Abstract: Examples associated with reading difficulty level based resource recommendation are disclosed. One example may involve instructions stored on a computer readable medium. The instructions, when executed on a computer, may cause the computer to obtain a set of candidate resources related to a source document. The candidate resources may be obtained based on content extracted from the source document. The instructions may also cause the computer to identify reading difficulty levels of members of the set of candidate resources. The instructions may also cause the computer to recommend a selected candidate resource to a user. The selected candidate resource may be recommended based on subject matter similarity between the selected candidate resource and the source document. The selected candidate resource may also be recommended based on reading difficulty level similarity between the selected candidate resource and the source document.

Abstract: A learning graph is generated for documents according to a sequencing approach. The learning graph includes nodes corresponding to the documents and edges. Each edge connects two of the nodes and indicates a sequencing relationship between two of the documents to which the two of the nodes correspond that specifies an order in which the two of the documents are to be reviewed in satisfaction of the learning goal. The learning graph is a directed graph specifying a learning path through the documents to achieve a learning goal in relation to a subject.

Abstract: Examples relate to grouping students using content fields. Student data including a plurality of content fields is obtained. Each content field of the plurality of content fields includes a value that represents an unstructured marking linked to a content data collection. Student profiles are generated by assigning a student identification number to each of the plurality of content fields. Each of the student identification numbers are organized into at least one student group by analyzing the set of student profiles.

Abstract: Matching of an input document to documents in a document collection is described herein. In an example, a similarity correspondence between an input document and one or more documents in a base document collection is established. A set of base document segments and a set of message types associated to document segments in the set of base document segments is provided. The set of base document segments is derived from documents in the base document collection. The input document is segmented into input document segments corresponding to message types. Segment similarity between input document segments and base document segments corresponding to the same message types is computed. The similarity correspondence between the input document and at least one document in the base document collection is based on the computed segment similarity.

Abstract: Examples relate to citation explanations. A process to provide citation explanation is provided herein. The process analyzes a primary document to extract a citation claim. The process generates a set of candidate segments of a cited document that may correspond to the citation claim. The process also analyzes the set of candidate segments.

Abstract: Automated content selection is disclosed. An example method includes generating a plurality of rankings for each document in a set of input documents, each ranking based on separate interesting document properties. The method also includes selecting a subset of the set of input documents, wherein each document selected for the subset is based on rankings of the selected document. The method also includes determining interesting properties of the subset. The method also includes selecting a subset with respect to parameters being optimized. The method also includes outputting a composition including the documents in the subset.

Abstract: Systems and methods associated with external resource identification are disclosed. One example method may be embodied on a non-transitory computer-readable medium storing computer-executable instructions. The instructions, when executed by a computer may cause the computer to perform the method. The method includes classifying a segment of a document into a member of a set of topics discussed within the document. The method also includes identifying, based on the structure of the segment and keywords from the segment, information that a reader of the document could seek upon reading the segment. The method also includes obtaining, based on the member of the set of topics, a set of candidate external resources that potentially contain the information. The method also includes presenting, in response to a user interaction with the document, a member of the set of candidate external resources identified as being likely to contain the information.

Abstract: Examples associated with keyframe annotation are disclosed. One example includes extracting a set of keyframes from a video presentation. A subset of the keyframes is selected to present to a user based on a user preference. Annotations are generated for the subset of the keyframes. The annotations are personalized to the user. The subset of the keyframes and the annotations are presented to the user.

Abstract: Embodiments of the present invention relate to a new method of entity integration using high-level scripting languages. In one embodiment, a method of and computer product for entity integration is provided. An entity declaration is read from a machine readable medium. The entity declaration describes an entity including at least one nested entity. An index declaration is read from a machine readable medium. The index declaration describes an index of nested entities. An entity population rule is read from a machine readable medium. The entity population rule describes a mapping from an input schema to an output schema. The output schema conforms to the entity declaration. A plurality of input records is read from a first data store. The input records conform to the input schema. The entity population rule applies to the plurality of records to create a plurality of output records complying with the output schema. An index of nested entities is populated. The index complies with the index declaration.

Abstract: Examples associated with keyframe annotation are disclosed. One example includes extracting a set of keyframes from a video presentation. A subset of the keyframes is selected to present to a user based on a user preference. Annotations are generated for the subset of the keyframes. The annotations are personalized to the user. The subset of the keyframes and the annotations are presented to the user.

Abstract: Examples disclosed herein relate to determine attention span style time correlation. In one implementation, a processor determines, based on information related to a users navigation of digital material and stored classifier information, time correlation information related to the users attention span style.

Abstract: Example implementations disclosed herein can be used to generate student user-specific customized hybrid educational documents. Such implementations include systems, methods, and devices for determining an attention span profile associated with a particular student user, and generating a custom educational document in response to the attention span profile. Student user experience feedback and test results determined from the use of the customized educational document can be assessed to update the student user's attention span profile. The updated student user attention span can then be used to update the customized educational document.

Abstract: A method is described in which a topic similarity score, a product similarity score and an operating system similarity score between an original post and each one of a plurality of previous posts are determined; an overall similarity score of the each one of the plurality of previous posts based on the topic similarity score, the product similarity score and the operating system similarity score is determined; and a recommendation of a top K number of the plurality of previous posts based on the overall similarity score of the each one of the plurality of previous posts is sent to a display device.

Abstract: Disclosed herein are a system, non-transitory computer readable medium and method for fulfilling requests for source code. A description is associated with each section of source code text. A section of source code, whose description at least partially matches a source code request, is obtained and displayed.

Abstract: Examples relate to citation explanations. A process to provide citation explanation is provided herein. The process analyzes a primary document to extract a citation claim. The process generates a set of candidate segments of a cited document that may correspond to the citation claim. The process also analyzes the set of candidate segments.

Abstract: Personalized learning based on functional summarization is disclosed. One example is a system including a content processor, a plurality of summarization engines, at least one meta-algorithmic pattern, an evaluator, and a selector. The content processor provides course material to be learned, the course material selected from a corpus of educational content, and identifies retained material indicative of a portion of the course material retained by user. Each of the plurality of summarization engines provides a differential summary indicative of differences between the course material and the retained material. The at least one meta-algorithmic pattern is applied to at least two differential summaries to provide a meta-summary using the at least two differential summaries. The evaluator determines a value of each differential summary and meta-summary.

Abstract: Systems and methods associated with external resource identification are disclosed. One example method may be embodied on a non-transitory computer-readable medium storing computer-executable instructions. The instructions, when executed by a computer may cause the computer to perform the method. The method includes classifying a segment of a document into a member of a set of topics discussed within the document. The method also includes identifying, based on the structure of the segment and keywords from the segment, information that a reader of the document could seek upon reading the segment. The method also includes obtaining, based on the member of the set of topics, a set of candidate external resources that potentially contain the information. The method also includes presenting, in response to a user interaction with the document, a member of the set of candidate external resources identified as being likely to contain the information.

Abstract: Examples relate to grouping students using content fields. Student data including a plurality of content fields is obtained. Each content field of the plurality of content fields includes a value that represents an unstructured marking linked to a content data collection. Student profiles are generated by assigning a student identification number to each of the plurality of content fields. Each of the student identification numbers are organized into at least one student group by analyzing the set of student profiles.

Abstract: Examples disclosed herein relate to creating a heterogeneous learner group. In one implementation, a processor associates a selected learner with a group of learners based on a value of a factor associated with the group of learners compared to a value of the factor associated with the selected learner. For example, the value associated with the selected learner may be indicative of a strength compared to the value associated with the group of learners. The processor may output information related to a heterogeneous learner group created from the group of learners and the selected learner.

Abstract: Examples disclosed herein relate to associating a learner and learning content. A processor determines a learning type cluster based on clustering of learning content attributes and learner attributes based on historical pairings of content and learners and information about outcomes of the pairings. The processor may associate a piece of learning content and a learner based on the learning type clusters and output information about the association.