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Article overview
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Core Course Analysis for Undergraduate Students in Mathematics | Ritvik Kharkar
; Jessica Tran
; Charles Z. Marshak
; | Date: |
2 May 2016 | Abstract: | In this work, we develop statistical tools to understand core courses at the
university level. Traditionally, professors and administrators label courses as
"core" when the courses contain foundational material. Such courses are often
required to complete a major, and, in some cases, allocated additional
educational resources. We identify two key attributes which we expect core
courses to have. Namely, we expect core courses to be highly correlated with
and highly impactful on a student’s overall mathematics GPA. We use two
statistical procedures to measure the strength of these attributes across
courses. The first of these procedures fashions a metric out of standard
correlation measures. The second utilizes sparse regression. We apply these
methods on student data coming from the University of California, Los Angeles
(UCLA) department of mathematics to compare core and non-core coursework. | Source: | arXiv, 1605.0328 | Services: | Forum | Review | PDF | Favorites |
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