Sparse Factor Analysis for Learning Analytics and Content Analytics

Inactive Publication Date: 2014-09-18
RICE UNIV
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Benefits of technology

[0012]The computer may compute output data based on the input data using a latent factor model, where said computing is performed by the computer system, where the output data includes at least: (a) an association matrix that defines a set of K concepts associated with the set of content items, where K is smaller than the number of the content items, where, for each of the K concepts, the association matrix defines the concep...

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Requiring domain experts to label the questions with tags is an obvious limitation to the approach, ...

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  • Sparse Factor Analysis for Learning Analytics and Content Analytics
  • Sparse Factor Analysis for Learning Analytics and Content Analytics
  • Sparse Factor Analysis for Learning Analytics and Content Analytics

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Terminology

[0057]A memory medium is a non-transitory medium configured for the storage and retrieval of information. Examples of memory media include: various kinds of semiconductor-based memory such as RAM and ROM; various kinds of magnetic media such as magnetic disk, tape, strip and film; various kinds of optical media such as CD-ROM and DVD-ROM; various media based on the storage of electrical charge and / or any of a wide variety of other physical quantities; media fabricated using various lithographic techniques; etc. The term “memory medium” includes within its scope of meaning the possibility that a given memory medium might be a union of two or more memory media that reside at different locations, e.g., in different portions of an integrated circuit or on different integrated circuits in an electronic system or on different computers in a computer network.

[0058]A computer-readable memory medium may be configured so that it stores program instructions and / or data, where th...

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Abstract

A mechanism for facilitating personalized learning. A computer receives graded response data including grades that have been assigned to answers provided by learners in response to a set of questions. Output data is computed based on the graded response data using a latent factor model. The output data includes at least: an association matrix that defines a set of K concepts implicit in the set of questions, wherein K is smaller than the number of questions, wherein, for each of the K concepts, the association matrix defines the concept by specifying strengths of association between the concept and the questions; and a learner knowledge matrix including, for each learner and each of the K concepts, an extent of the learner's knowledge of the concept. The computer may display a visual representation of the association strengths in the association matrix and/or the extents in the learner knowledge matrix.

Description

PRIORITY CLAIM DATA[0001]This application claims the benefit of priority to U.S. Provisional Application No. 61 / 790,727, filed on Mar. 15, 2013, entitled “SPARSE Factor Analysis for Learning Analytics and Content Analytics”, invented by Richard G. Baraniuk, Andrew S. Lan, Christoph E. Studer, and Andrew E. Waters, which is hereby incorporated by reference in its entirety as though fully and completely set forth herein.STATEMENT OF GOVERNMENT RIGHTS[0002]This invention was made with government support under NSF Grant No. IIS-1124535 awarded by the National Science Foundation, Office of Naval Research Grant No. N00014-10-1-0989 awarded by the U.S. Department of Defense, and Air Force Office of Scientific Research Grant No. FA9550-09-1-0432 also awarded by the U.S. Department of Defense. The government has certain rights in the invention.FIELD OF THE INVENTION[0003]The present invention relates to the field of machine learning, and more particularly, to mechanisms for: (a) exposing the...

Claims

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Application Information

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IPC IPC(8): G09B5/02G06N20/00G06F40/00
CPCG09B5/02G09B7/00G06N20/00
Inventor BARANIUK, RICHARD G.LAN, ANDREW S.STUDER, CHRISTOPH E.WATERS, ANDREW E.
Owner RICE UNIV
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