Learning systems and methods of the present disclosure include generating a text document based on a digital file, tokenizing the text document, generating a semantic model based on the tokenized text document using an unsupervised machine learning algorithm, assigning a plurality of passage scores to a corresponding plurality of passages of the tokenized text document, selecting one or more candidate knowledge items from the tokenized text document based on the plurality of passage scores, filtering the one or more candidate knowledge items based on user data, generating one or more structured learning assets based on the one or more filtered candidate knowledge items, generating an interaction based at least on the one or more structured learning assets, and transmitting the interaction to a user device. Each passage score is assigned based on a relationship between a corresponding passage and the semantic model.

A platform is described for analyzing assessment data, including correlating assessment data with learning outcome data, and presenting analysis results through one or more interactive dashboards. A course syllabus and/or other course described information is analyzed to identify expected target outcomes to be achieved by students of the course, and to identify a relative weight of each of the various target outcomes based on their coverage in the syllabus. Assessment data describing a digitally administered assessment is analyzed to identify the categories that are assessed in the various questions of the assessment. The assessment data, including scores on the assessment, can be presented in an interactive dashboard to indicate how well students are performing on assessments with respect to particular learning outcomes, determine question quality, and present other information regarding assessments and/or curriculum effectiveness.

A platform is described for analyzing assessment data, including correlating assessment data with learning outcome data, and presenting analysis results through one or more interactive dashboards. A course syllabus and/or other course described information is analyzed to identify expected target outcomes to be achieved by students of the course, and to identify a relative weight of each of the various target outcomes based on their coverage in the syllabus. Assessment data describing a digitally administered assessment is analyzed to identify the categories that are assessed in the various questions of the assessment. The assessment data, including scores on the assessment, can be presented in an interactive dashboard to indicate how well students are performing on assessments with respect to particular learning outcomes, determine question quality, and present other information regarding assessments and/or curriculum effectiveness.

A platform is described for analyzing assessment data, including correlating assessment data with learning outcome data, and presenting analysis results through one or more interactive dashboards. A course syllabus and/or other course described information is analyzed to identify expected target outcomes to be achieved by students of the course, and to identify a relative weight of each of the various target outcomes based on their coverage in the syllabus. Assessment data describing a digitally administered assessment is analyzed to identify the categories that are assessed in the various questions of the test, and determine student performance with respect to particular categories. The platform provides interactive dashboards to present analysis results and provide integration with a continuous quality improvement (CQI) system for task tracking related to the analysis results.

A platform is described for analyzing assessment data, including correlating assessment data with learning outcome data, and presenting analysis results through one or more interactive dashboards. A course syllabus and/or other course described information is analyzed to identify expected target outcomes to be achieved by students of the course, and to identify a relative weight of each of the various target outcomes based on their coverage in the syllabus. Assessment data describing a digitally administered assessment is analyzed to identify the categories that are assessed in the various questions of the test, and determine student performance with respect to particular categories. The platform provides interactive dashboards to present analysis results and provide integration with a continuous quality improvement (CQI) system for task tracking related to the analysis results.

A platform is described for analyzing assessment data, including correlating assessment data with learning outcome data, and presenting analysis results through one or more interactive dashboards. A course syllabus and/or other course described information is analyzed to identify expected target outcomes to be achieved by students of the course, and to identify a relative weight of each of the various target outcomes based on their coverage in the syllabus. Assessment data describing a digitally administered assessment is analyzed to identify the categories that are assessed in the various questions of the test. The target outcomes are compared to the assessed categories to determine the extent to which the assessment is assessing each of the target outcomes, the degree of correspondence between the distribution of assessed categories and the distribution of target outcomes, and whether any gaps are present indicating that certain target outcomes are going unassessed or are insufficiently assessed.

A platform is described for analyzing assessment data, including correlating assessment data with learning outcome data, and presenting analysis results through one or more interactive dashboards. A course syllabus and/or other course described information is analyzed to identify expected target outcomes to be achieved by students of the course, and to identify a relative weight of each of the various target outcomes based on their coverage in the syllabus. Assessment data describing a digitally administered assessment is analyzed to identify the categories that are assessed in the various questions of the test. The target outcomes are compared to the assessed categories to determine the extent to which the assessment is assessing each of the target outcomes, the degree of correspondence between the distribution of assessed categories and the distribution of target outcomes, and whether any gaps are present indicating that certain target outcomes are going unassessed or are insufficiently assessed.

Improving performance of a dialogue-based tutor includes receiving feedback from a user associated with a natural language interaction of the user with a dialogue-based tutoring (DBT) system. The DBT system includes training data and domain-specific content. One or more feedback categories is presented to the user based upon the feedback. A selection of a feedback category from among the one or more feedback categories is received from the user. A feedback candidate is generated based upon the feedback and the selected feedback category. The feedback candidate is indicative of a potential change in one or more of the training data and the domain-specific content based upon the feedback.

Improving performance of a dialogue-based tutor includes receiving feedback from a user associated with a natural language interaction of the user with a dialogue-based tutoring (DBT) system. The DBT system includes training data and domain-specific content. One or more feedback categories is presented to the user based upon the feedback. A selection of a feedback category from among the one or more feedback categories is received from the user. A feedback candidate is generated based upon the feedback and the selected feedback category. The feedback candidate is indicative of a potential change in one or more of the training data and the domain-specific content based upon the feedback.

The present invention is a language learning method and system. The present invention records a conversation by two native-speakers in a target language. The conversation is transcribed and translated. The transcribed and translated versions are both segmented into snippets that last between thirty (30) seconds and five (5) minutes. A concordance is created from the transcribed segmented conversation. A pareto distribution is created from the concordance. A set of high-frequency, non-trivial words and phrases are defined by the pareto distribution. A language learning application teaches a student the target language by presenting language learning exercises to the student which concentrate on the high-frequency, non-trivial words.