Abstract:
Method to perform an operation comprising identifying, in a corpus comprising a plurality of items of content, a subset of the plurality of items of content having a concept matching a concept in a learning environment, wherein each item of content comprises a set of attributes, computing an assistance score for each item of content in the subset based on the set of attributes of the respective item of content in the subset and a set of attributes of a user in the learning environment, and upon determining that a first item of content, of the subset of items of content, has as an assistance score greater than the assistance scores of the other items in the subset, returning the first item of content to the user as a learning supplement for the concept in the learning environment.

Description:
CROSS-REFERENCE TO RELATED APPLICATIONS 
       [0001]    This application is a continuation of co-pending U.S. patent application Ser. No. 14/729,993, filed Jun. 3, 2015. The aforementioned related patent application is herein incorporated by reference in its entirety. 
     
    
     BACKGROUND 
       [0002]    The present invention relates to learning aids on computing devices, and more specifically, to dynamic learning supplementation with intelligent delivery of appropriate content. 
         [0003]    Educational institutions are increasingly embracing students&#39; use of computers both in and out of the classroom, especially the use of tablets and other small-form computing platforms. Moreover, instead of relying exclusively on a controlled set of instructional materials and applications, educators are increasingly utilizing online sources of information. However, additional solutions are needed to take advantage of cognitive style computing in a classroom environment for improved overall education and learning experiences for students. 
       SUMMARY 
       [0004]    Embodiments disclosed herein provide systems, methods, and computer program products to perform an operation comprising identifying, in a corpus comprising a plurality of items of content, a subset of the plurality of items of content having a concept matching a concept in a learning environment, wherein each item of content comprises a set of attributes, computing an assistance score for each item of content in the subset based on the set of attributes of the respective item of content in the subset and a set of attributes of a user in the learning environment, and upon determining that a first item of content, of the subset of items of content, has as an assistance score greater than the assistance scores of the other items in the subset, returning the first item of content to the user as a learning supplement for the concept in the learning environment. 
     
    
     
       BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWINGS 
         [0005]      FIG. 1  illustrates a system which provides dynamic learning supplementation with intelligent delivery of appropriate content, according to one embodiment. 
           [0006]      FIG. 2  illustrates a method to provide dynamic learning supplementation with intelligent delivery of appropriate content, according to one embodiment. 
           [0007]      FIG. 3  illustrates a method to determine a current concept, according to one embodiment. 
           [0008]      FIG. 4  illustrates a method to determine a level of comprehension, according to one embodiment. 
           [0009]      FIG. 5  illustrates a method to identify and return supplemental learning materials, according to one embodiment. 
       
    
    
     DETAILED DESCRIPTION 
       [0010]    Embodiments disclosed herein provide cognitive computing to derive superior benefits from students&#39; access to computing devices, such as laptops, tablets, and the like. More specifically, embodiments disclosed herein deliver a more dynamic, enhanced learning experience through cognitive supplementation and/or lesson augmentation during lectures, learning activities, and extra-curricular engagement. The learning enhancements disclosed herein are individualized to each student, and learns over time how to deliver the most appropriate and valuable enhanced learning experience across all courses of study. Doing so challenges each student regardless of their abilities, and allows each student to achieve beyond what traditional education systems can provide. Generally, embodiments disclosed herein drive students to explore topics at a greater depth in an individualized manner, as students at all levels are challenged to excel further. 
         [0011]    Generally, embodiments disclosed herein monitor the lecture feed (using, for example, speech, images or other visual content, lesson plans, and text analysis) to determine a current learning concept. Embodiments disclosed herein may then identify content that is related to the current learning concept, and deliver the content to the students. This supplemental content may be tailored to the particular learning characteristics of the student (such as whether the student is gifted, a visual learner, etc.). Embodiments disclosed herein also monitor student actions, dynamically formulating questions that engage the student and assess their understanding of the topic. If students need more information to solidify their understanding of the topic, embodiments disclosed herein find the best supplemental content, and present the supplemental content in a form that best suits the student&#39;s learning profile. In addition, embodiments disclosed herein continue to engage the student until the topic is understood, such as providing additional learning content after school, via email, and the like. 
         [0012]    For example, a teacher may be discussing American history during a classroom lecture. Embodiments disclosed herein may listen to the lecture audio in real time to determine when to deliver supplementary content to student computing devices. For example, if embodiments disclosed herein determine that the teacher is covering American history during the time of George Washington, and the teacher mentions the Delaware River. In response, embodiments disclosed herein may display a related image on student computing devices, such as an image of George Washington crossing the Delaware River. 
         [0013]      FIG. 1  illustrates a system  100  which provides dynamic learning supplementation with intelligent delivery of appropriate content, according to one embodiment. The system  100  includes a computer  102  connected to other computers via a network  130 . In general, the network  130  may be a telecommunications network and/or a wide area network (WAN). In a particular embodiment, the network  130  includes access to the Internet. 
         [0014]    The computer  102  generally includes a processor  104  which obtains instructions and data via a bus  120  from a memory  106  and/or storage  108 . The computer  102  may also include one or more network interface devices  118 , input devices  122 , cameras  123 , output devices  124 , and microphone  125  connected to the bus  120 . The computer  102  is generally under the control of an operating system. Examples of operating systems include the UNIX operating system, versions of the Microsoft Windows operating system, and distributions of the Linux operating system. (UNIX is a registered trademark of The Open Group in the United States and other countries. Microsoft and Windows are trademarks of Microsoft Corporation in the United States, other countries, or both. Linux is a registered trademark of Linus Torvalds in the United States, other countries, or both.) More generally, any operating system supporting the functions disclosed herein may be used. The processor  104  is a programmable logic device that performs instruction, logic, and mathematical processing, and may be representative of one or more CPUs. The network interface device  118  may be any type of network communications device allowing the computer  102  to communicate with other computers via the network  130 . 
         [0015]    The storage  108  is representative of hard-disk drives, solid state drives, flash memory devices, optical media and the like. Generally, the storage  108  stores application programs and data for use by the computer  102 . In addition, the memory  106  and the storage  108  may be considered to include memory physically located elsewhere; for example, on another computer coupled to the computer  102  via the bus  120 . 
         [0016]    The input device  122  may be any device for providing input to the computer  102 . For example, a keyboard and/or a mouse may be used. The input device  122  represents a wide variety of input devices, including keyboards, mice, controllers, and so on. The camera  123  may be any image capture device configured to provide image data to the computer  102 . The output device  124  may include monitors, touch screen displays, and so on. The microphone  125  is configured to capture and record audio data. 
         [0017]    As shown, the memory  106  contains a virtual classroom application  111 . The virtual classroom  111  is any application configured to provide a virtual learning environment, such as a chat room or any dedicated suite of online learning tools. The memory  106  also contains a QA application  112 , which is an application generally configured to provide a deep question answering (QA) system. One example of a deep question answering system is Watson, by the IBM Corporation of Armonk, N.Y. A user may submit a case (also referred to as a question) to the QA application  112 . The QA application  112  will then provide an answer to the case based on an analysis of a corpus of information  114 . Although depicted as executing on a single computer, the functionality of the QA application  112  may be provided by grid or cluster of computers (not pictured), and the QA application  112  may serve as a frontend to orchestrate such distributed functionality. 
         [0018]    The QA application  112  is trained to generate responses to cases during a training phase. During the training phase, the QA application  112  is trained to answer cases using an “answer key” which predefines the most correct responses. During training, the QA application  112  ingests content in the corpus  114  to produce one or more machine learning models (not pictured). In addition, during the training phase, the QA application  112  is configured to identify data attributes which are important to answering cases (namely, those attributes having an impact on the confidence score of a given answer). 
         [0019]    After being trained, the QA application  112  may process user cases through a runtime analysis pipeline. In at least one embodiment, the cases include a current lecture or study topic and a user profile, and the candidate answers returned by the QA application  112  correspond to supplemental learning material that can be returned to the user. The analysis pipeline executes a collection of analysis programs to evaluate both the question text and candidate answers (i.e., text passages extracted from documents in a corpus  114 ) in order to construct the most probable correct answer, based on the information extracted from the corpus and from the question. A typical execution pipeline may begin with question analysis, which analyzes and annotates each question presented in the case to identify key topics, concepts, and attributes for conducting a search. The next step of the pipeline may include a primary search, which involves searching for documents in the corpus  114  using the key attributes from the question analysis phase. The next step of the pipeline may identify candidate answers. For example, the QA application  112  may identify key matching passages (based on, for example, topics, concepts, and/or string matching) from the search results with passages in the candidate answers. The QA application  112  may then score each candidate answer. In the next step of the pipeline, the QA application  112  may then retrieve supporting evidence for the candidate answers. The QA application  112  may then complete the pipeline by scoring the various candidate answers considering supporting evidence (if such supporting evidence was processed for the candidate answer, as described herein), from which the most correct answer identified by the QA application  112  may returned to the user. 
         [0020]    The QA application  112  may be configured to provide dynamic learning supplementation with intelligent delivery of appropriate content. Generally, the QA application  112  may determine a current learning topic (or concept, or context) by analyzing sources of input data available in a current learning environment. For example, in classroom, the QA application  112  may convert speech captured by the microphone  125  to text, and analyze the text to identify one or more topics being discussed by the instructor. Similarly, the QA application  112  may analyze text in a virtual classroom  111  to identify concepts being discussed by an instructor. The QA application  112  may also identify text in an image of a classroom blackboard captured by the camera  125 , and analyze the text to determine one or more concepts in the text. Further still, the QA application  112  may analyze documents, applications  151 , web searches, or any other content  152  that a user is interacting with on one of the computing devices to determine the current learning topic. 
         [0021]    The QA application  112  may also determine, for one or more users of the computing devices  150 , the respective user&#39;s level of understanding of the learning topic. The QA application  112  may leverage information about the user in a user profile stored in the profiles  117 , as well as gather real-time information to determine the user&#39;s level of understanding of the topic. For example, the profile  117  may indicate that the user struggles with math and excels at science, providing the QA application  112  with previously acquired data regarding the user. In addition, the QA application  112  may use the camera  123  to capture images of the user&#39;s face to detect facial expressions indicating frustration, confusion, or other emotions indicating a level of understanding of the current learning topic. Further still, the QA application  112  may use the microphone  125  to capture audio of a question the user asks about the topic. The QA application  112  may then analyze the question to determine a level of understanding associated with the question (such as whether the question focuses on a basic concept of the learning topic, or a more advanced concept of the learning topic). 
         [0022]    For example, if a teacher in a classroom is discussing the American Revolution, the QA application  112  may identify keywords about the concept such as “1776,” “Declaration of Independence,” and the like. The QA application  112  may then reference an ontology  116  to determine that the American Revolution is a current topic (or concept) of the lecture. The QA application  112  may then identify, from the corpus  114 , one or more items of content that may serve as learning supplements for the discussion related to the American Revolution. The QA application  112  may focus on items in the corpus  114  having attributes that match attributes of a given user. For example, the QA application  112  may ensure that the content in the corpus  114  is of a reading level that matches the reading level of the respective user. Generally, the QA application  112  may score each identified item of content in the corpus  114  using a machine learning model  115 . The output of the ML model  115  may be a score reflecting a suitability of a given item of content from the corpus  114  relative to a given user. The QA application  112  may then return one or more items of content having the highest suitability score for each user. 
         [0023]    For example, if the profile  117  of student X indicates that student X has a high level of understanding of the American Revolution and an interest in studying law, the QA application  112  may return, user X&#39;s computing device  150 , a copy of the Declaration of Independence. Similarly, student Y&#39;s profile  117  may specify that student Y is a visual learner. The QA application  112 , may analyze student Y&#39;s questions about the American Revolution to determine that student Y is struggling with the core disputes that triggered the Revolution, the QA application  112  may return an image which highlights the main dispute that caused the Revolution, such as taxation without representation, and the like. In addition, the QA application  112  may follow up with the students after presenting supplemental learning material. The QA application  112  may, for example, email the students with additional learning material, quizzes, and the like, to challenge the student to learn more about the subject. The QA application  112  may also monitor the user&#39;s progress in learning or understanding the topic to tailor subsequent learning supplements based on the user&#39;s most current level of understanding of the topic. 
         [0024]    As shown, the storage  108  includes a corpus  114 , machine learning models  115 , ontologies  116 , profiles  117 , schedules  119 , and feedback  121 . The corpus  114  is a body of information used by the QA application  112  to generate answers to questions (also referred to as cases). For example, the corpus  114  may contain scholarly articles, dictionary definitions, encyclopedia references, product descriptions, web pages, and the like. The machine learning (ML) models  115  are models created by the QA application  112  during a training phase, which are used during the execution pipeline to score and rank candidate answers to cases based on features (or attributes) specified during the training phase. For example, a ML model  115  may score supplemental learning content identified in the corpus  114  based on how well the supplemental learning content matches the current learning topic, the user&#39;s level of understanding of the learning topic, the user&#39;s reading level, the user&#39;s preferred method of learning (such as being a visual learner, audio learner, and the like), the format of the supplemental learning content, feedback related to the supplemental learning content stored in the feedback  121 , and the like. 
         [0025]    The ontologies  116  include one or more ontologies providing a structural framework for organizing information. An ontology formally represents knowledge as a set of concepts within a domain, and the relationships between those concepts. Profiles  117  include information related to different users. The user profiles in the profiles  117  may include any information about the users, including biographical information, education level, profession, reading level, preferred learning techniques, levels of understanding of a plurality of learning subjects, and the like. The schedules  119  may include data specifying lesson plans, lecture topics, business agendas, and the like. For example, a teacher may create a day&#39;s lesson plan that specifies which topics will be taught at which times during the day (such as Greek mythology being taught from 9:00-10:00 AM). In at least one embodiment, the QA application  112  may leverage the schedules  119  when determining the current context (or topic of discussion). The QA application  112  may also ingest a schedule  119  prior to a lecture to provide teachers suggested content for inclusion or exclusion from the lecture. Similarly, the QA application  112  may use the schedules  119  to dynamically generate content for students to review prior to a lecture. The feedback  121  includes feedback from different users related to content in the corpus  114  returned as supplemental learning content. For example, students and teachers may provide feedback indicate whether a video about the Pythagorean Theorem was an effective learning supplement. Doing so may allow the QA application  112  to determine whether or not to provide the video as a learning supplement to other students in the future. 
         [0026]    As shown, the networked system  100  includes a plurality of computing devices  150 . The computing devices  150  may be any type of computing device, including, without limitation, laptop computers, desktop computers, tablet computers, smartphones, portable media players, portable gaming devices, and the like. As shown, the computing devices  150  include an instance of the QA application  112 , applications  151 , content  152  on the computing devices  150 . The applications  151  may include any application or service, such as word processors, web browsers, e-reading applications, video games, productivity software, business software, educational software, and the like. The content  152  may be any locally stored content, such as documents, media files, and the like. The instance of the QA application  112  executing on the computing devices  150  may interface with the instance of the QA application  112  executing on the computer  102  to provide supplemental learning content (from the corpus  114 , the servers  160 , or any other source) to users of the computing devices  150 . 
         [0027]    As shown, remote servers  160  provide services  161  and content  162  to the computing devices  150 . The services  161  may include any computing service, such as search engines, online applications, and the like. The content  162  may be any content, such as web pages (e.g., an online encyclopedia), media, and the like. The QA application  112  may provide services from the services  161  and/or content  162  to the users computing devices  150  as learning supplements. 
         [0028]      FIG. 2  illustrates a method  200  to provide dynamic learning supplementation with intelligent delivery of appropriate content, according to one embodiment. Generally, the QA application  112  may execute the steps of the method  200  to provide cognitive supplementation and lesson augmentation during, for example, classroom lectures, learning activities, and after-school engagement. The QA application  112  may individualize the supplemental content to each student while learning over time how to deliver the most appropriate and valuable enhanced learning experience in specific classroom. 
         [0029]    As shown, the method  200  begins at step  210 , where a machine learning (ML) model is created and stored in the ML models  115  during a training phase of the QA application  112 . The ML model may specify different attributes, or features, that are relevant in scoring a piece of content from the corpus  114  as being a suitable learning supplement for a given user (or users). For example, the features may include reading levels (of users and content), levels of sophistication of content in the corpus  114 , a format of the content, an instruction type of each item of content, feedback reflecting a level of effectiveness of each an item of content, a learning classification of the user, a preferred learning format of the user, a preferred instruction type of the user, and the like. 
         [0030]    At step  220 , the QA application  112  may be deployed on a computing system in a learning environment. For example, the QA application  112  may be deployed on the computer  102  serving as a central controller in a classroom where students use a computing device  150  executing the QA application  112 . At step  230 , described in greater detail with reference to  FIG. 3 , the QA application  112  may determine the current learning concepts (or topics). For example, during a classroom lecture, the QA application  112  may identify an image of an integral presented to students and analyze the instructor&#39;s speech to determine that the current topic is calculus. At step  240 , described in greater detail with reference to  FIG. 4 , the QA application  112  may determine, for one or more users, a respective level of comprehension (or understanding) of the current learning topic. For example, the QA application  112  may determine from the profiles  117  that a student who consistently receives A&#39;s in mathematics courses has a high level of comprehension of calculus. Similarly, a different student profile  117  may indicate that another student consistently receives D&#39;s in mathematics courses may have a low level of comprehension (or understanding) of calculus. 
         [0031]    At step  250 , described in greater detail with reference to  FIG. 5 , the QA application  112  may identify content from the corpus  114  and return the content to the user as a learning supplement. Generally, the QA application  112  may be configured to receive the current learning topic and the user&#39;s profile  117  as the “case” or “question.” The QA application  112  may then identify content in the corpus  114  matching the topic (and or one or more high-level filters based on the profile). The QA application  112  may then score the identified content using the ML model  115 . The output of the ML model  115  may be a score for each item of content, reflecting a level of suitability for the content relative to the user&#39;s attributes. The QA application  112  may then return the item of content from the corpus  114  having the highest score to the user as a learning supplement. For example, the QA application  112  may return an animated graphic of what an integral is to a visual learner struggling with integrals, while returning an audio book on triple integrals to an advanced mathematics student who is comfortable with single integrals and is an audio learner. At step  260 , the QA application  112  may continue to monitor user comprehension of the learning topic. At step  270 , the QA application  112  may provide additional supplemental learning content at predefined times (such as during a lecture, after a lecture, at nights, on weekends, and the like). For example, the QA application  112  may send a dynamically generated set of additional learning content at the end of a lecture to the user via email. As another example, the QA application  112  may re-engage lost, bored, or struggling students by providing supplemental learning content during a lecture, which may cause the student to actively participate in the lecture. 
         [0032]      FIG. 3  illustrates a method  300  corresponding to step  230  to determine a current learning concept, according to one embodiment. In at least one embodiment, the QA application  112  performs the steps of the method  300 . The method  300  begins at step  310 , where the QA application  112  may optionally identify concepts specified in a predefined schedule of concepts in the schedules  119 . For example, a teacher may specify daily schedules indicating which subjects will be taught at what times. The QA application  112  may use these schedules to supplement natural language processing performed on any captured text, speech, images, and the like. At step  320 , the QA application  112  may convert speech captured by the microphone  125  to text. At step  330 , the QA application  112  may identify concepts in text. The text may be the output of the converted speech at step  320 , or may be text captured by the QA application  112  from different sources, such as the virtual classroom application  111 . At step  340 , the QA application  112  may identify concepts in image data. The image data may include images and/or text that the QA application  112  may analyze to identify concepts. At step  350 , the QA application  112  may identify a concept based on content accessed by a user on their respective computing device  150 . For example, the QA application  112  may identify open applications  151 , web searches, and the like. The QA application  112  may also leverage this information to determine a user&#39;s level of engagement with a current lecture. At step  360 , the QA application  112  may determine the current learning concept based on the concepts identified at step  310 - 350 . Therefore, for example, if the QA application  112  determines that a lesson plan in the schedules  119  indicates a geometry lesson is scheduled for 2:00-3:00 PM, that the instructor is talking about the angles of a triangle, and identifies triangles and other geometric objects drawn on a blackboard, the QA application  112  may determine that geometry is the current subject (or concept). Doing so may allow the QA application  112  to return geometry related supplemental learning content to the computing devices  150 . The QA application  112  may perform the steps of the method  300  continuously, or according to a predefined timing schedule to ensure that the most current learning concept is detected. 
         [0033]      FIG. 4  illustrates a method  400  corresponding to step  240  to determine a level of comprehension, according to one embodiment. Generally, the QA application  112  may perform the steps of the method  400  to determine the user&#39;s level of understanding of (or comprehension, familiarity, comfort, etc) a given learning topic. The QA application  112  may perform the steps of the method  400  for any number of users. As shown, the method  400  begins at step  410 , where the QA application  112  may analyze user data in the profiles  117 . The profiles may specify learning strengths, weaknesses, preferences, and the like. At step  420 , the QA application  112  may ask the user questions to gauge their level of understanding. The QA application  112  may leverage the number of correct or incorrect answers the user provides to determine the user&#39;s level of understanding of a given topic. At step  430 , the QA application  112  may analyze one or more of user actions, statements, expressions, or focus. For example, the QA application  112  may identify questions, facial expressions, gestures, or statements indicating frustration or lack of understanding during a lecture. Similarly, if a student asks advanced questions during an introductory lecture on a topic, the QA application  112  may determine that the user has a level of understanding that exceeds the introductory material. At step  440 , the QA application  112  may determine the user&#39;s level of understanding based on the determinations made at steps  410 - 430 . The QA application  112  may also update the user&#39;s profile in the profiles  117  to reflect the most current level of understanding of the current learning topic. 
         [0034]      FIG. 5  illustrates a method  500  corresponding to step  250  to identify and return supplemental learning materials, according to one embodiment. The method  500  begins at step  510 , where the QA application  112  receives the current learning concept and user information (such as data from the user&#39;s profile  117  and information about the user&#39;s level of understanding of the current learning concept determined via the method  400 ). At step  520 , the QA application  112  may search the corpus  114  to identify items of content including the current learning concept. The QA application  112  may reference concept annotations of items of content the corpus  114 , or may perform natural language processing on the content to determine whether the content includes a matching concept. For example, if the current lecture concept is P orbitals in chemistry, the QA application  112  may identify articles, videos, and images in the corpus  114  which discuss P orbitals of atoms. At step  530 , the QA application  112  may execute a loop including step  540  for each item of content identified at step  520 . At step  540 , the QA application  112  may apply a machine learning (ML) model from the ML models  115  to compute a score for the current item of content. The score may be a suitability score reflecting how suitable the content would serve as a learning tool for the current user. The ML model may compute the score based on how well the attributes of the user match the attributes of the content, as well as feedback from the feedback  120  related to the item of content. For example, if the user is an expert in psychology, and the current item of content is a part of an introductory lesson in psychology, the ML model would output a score indicating a low suitability level for the expert. As another example, if feedback from users and teachers in the feedback  121  indicates that a video on algebra is beneficial for users struggling with algebra, and the current student is determined to be struggling with algebra, the ML model may output a score reflecting a high level of suitability to return the video on algebra to the student as a learning tool. At step  550 , the QA application  112  determines whether more items of content remain. If more items of content remain, the QA application  112  returns to step  530 . If no more items of content remain, the QA application  112  proceeds to  560 , where the QA application  112  may return the item of content having the highest score as a learning supplement. 
         [0035]    Advantageously, embodiments disclosed herein dynamically return supplemental learning content to all types of users. Embodiments disclosed herein may monitor a current learning environment (such as a physical classroom, virtual classroom, or a user&#39;s computer) to determine a current learning concept. Doing so may allow embodiments disclosed herein to identify related topics for which content can be returned to the student as a supplemental learning tool. Embodiments disclosed herein monitor user actions, dynamically formulating questions that quickly assess the user&#39;s understanding of the learning topic. If the user needs more information to solidify their understanding, embodiments disclosed herein find the best content to do so, and return the content that is in a format that best suits the student&#39;s learning profile (such as visual items for visual learners). Embodiments disclosed herein may return the supplemental content immediately, or postponed and sent at a later time via email or some other mechanism). In addition, embodiments disclosed herein may continue to engage users, even outside of the classroom, until the user understands the topic. For example, embodiments disclosed herein may prompt the student to set aside a time to engage in further supplemental learning. Further still, embodiments disclosed herein determine the user&#39;s state—such as whether the user is interested or confused, to ensure that students remain engaged. 
         [0036]    The descriptions of the various embodiments of the present invention have been presented for purposes of illustration, but are not intended to be exhaustive or limited to the embodiments disclosed. Many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the described embodiments. The terminology used herein was chosen to best explain the principles of the embodiments, the practical application or technical improvement over technologies found in the marketplace, or to enable others of ordinary skill in the art to understand the embodiments disclosed herein. 
         [0037]    In the foregoing, reference is made to embodiments presented in this disclosure. However, the scope of the present disclosure is not limited to specific described embodiments. Instead, any combination of the recited features and elements, whether related to different embodiments or not, is contemplated to implement and practice contemplated embodiments. Furthermore, although embodiments disclosed herein may achieve advantages over other possible solutions or over the prior art, whether or not a particular advantage is achieved by a given embodiment is not limiting of the scope of the present disclosure. Thus, the recited aspects, features, embodiments and advantages are merely illustrative and are not considered elements or limitations of the appended claims except where explicitly recited in a claim(s). Likewise, reference to “the invention” shall not be construed as a generalization of any inventive subject matter disclosed herein and shall not be considered to be an element or limitation of the appended claims except where explicitly recited in a claim(s). 
         [0038]    Aspects of the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment (including firmware, resident software, micro-code, etc.) or an embodiment combining software and hardware aspects that may all generally be referred to herein as a “circuit,” “module” or “system.” 
         [0039]    The present invention may be a system, a method, and/or a computer program product. The computer program product may include a computer readable storage medium (or media) having computer readable program instructions thereon for causing a processor to carry out aspects of the present invention. 
         [0040]    The computer readable storage medium can be a tangible device that can retain and store instructions for use by an instruction execution device. The computer readable storage medium may be, for example, but is not limited to, an electronic storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination of the foregoing. A non-exhaustive list of more specific examples of the computer readable storage medium includes the following: a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), a static random access memory (SRAM), a portable compact disc read-only memory (CD-ROM), a digital versatile disk (DVD), a memory stick, a floppy disk, a mechanically encoded device such as punch-cards or raised structures in a groove having instructions recorded thereon, and any suitable combination of the foregoing. A computer readable storage medium, as used herein, is not to be construed as being transitory signals per se, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through a waveguide or other transmission media (e.g., light pulses passing through a fiber-optic cable), or electrical signals transmitted through a wire. 
         [0041]    Computer readable program instructions described herein can be downloaded to respective computing/processing devices from a computer readable storage medium or to an external computer or external storage device via a network, for example, the Internet, a local area network, a wide area network and/or a wireless network. The network may comprise copper transmission cables, optical transmission fibers, wireless transmission, routers, firewalls, switches, gateway computers and/or edge servers. A network adapter card or network interface in each computing/processing device receives computer readable program instructions from the network and forwards the computer readable program instructions for storage in a computer readable storage medium within the respective computing/processing device. 
         [0042]    Computer readable program instructions for carrying out operations of the present invention may be assembler instructions, instruction-set-architecture (ISA) instructions, machine instructions, machine dependent instructions, microcode, firmware instructions, state-setting data, or either source code or object code written in any combination of one or more programming languages, including an object oriented programming language such as Smalltalk, C++ or the like, and conventional procedural programming languages, such as the “C” programming language or similar programming languages. The computer readable program instructions may execute entirely on the user&#39;s computer, partly on the user&#39;s computer, as a stand-alone software package, partly on the user&#39;s computer and partly on a remote computer or entirely on the remote computer or server. In the latter scenario, the remote computer may be connected to the user&#39;s computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet Service Provider). In some embodiments, electronic circuitry including, for example, programmable logic circuitry, field-programmable gate arrays (FPGA), or programmable logic arrays (PLA) may execute the computer readable program instructions by utilizing state information of the computer readable program instructions to personalize the electronic circuitry, in order to perform aspects of the present invention. 
         [0043]    Aspects of the present invention are described herein with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and/or block diagrams, and combinations of blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer readable program instructions. 
         [0044]    These computer readable program instructions may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions/acts specified in the flowchart and/or block diagram block or blocks. These computer readable program instructions may also be stored in a computer readable storage medium that can direct a computer, a programmable data processing apparatus, and/or other devices to function in a particular manner, such that the computer readable storage medium having instructions stored therein comprises an article of manufacture including instructions which implement aspects of the function/act specified in the flowchart and/or block diagram block or blocks. 
         [0045]    The computer readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other device to cause a series of operational steps to be performed on the computer, other programmable apparatus or other device to produce a computer implemented process, such that the instructions which execute on the computer, other programmable apparatus, or other device implement the functions/acts specified in the flowchart and/or block diagram block or blocks. 
         [0046]    The flowchart and block diagrams in the Figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of the present invention. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of instructions, which comprises one or more executable instructions for implementing the specified logical function(s). In some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems that perform the specified functions or acts or carry out combinations of special purpose hardware and computer instructions. 
         [0047]    Embodiments of the invention may be provided to end users through a cloud computing infrastructure. Cloud computing generally refers to the provision of scalable computing resources as a service over a network. More formally, cloud computing may be defined as a computing capability that provides an abstraction between the computing resource and its underlying technical architecture (e.g., servers, storage, networks), enabling convenient, on-demand network access to a shared pool of configurable computing resources that can be rapidly provisioned and released with minimal management effort or service provider interaction. Thus, cloud computing allows a user to access virtual computing resources (e.g., storage, data, applications, and even complete virtualized computing systems) in “the cloud,” without regard for the underlying physical systems (or locations of those systems) used to provide the computing resources. 
         [0048]    Typically, cloud computing resources are provided to a user on a pay-per-use basis, where users are charged only for the computing resources actually used (e.g. an amount of storage space consumed by a user or a number of virtualized systems instantiated by the user). A user can access any of the resources that reside in the cloud at any time, and from anywhere across the Internet. In context of the present invention, a user may access applications or related data available in the cloud. For example, the QA application  112  could execute on a computing system in the cloud and dynamically identify individualized learning content for users. In such a case, the QA application  112  could store the identified learning content at a storage location in the cloud. Doing so allows a user to access this information from any computing system attached to a network connected to the cloud (e.g., the Internet). 
         [0049]    While the foregoing is directed to embodiments of the present invention, other and further embodiments of the invention may be devised without departing from the basic scope thereof, and the scope thereof is determined by the claims that follow.