Systems and methods for calculating text difficulty

A system comprising:

• a database comprising non-transitory machine-readable storage media configured to store a corpus comprising one or more resources, and metadata associated with each of the one or more resources in the corpus;
• a learner database comprising a non-transitory machine-readable storage media configured to store one or more learner profiles;
• and
• a computer comprising a processor and a non-transitory machine-readable storage containing a text difficulty calculator module executed by the processor, wherein the computer is configured to:
• identify in text of a resource of the corpus a set of lexical features according to a lexical feature bank file, the set of lexical features including one or more semantic features and one or more syntactic features;
• store, into the database storing the resource of the corpus, metadata associated with each resource, the metadata containing a value associated with each of the identified semantic and syntactic features in the text according to the bank file, and metadata containing a statistical model associated with one or more resources of the corpus;
• determine a text difficulty score associated with the resource using each of the values assigned to the identified semantic and syntactic features;
• determine a text difficulty score of a new resource containing one or more lexical features, the text difficulty score of the new resource based upon the statistical model associated with the one or more resources of the corpus and the one or more lexical features of the new resource;
• output the text difficulty score to a computing device of a content curator;
• and
• adjust the text difficulty score according to an input received from the computing device of the content curator.

A computer-implemented method comprising:

• extracting, by a computer, one or more linguistic features having a weighted value from a plurality of training resources containing text, wherein each linguistic feature of the text of each training resource is associated with a metadata label indicating the weighted value for each respective feature and containing a text difficulty score of the text;
• determining, by the computer, a vector value associated with each training resource based on each of the weighted values of each of the extracted one or more linguistic features;
• generating, by the computer, from metadata associated with each of the training resources stored in a database, a statistical model using the vector values associated with each training resource, wherein the statistical model represents a correlation between a set of features selected for extraction, a set of weighted values assigned to the set of features selected for extraction, and a set of text difficulty scores associated with the training resources;
• and
• in response to the computer receiving from a second computer a new resource to include in the plurality of training resources stored in the database:
• extracting, by the computer, one or more linguistic features having a weighted value from the new resource;
• determining, by the computer, a vector value for the new resource based upon the set of extracted linguistic features;
• and
• generating, by the computer, metadata containing a text difficulty score for the new resource based upon the vector value for the new resource and the statistical model.