]    The communication interface  802 , through the content building circuitry  801 , may for example provide a curator input interface  816  through which a content asset curator  116  may suggest the addition of one or more suggested content assets  901  for a content interface  825 . In one implementation, with reference to  FIG. 9 , the curator input interface  816  may include a suggested content asset location field  902  in which the content asset curator  116  may provide the location of a suggested content asset  901 , for example, the reference location, e.g. a URL, of a video clip on YouTube. The curator input interface  816 , through the content building circuitry  801 , may allow the user to retrieve  903  the suggested content asset  901 , which the curator input interface  816  may then render on display  814 . 
         [0079]    The curator input interface  816  may also include a content interface selector  904 , which may allow the content asset curator  116  to select a content interface  825  with which the suggested content asset  901  is to be associated. The curator input interface  816  may communicate the selection of content interface  825  to the content building circuitry  801 , which may, in turn, generate a content interface preview  905 . The curator input interface  816  may display the content interface preview  905  on display  214  so that the content asset curator  116  may be able to view the content interface  825  and confirm the appropriateness of the suggested content asset  901  for association with the selected content interface  825 . The curator input interface  816  may also provide the content asset curator  116  with the option of submitting a content interface building directive  818  based on the suggested content asset  901  and the selected content interface  825 , which the content building circuitry  801  may store in content database  108 . 
         [0080]    In another implementation, with reference to  FIG. 12 , the curator input interface  816  may be extended to provide an integrated curator input interface  1200  The integrated curator input interface  1200 , may include a content interface details region  1200   a  that allows a curator to set the general details of a content interface  225 , and a content asset region  1200   b  that allows a curator to add new internal content assets  119  or external content assets  109  or modify existing internal content assets  119  or external content assets  109 . The curator input interface  1200  may also provide an integrated search region  1200   c , through which a curator may identify, retrieve, and add internal content assets  119  and external content assets  109  to the content interface  225 , and a navigation bar  1210 . Through the navigation bar  1210  (i.e., via an access interface GUI element  1210   d ) a curator may access existing content interfaces  225  that the curator is managing for modification, and save, publish or preview (i.e., via save, publish or preview GUI elements  1210   a - c ) individual content interfaces  225 . 
         [0081]    The content interface details region  1200   a  may provide the curator with the ability to create a new content interface  225  (i.e., via the add content interface GUI element  1221 ) or modify an existing content interface  225  that the curator has accessed (i.e., via the access interface GUI element  1210   d ). The curator may also view statistics  1222  regarding the content interfaces  225  that the curator is managing, which for example, may include details like the number of internal content assets  119  and external content assets  109  (identified as published or unpublished) within the content interface  225 , the number of endorsements, followers, views, and likes that the content interface  225  has received, and the number of content consuming entities  225  that have completed, or the average level of completion of, the content interface  225 . The curator input interface  1200 , through the content building circuitry  801 , may allow a curator to set the title  1224  and general topic  1225  of the content interface  225 , and provide a description  1226  thereof. The curator input interface  1200  may also allow a curator to select an entity  1227  to which the content interface  225  will be deployed (for tracking and reporting purposes), and adjust  1228  content tags  226  that are associated with the content interface  225 , e.g., for defining skills and roles. The content building circuitry  801  may generate a preview image or allow a curator to select  1223  a preview image for the content interface  225 . A curator may also be able to delegate authority to other curators, who may have similar rights to that of the curator owner, being able to add, edit, or re-arrange internal content assets  119  and external content assets  109  in the content interface  225 , but may be limited in their ability to delete an internal content asset  119  or external content asset  109  or content interface  225 . 
         [0082]    The content asset region  1200   b  may display internal content assets  119  and external content assets  109  that have been added to the content interface  225 , and allow a curator to adjust the order of and manually add  1230  internal content assets  119  and external content assets  109 , for example, by providing a heading, description, preview image, and reference location for the internal content assets  119  and external content assets  109 , along with content tags  226  for the respective internal content assets  119  and external content assets  109 . A curator may also be able to add internal content assets  119  and external content assets  109  using the integrated search region  1200   c , which may integrate search services within the curator input interface  1200 , for example, using public facing APIs (e.g., YouTube, Google, TedTalks) and private facing APIs (e.g., those of the organization). The integrated search region  1200   c  may allow a curator to select a particular search service (via search service GUI elements  1241   a - d ), and may provide a search field  1243  through which a curator may enter search parameters. The content building circuitry  801  may execute the search and return internal content assets  119  and external content assets  109  which may populate the search results region  1245 . The internal content assets  119  and external content assets  109  that are returned may be added to the content interface  225  by curators (and delegates), for example, by dragging and dropping the result from the search results region  1245  to the content asset region  1200   b  of the curator input interface  1200 , whereby the content building circuitry  801  may process the internal content asset  119  or external content asset  109  that is selected from the search results region  1245  and link the content asset to the content interface  225 . 
         [0083]    The communication interface  802 , through the content building circuitry  801 , may also provide a subject matter expert input interface  817  through which a subject matter expert  117  may validate and publish a suggested content asset  901 . In one implementation, with reference to  FIG. 9 , the content building circuitry  801  may retrieve the content interface building directives  818  from content database  108 . The content building circuitry  801  may then generate a list of suggested content assets  901  which the subject matter expert input interface  817  may present to the subject matter expert  117 . The subject matter expert  117  may also be provided with a suggested content asset selector  909 , which the subject matter expert  117  may use to selectively display a suggested content asset  901  from the generated list of suggested content assets  901 . The subject matter expert input interface  817  through the content building circuitry  801  may then retrieve and display the selected suggested content asset  901  for viewing by the subject matter expert  117  on display  214 . The content building circuitry  801  may also generate and display a content interface preview  905  based on the content interface  825  associated with the content interface building directive  818 , which the subject matter expert input interface  817  may present to the subject matter expert  117  for viewing on display  214 . 
         [0084]    Validating Suggested Content Assets 
         [0085]    The subject matter expert input interface  817  may also provide the subject matter expert  117  with the ability to submit a validation directive  819  regarding a suggested content asset  901 , which may reflect the subject matter expert&#39;s  117  assessment of the content asset&#39;s  901  relevance to the content interface. Prior to evaluation of the suggested content asset  901  by the subject matter expert  117 , the suggested content asset  901  may be marked or identified as pending review by the content building circuitry  801 . Once a validation directive  819  has been submitted, the content building circuitry  801  may determine whether the validation directive  819  is intended to accept or reject the suggested content asset  901 , and where the validation directive  819  indicates acceptance, the content building circuitry  801  may add the suggested content asset  901  to a collection of available content assets for the content interface  825  stored in content database  108 . The content building circuitry  801  may add the suggested content assets  901 , for example, by creating links to the content assets in the content interface  825 . Where the validation directive  819  indicates a rejection, the content building circuitry  801  may mark the suggested content asset  901  as being rejected and may forgo adding the suggested content asset  901  to the content database  108 . With respect to suggested content assets  901  that are pending review by subject matter experts  117 , the content building circuitry  801  may include or withhold the suggested content asset  901  from the content database  108 . Following validation, the content building circuitry  801  may adjust attributes of the suggested content asset  901  and no longer identify the suggested content asset  901  as pending. The validation status of a suggested content asset  901 , either pending, validated, or rejected, may be stored in content database  108 . 
         [0086]    Publishing and Unpublishing Content Assets 
         [0087]    In addition to the facilitating the submission of validation directives  819 , the subject matter expert input interface  817  may also provide subject matter experts  117  with the ability to submit a publication directive  820 , which may indicate a desire to add a validated content asset  911  to an associated content interface  825 . The content building circuitry  801 , for example, may determine whether the publication directive  820  is intended to publish or unpublish (e.g., remove from accessibility or view) a validated content asset  911 , and the content building circuitry  801  may mark the validated content asset  911  accordingly. The publication status of a validated content asset  911  may be stored in content database  108 . 
         [0088]    The subject matter expert input interface  817 , under the control of the content building circuitry  801 , may also be configured to maintain a content interface  825 . For example, the content building circuitry  801  may retrieve the set of validated content assets  911 , and determine whether a validated content asset  911  has been marked for publication or unpublication. Based on this determination, the content building circuitry  801  may be further configured to add those validated content assets  911  marked for publication to the respective content interface  825 , while reserving those validated content assets  911  which are marked as unpublished. In instances where a validated content asset  911  has already been published but is marked for unpublication, the content building circuitry  801  may be configured to remove the validated content asset  911  from its respective content interface  825 . In adding validated content assets  911  to the respective content interfaces  825 , the content building circuitry  801  may provide the validated content asset  911  itself or provide the communication interface  802  with a reference to the location of the validated content asset  911 , which may be retrieved from a remote server for rendering. In this way, the communication interface  802  may be seen as a thin layer aggregating various internal content assets  119  and external content assets  109  into a content interface  825 . 
         [0089]    Content Tagging and Predictive Suggestions 
         [0090]    In another aspect of operation, the subject matter expert input interface  817  may provide the subject matter expert  117  with the ability to add additional content tags  226 , which serve to characterize the suggested content asset  901 . For example, the subject matter expert input interface  817  may provide the subject matter expert  117  with a field for entering and submitting tagging inputs  910 . The content building circuitry  801  may interpret these tagging inputs, or tagging directives, and may add a content tag  226  to the content database  108  for a suggested content asset  901 . In some instances, the subject matter expert input interface  817 , through the content building circuitry  801 , may provide additionally functionality with respect the field for entering and submitting tagging inputs  910 . For example, the content building circuitry  801  may be configured to suggest or predict appropriate tags in response to previously entered content tags  226  provided in the field for entering and submitting tagging inputs  910 . The content building circuitry  801  may be configured to allow the subject matter expert input interface  817  to provide this functionality in real time as the subject matter expert  117  enters a tag into the field for entering and submitting tagging inputs  910 . For example, the subject matter expert  117  may want to add the tag “D3 Data Visualization” to a suggested content asset  901 . Immediately after entering the character “3”, the subject matter expert input interface  817 , under control of the content building circuitry  801 , may predict or suggest the entire tag, “D3 Data Visualization”. The content building circuitry  801  may predict or suggest the content tag  226 , for example, based on content tags  226  that have been previously entered. 
         [0091]    Identifying and Suspending Inoperable Links 
         [0092]    In another aspect of operation, the learning system  100  may further comprise a link scanning circuitry  815 , which may serve to identify links to internal content assets  119  and external content assets  109  which are inoperable and which may take certain actions in response. For example, the link scanning circuitry  815  may send a link error message through the curator input interface  802  to a content asset curator  116 , who may be associated with or responsible for the content interfaces  825  having the inoperable link. As illustrated in  FIG. 8 , the link scanning circuitry  815  may be a sub-component of the content building circuitry  801  in communication with communication interface  802 . The link scanning circuitry  815  may be configured to retrieve a set of reference locations associated with one or more internal content assets  119  and external content assets  109  from content database  108 . The reference locations, for example, may be in the form of a link, e.g. a URL, or other location data, which the link scanning circuitry  815  may be configured to follow in order to determine if the underlying content asset is operable. If the link scanning circuitry  815  determines that a link is broken, i.e. where the underlying internal content asset  119  or external content asset  109  is inoperable, the link scanning circuitry  815  may suspend the internal content asset  119  or external content asset  109  from those content interfaces  825  in which it appears. The link scanning circuitry  815  may be further configured to alert a content asset curator  116  as to the deficient link. In some instances, where the internal content asset  119  or external content asset  109  is provided by reference, the link scanning circuitry  815  may be configured to notify the content asset curator  116  based on changes in the underlying internal content asset  119  or external content asset  109 . For example, the link scanning circuitry  815  may determine a change in certain file attributes, e.g. size, date of creation, last modification, and notify the content asset curator  116  that attention may be warranted. Where a link is found inoperable, the link scanning circuitry  815  may affect the suspension of an inoperable content asset without suspending the content interface  825  as a whole, for example, by marking the internal content asset  119  or external content asset  109  for unpublication. As described above, the content building circuitry  801  may be configured to maintain a content interface  825 , which may involve removing a content asset from a content interface  825  upon a determination that the content asset was marked to be unpublished. 
         [0093]    Facilitating Content Curation and Subject Matter Expert Validation 
         [0094]      FIG. 10  provides example logic  1000 , which the learning system  100  may implement to facilitate content curation, e.g., by providing a curator input interface  816  and a subject matter expert input interface  817 . The logic  1000  may initialize the learning system  100 , which may, for example, include populating the content database  108  with available internal content assets  119  and external content assets  109 , content interfaces  825 , content tags  226 , and content building, validating and publishing directives  820  ( 1001 ). The logic  1000  may also generates a curator input interface  816  in content database  108 , which the communication interface  802  provides to the content consuming entity  115  for display on display  214  ( 1002 ). The logic  1000  may also allow the content asset curator  116  to interact with the communication interface  802  to retrieve a suggested content asset  901  and to specify a content interface  825 . The, content asset curator  116  through the curator input interface  816  may submit content interface building directives  818  to be stored in content database  108 , which the content building circuitry  801  may mark as pending for review by a subject matter expert  117  ( 1003 ). 
         [0095]    The logic  1000  may generate a subject matter expert input interface  817 , which may include the list of suggested content assets  901  pending review ( 1004 ). The subject matter expert  117  may preview the suggested content asset  901  along with the corresponding content interface  825 . The logic  1000  may also allow the subject matter expert  117 , through the subject matter expert input interface  817 , to provide a validation directive  819 , validating or rejecting the suggested content asset  901  ( 1005 ). After validation or rejection the suggested content asset  901  may no longer be considered to be pending. In response to a positive validation directive  819 , the validated content asset  911  is added with all other internal content assets  119  and external content assets  109  and is available for curation by the subject matter expert  117 . Once validated, the subject matter expert  117  may also provide content tags regarding the validated content assets  911 . 
         [0096]    The logic  1000  may also permit the subject matter expert  117  to issue a publication directive, marking the validated content asset  911  as being in condition for publication ( 1005 ). The content building circuitry  801  may introduce or add the validated content assets  911  marked to be published to their respective corresponding content interfaces  825 , and may withhold validated content assets  911  that are not marked to be published. The logic  1000  may also maintain content portals  230  by linking the content interfaces  825 , containing the validated content assets  911  marked for publishing, to the content portal  230  ( 1006 ). 
         [0097]    The methods, devices, processing, and logic described above may be implemented in many different ways and in many different combinations of hardware and software. For example, all or parts of the implementations may be circuitry that includes an instruction processor, such as a Central Processing Unit (CPU), microcontroller, or a microprocessor; an Application Specific Integrated Circuit (ASIC), Programmable Logic Device (PLD), or Field Programmable Gate Array (FPGA); or circuitry that includes discrete logic or other circuit components, including analog circuit components, digital circuit components or both; or any combination thereof. The circuitry may include discrete interconnected hardware components and/or may be combined on a single integrated circuit die, distributed among multiple integrated circuit dies, or implemented in a Multiple Chip Module (MCM) of multiple integrated circuit dies in a common package, as examples. 
         [0098]    The circuitry may further include or access instructions for execution by the circuitry. The instructions may be stored in a tangible storage medium that is other than a transitory signal, such as a flash memory, a Random Access Memory (RAM), a Read Only Memory (ROM), an Erasable Programmable Read Only Memory (EPROM); or on a magnetic or optical disc, such as a Compact Disc Read Only Memory (CDROM), Hard Disk Drive (HDD), or other magnetic or optical disk; or in or on another machine-readable medium. A product, such as a computer program product, may include a storage medium and instructions stored in or on the medium, and the instructions when executed by the circuitry in a device may cause the device to implement any of the processing described above or illustrated in the drawings. 
         [0099]    The implementations may be distributed as circuitry among multiple system components, such as among multiple processors and memories, optionally including multiple distributed processing systems. Parameters, databases, and other data structures may be separately stored and managed, may be incorporated into a single memory or database, may be logically and physically organized in many different ways, and may be implemented in many different ways, including as data structures such as linked lists, hash tables, arrays, records, objects, or implicit storage mechanisms. Programs may be parts (e.g., subroutines) of a single program, separate programs, distributed across several memories and processors, or implemented in many different ways, such as in a library, such as a shared library (e.g., a Dynamic Link Library (DLL)). The DLL, for example, may store instructions that perform any of the processing described above or illustrated in the drawings, when executed by the circuitry. 
         [0100]    Various implementations have been specifically described. However, many other implementations are also possible.