US 12,469,401 B2
Methods and systems for improving resource content mapping for an electronic learning system
Jugoslav Bilic, Kitchener (CA); Stephen John Michaud, Kitchener (CA); Martin David Goodenough Bayly, Kitchener (CA); and Ryan Clayton Ogg, Kitchener (CA)
Filed by D2L Corporation, Kitchener (CA)
Filed on Mar. 20, 2024, as Appl. No. 18/611,054.
Application 18/611,054 is a continuation of application No. 17/854,438, filed on Jun. 30, 2022, granted, now 11,967,251.
Application 17/854,438 is a continuation of application No. 16/922,664, filed on Jul. 7, 2020, granted, now 11,410,563, issued on Aug. 9, 2022.
Application 16/922,664 is a continuation of application No. 14/729,612, filed on Jun. 3, 2015, granted, now 10,748,436, issued on Aug. 18, 2020.
Prior Publication US 2024/0312355 A1, Sep. 19, 2024
This patent is subject to a terminal disclaimer.
Int. Cl. G09B 5/00 (2006.01)
CPC G09B 5/00 (2013.01) 12 Claims
OG exemplary drawing
 
1. A method for improving resource content mapping for an electronic learning system, the method comprising:
receiving an electronic resource comprising a content having one or more resource property fields defining at least one characteristic of the electronic resource;
sectioning the content data into one or more content portions based on an analysis of at least one of the content data and the one or more resource property fields;
assigning a relevance score for the at least one content portion in respect of at least one learning objective of the one or more learning objectives;
determining if the relevance score assigned to the at least one least one content portion satisfies a relevance threshold for that learning objective;
in response to determining the relevance score at least satisfies the relevance threshold, assigning the at least one content portion with that learning objective;
determining, by semantic analysis, a first semantic correlation score between the at least one content portion and a resource-associated learning objective, and a second semantic correlation score between the at least one content portion and a content-associated learning objective;
generating a combined correlation score by combining the first and second semantic correlation scores by one or more weighting factors; and
selecting, based on the combined correlation score, the at least one content portion for inclusion in a user-specific learning path associated to the learning objective.