Browsing by Author "Waters, Andrew E."

Now showing 1 - 3 of 3
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    Do open educational resources improve student learning? Implications of the access hypothesis
    (Public Library of Science, 2019) Grimaldi, Phillip J.; Mallick, Debshila Basu; Waters, Andrew E.; Baraniuk, Richard G.
    Open Educational Resources (OER) have been lauded for their ability to reduce student costs and improve equity in higher education. Research examining whether OER provides learning benefits have produced mixed results, with most studies showing null effects. We argue that the common methods used to examine OER efficacy are unlikely to detect positive effects based on predictions of the access hypothesis. The access hypothesis states that OER benefits learning by providing access to critical course materials, and therefore predicts that OER should only benefit students who would not otherwise have access to the materials. Through the use of simulation analysis, we demonstrate that even if there is a learning benefit of OER, standard research methods are unlikely to detect it.
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    Mathematical language processing: automatic grading and feedback for open response mathematical questions
    (2019-08-06) Lan, Shiting; Vats, Divyanshu; Waters, Andrew E.; Baraniuk, Richard G.; Rice University; United States Patent and Trademark Office
    Mechanisms for automatically grading a large number of solutions provided by learners in response to an open response mathematical question. Each solution is mapped to a corresponding feature vector based on the mathematical expressions occurring in the solution. The feature vectors are clustered using a conventional clustering method, or alternatively, using a presently-disclosed Bayesian nonparametric clustering method. A representative solution is selected from each solution cluster. An instructor supplies a grade for each of the representative solutions. Grades for the remaining solutions are automatically generated based on their cluster membership and the instructor supplied grades. The Bayesian method may also automatically identify the location of an error in a given solution. The error location may be supplied to the learner as feedback. The error location may also be used to extract information from correct solutions. The extracted information may be supplied to a learner as a solution hint.
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    Sparse factor analysis for analysis of user content preferences
    (2017-07-11) Baraniuk, Richard G.; Lan, Andrew S.; Studer, Christoph E.; Waters, Andrew E.; Rice University; United States Patent and Trademark Office
    A mechanism for discerning user preferences for categories of provided content. A computer receives response data including a set of preference values that have been assigned to content items by content users. Output data is computed based on the response data using a latent factor model. The output data includes at least: an association matrix that defines K concepts associated with the content items, wherein K is smaller than the number of the content items, wherein, for each of the K concepts, the association matrix defines the concept by specifying strengths of association between the concept and the content items; and a concept-preference matrix including, for each content user and each of the K concepts, an extent to which the content user prefers the concept. The computer may display a visual representation of the association strengths in the association matrix and/or the extents in the concept-preference matrix.