An approach for automatic learning and inference by knowledge map

Online learning repositories are the heart of learning content management systems. This article proposes a model that utilizes the educational content of learning repositories, to create and display multiple learning paths to the students. When a student specifies a topic to study, the model creates the learning paths in form of a tree, with student specified learning concepts as the root node and its co-existing concepts as the child nodes. The model also proposes to automatically extract three co-existing concepts: prerequisites, subsequent topics and features. Use of pattern-based mining and a rule-based classification approach is proposed for the extraction of co-existing concepts. Automatically extracted results are checked for meaningfulness and usefulness against expert generated results. Evaluation of the authors' model on various learning materials shows the appropriate generation of learning paths depicting the co-exiting concepts. The average F1 score obtained for automatic prerequisite extraction is 78%, automatic subsequent topic extraction is 83% and automatic feature extraction is 88%.

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An approach for automatic learning and inference by knowledge map

Cited by 2 publications

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A SCORM/IMS Compliance Online Test and Diagnosis System

A SCORM/IMS Compliance Online Test and Diagnosis System

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